--- title: "Entity resolution FAQ: 74 questions" summary: "The 74 questions a team defending a company master asks before trusting a match, grouped into 8 subjects, each answered with the population behind any figure." canonical_url: https://entitybind.com/faq last_updated: 2026-10-15 copy: full --- # Entity resolution FAQ: 74 questions > The 74 questions a team defending a company master asks before trusting a match, grouped into 8 subjects, each answered with the population behind any figure. Canonical page: https://entitybind.com/faq Last updated: 2026-10-15 ## The questions you ask before you trust a match. All 74, answered once. You own a company master you have to defend: a stewardship queue, a CRM after a merger, a supplier file that pays money out on Friday. Before you let anything decide which rows are the same company, you want to know how it decides, what it does when it cannot, what it costs, and who can see your file. All 74 answers are here, in 8 groups, in the order an evaluation runs — every figure with the population behind it. [Start free →](https://app.entitybind.com/app?signup=1) · [Start with the ones people ask first](https://entitybind.com/faq#start) Self-service. Create your workspace online and verify your email to start matching company records. Free starts with 300 credits and no card. Paid monthly plans are available in workspace Billing; VAT is added where applicable. Usage is tracked without automatic overage charges. [Trust and security](https://entitybind.com/trust) - 74: questions, each answered in full - 8: groups, each also a page of its own - 2026-09-05: when every figure quoted here was last read off the product repository ## The 6 questions people open with Each one opens the full answer further down this page. The line underneath is the short version, and the answer is where the qualifications live. 1. [What happens when you cannot decide?](https://entitybind.com/faq#q-what-happens-when-you-cannot-decide) Nothing is bound. You get the ranked shortlist, the column that would settle it, and a zero on the bill. 2. [So what is the number you would put in front of a sceptic?](https://entitybind.com/faq#q-so-what-is-the-number-you-would-put-in-front-of-a-sceptic) One figure, with the fixture it was measured on and the interval around it. 3. [Will you write to our CRM?](https://entitybind.com/faq#q-will-you-write-to-our-crm) No. A Sprint runs on a copy of your file and writes nothing back to your systems. 4. [How much does a 100,000-row file cost?](https://entitybind.com/faq#q-how-much-does-a-100-000-row-file-cost) Priced per resolved company. An abstention and a replay both cost nothing. 5. [We already have an MDM platform. Where does this fit?](https://entitybind.com/faq#q-we-already-have-an-mdm-platform-where-does-this-fit) Beside it. We test the matches it proposes and contradict them with a citation when they are wrong. 6. [How do we start?](https://entitybind.com/faq#q-how-do-we-start) Create your Free workspace, verify your email and upload a company CSV. Contact us separately for an assisted Sprint. 0 wrong merges, across every shape of file we tested. Zero wrong bindings were observed across the historical six-shape recording and the separate domain fixture. Observed correctness was 1.0000 on accepted outcomes; this does not guarantee an error-free new workload. [The fixtures, the intervals and the method →](https://entitybind.com/accuracy) ## Built for Teams with a company master they have to defend — an MDM or stewardship queue, a CRM after a migration or a merger, a supplier file before a payment run, or counterparty onboarding that has to survive an audit. ## Wrong for Buying breadth as the product: we hold a fraction of an incumbent’s records and are not building a global company graph. Entity Bind resolves companies and organisations only. It does not resolve people, vessels, bank-account ownership, sanctions exposure, emissions or geolocation, and its results may not be used for eligibility decisions about individuals, including employment, credit and tenancy. ## 8 groups, 74 answers Ordered the way an evaluation runs: what the product is, whether the numbers hold, how it is called, what it costs, what it covers, and whether it can be bought. Every group is answered in full below and again on a page of its own. 1. [Entity resolution basics](https://entitybind.com/faq#basics) (9 questions) 2. [Accuracy and evidence](https://entitybind.com/faq#accuracy) (12 questions) 3. [The API](https://entitybind.com/faq#api) (11 questions) 4. [The MCP server](https://entitybind.com/faq#mcp) (5 questions) 5. [Pricing and credits](https://entitybind.com/faq#pricing) (8 questions) 6. [Coverage and sources](https://entitybind.com/faq#coverage) (9 questions) 7. [Security and data protection](https://entitybind.com/faq#security) (9 questions) 8. [Buying and engagement](https://entitybind.com/faq#buying) (11 questions) Every answer below is about the product. The answer about your own rows has to be measured on your file. Create your Free workspace and upload a company CSV, or contact us to scope an assisted measurement. [Start free →](https://app.entitybind.com/app?signup=1) ## Entity resolution basics 01 of 08 · 9 questions What the product is, what entity resolution actually means, what comes back when the evidence will not carry a match, and the two things it deliberately is not: a people-data provider, and a replacement for the firmographic database you already pay for. Argued in full on [How it works](https://entitybind.com/how-it-works), [Compare](https://entitybind.com/compare) and [Use cases](https://entitybind.com/use-cases). ### What is Entity Bind? Entity Bind is an AI-native entity resolution API and MCP server for company identity. One record in, one resolved company record out — with citations, a calibrated confidence score, a receipt for every decision, and an honest abstention instead of a guess when the evidence will not carry a match. It runs beside the systems you already have rather than replacing one: your CRM, supplier master, master data platform or licensed data feed proposes a company match, and Entity Bind accepts it, withholds it or contradicts it independently, with a citation you can open. Scope is company and organisation identity only. It is built in London, and it is early-stage and priced as one — the resolution method and its measurements are published in full, and the global data breadth is not yet at incumbent scale. ### What is entity resolution? Entity resolution is deciding which records refer to the same real-world thing. In company data, that is the work of establishing that "Acme Widgets Ltd", "ACME WIDGETS LIMITED" and a row carrying only company number 09876543 are one company, and that the similarly named firm three counties away is a different one. It is not deduplication by string similarity, and it is not enrichment: enrichment adds fields to a record you have already identified, and resolution is the step that decides whose record it is. Get that step wrong and every field you add afterwards lands on the wrong company, which is why a wrong merge costs more than a missing one. ### How is this different from the fuzzy matching we already do? Two differences, and only one of them is about the score. The first is calibration: a similarity score of 0.87 is a statement about two strings, while a calibrated confidence is a statement about how often decisions like this one have turned out right, computed from recorded trial and error counts with no tunable term in the module. The second is what happens at the bottom of the range. A fuzzy matcher always returns its best candidate and leaves the threshold to you; Entity Bind refuses to bind below 0.90 confidence and returns an abstention carrying a ranked shortlist and the one column that would have decided it. Keep your fuzzy matcher for the obvious duplicates. This is for the rows that cost you money when they are wrong. ### What happens when you cannot decide? You get an abstention, which is a first-class answer rather than a null or an error. The response carries `decision: "unresolved"`, a ranked shortlist of up to five candidates each with its own probability, a typed reason for stopping, a `clarification` naming the one field that would settle it, a `review_id` so the row lands in a queue a person can work, and a `decision_id` that reopens the whole evidence trail at a URL. It costs zero credits at every stage, including the ones that spent the most getting there. What you do not get is a low-confidence guess with a number attached, because the number is what makes a guess look like a decision. ### Do you resolve people? No. Companies and organisations only, and it is a boundary rather than a gap being closed. The research agent is instructed never to collect information about private individuals, findings are checked against an attribute allow-list at the boundary, and anything outside that list is dropped rather than stored. Two named exceptions exist and both are registry data at company level: UK officer and persons-with-significant-control names under the Open Government Licence, and French officer names and roles under Licence Ouverte 2.0. Dates of birth, birth years, nationalities, countries of residence and personal addresses are discarded at ingestion and never reach storage. If you need a named contact at a company, that is a contacts vendor's job rather than ours. ### What is a decision receipt? Every decision the system takes — including the ones where it refused to act — persists a record you can reopen at `GET /v1/decisions/{id}`. It carries the feature vector, the top weighted contributions, the thresholds the call was judged under, the cited evidence with source URLs, the adjudicating model id and prompt version where one ran, the pipeline version, and a re-run of the score from the stored features, so you can recompute the probability rather than take it on trust. Receipts are queryable as a population too, filtered by pipeline version, threshold and date, so "show me every merge taken under the threshold we have since raised" is a question with an answer rather than a project. ### Is Entity Bind a database or an API? An API, an MCP server and CSV uploads in your Entity Bind workspace, all over one pipeline. There is a catalogue behind it — the entities your tenant has resolved, with per-field provenance and freshness — but it is your catalogue, scoped to your tenant, rather than a global company graph we sell access to. That boundary is deliberate: building a proprietary worldwide company database is explicitly not the plan, because breadth is not a battle a new entrant wins against incumbents holding hundreds of millions of records. What is on offer instead is the decision quality, the evidence trail and the refusal to guess, over registers that are public and citable. ### What is the cascade, and why does it matter commercially? Seven stages, cheapest first, each one seeing only what the stage before it could not decide: normalise the record, retrieve candidates, apply deterministic identifier rules, score probabilistically, put the borderline band to an LLM adjudicator, send the ambiguous tail to agentic registry and web research, then abstain. It matters commercially because the price follows the stage. A file carrying company numbers or LEIs binds deterministically, with no model call and no egress; a file of bare names escalates. So the shape of your file rather than its size sets what a run costs — which is why the first thing we ask for is a sample rather than a purchase order. ### What does the crosswalk do? You give it one identifier — an LEI, a company number, a CIK, a ticker, a domain, or a name — and it returns every identifier system the company is known by, each separately cited with its licence tier and freshness, plus one hop of ownership. It also returns the systems that were asked and came back empty, so "a wired source supplies this scheme and had no value for this company" reads differently from "no wired source supplies this scheme at all". A reference data load needs the negative as much as the positive: "GLEIF has no LEI for them" is a finding, and "we never asked" is a different finding. [Open Entity resolution basics as its own page →](https://entitybind.com/faq/basics) ## Accuracy and evidence 02 of 08 · 12 questions Every rate here arrives with its denominator, its interval and the fixture it was measured on, because the alternative is the bare percentage this product's own accuracy document exists to forbid. The weakest tier is answered as fully as the strongest. Argued in full on [Accuracy](https://entitybind.com/accuracy) and [How it works](https://entitybind.com/how-it-works). ### How accurate is it? Is it 99%? On the continuous-integration gold set, 357/357 automated merges were clean, with 63.10% of pairs decided automatically; the rest abstain. We quote the cautious end of that result, the 95% Wilson lower bound: 98.94%, which sits below 99% and is 24 clean labelled merges short of proving a 0.99 bound. The point estimate of precision is 1.0000. End-to-end resolution on that fixture is 70.78% (499/705). A "99% entity resolution" figure quoted without the evidence shortfall, the automation rate and the resolution-level number beside it overstates this system, whoever is quoting it. ### So what is the number you would put in front of a sceptic? Two, side by side. On six input shapes measured over 317 live GB companies, resolution ran from 100% on an identifier down to 58.10% on a name whose ending had been cut off by a field-length limit, blending to 95.75%. Accuracy on decided outcomes was 1.0000 on every one of those tiers, with 0 false binds. The blend is the fragile figure: its value is set by the mix of shapes in your file rather than by the engine, so a file of truncated names lands nearer 58.10% than 95.75%. Read the tier that matches your columns, not the average. ### What is the confidence floor, and is it an accuracy promise? The floor is 0.90, and it is not an accuracy promise. It is an acceptance contract, not calibration evidence: it says what we refuse to return, not how often an accepted match is right. Read it with the coverage it costs. A match below 0.90 confidence is unrepresentable. The record comes back as an abstention with a ranked shortlist instead. The rule sits on the resolution outcome itself, so an accepted match below the floor cannot be constructed anywhere in the system — a domain invariant — and a separate predicate re-checks it on the online, batch and replay paths. Even a cached replay whose inherited confidence has since fallen below the floor is withheld rather than re-served. On the representative fixture 1,666 / 1,666 accepted matches cleared it. ### Can the API return 0.99 confidence? Yes. Some supported evidence populations return values above 0.99, and the recorded v8 uncorroborated population carries 0.9968. The nominal 95% Wilson lower bound of 1,190 historical prompt-v8 uncorroborated MATCH verdicts with zero recorded errors. The 2026-08-29 population pools six input shapes with repeated company families and calibration/evaluation overlap. Its independence and transfer to new customer inputs are unproven. Legacy replay cannot certify current calibration. Confidence bounds depend on the recorded evidence population; there is no universal score for every match. Read match_confidence_basis and the evidence. A 0.99 filter can return some v8 adjudicated matches while excluding valid identifier matches, so validate its coverage cost on your own inputs. Read `decision` alongside `match_confidence_basis`: the confidence only means something once you know which outcome it belongs to. ### What happens to a file that is just company names? It comes back as shortlists rather than matches. A record carrying only a name, or a name plus prose, abstains at the probabilistic stage and returns the top five candidates plus the column that would decide between them — so the tier that cannot be answered stops being the tier that costs the most. Measured on the gated acquisition fixture, the true company ranked first for 151/205 (74%) and appeared in the top three for 205/205 (100%). The stub-adjudicated run behind the pairwise gate, not the 317-company live recording behind the tier table. Recall is a property of the list rather than of the winner, and 74% sits far below the 0.90 acceptance floor, so no row of that shortlist may be offered to you as a match, and none of it is folded into an accepted-match count. Send a domain, a postcode, a registration number or an LEI and the same rows resolve. ### What if the context in our records is wrong? It degrades to an abstention rather than to a wrong answer. In the historical 50-record wrong-town fixture, 72% resolved and zero wrong bindings were observed. This is a measured adversarial sample, not a guarantee for every misleading description. That result is the one worth testing us on, because false context is the normal condition of real CRM data: a trading address typed years ago, an office the company has since left, a note copied from a different account. A resolver that treats context as corroboration will confidently bind the wrong company on exactly those rows. The same property shows up in retrieval — a deliberately wrong town still returns the true company inside the top ten, and produces no false binds. ### Has any of this been validated externally? No. Every fixture is internal and repository-authored. The per-tier table rests on 317 live GB companies sampled from the 2026-08-01 Companies House snapshot, with same-name siblings included. Identifier-rich arrivals seed the catalogue before the weaker input shapes are evaluated. The continuous-integration gate rests on 1,176 labelled company pairs — 216 handwritten adversarial, 960 generated from a fixed seed — plus a 705-record fixture over 250 UK-registered companies. There is no customer holdout, no time or jurisdiction split, no blinded head-to-head against a named competitor, no independent laboratory validation and no third-party penetration test. The internal readiness rubric scores external validation 3 out of 15 and the product 74 out of 100 overall, which is why the site says early-stage rather than proven. What is available in its place is the method, the fixtures and the failure cases in full, and an evaluation that begins by running your file rather than by reading ours. ### What happens if the model changes? Adjudicated confidence is keyed by model id and prompt version, and both are written onto every verdict inside the decision receipt. Change either and the system fails closed: adjudicated bindings stop being publishable at the old bound until labelled calibration exists for the new pair, so a prompt tweak degrades the product to abstention rather than quietly recalibrating it upward. The alternative arrangement — a model swap nobody outside notices, while the accuracy page keeps its old number — is the one this design exists to rule out. It also means a receipt written last quarter tells you exactly which model saw what under which prompt, which is the question an auditor asks about an automated decision. ### How is the confidence number actually produced? It is not the model's self-report. Each stage publishes the cautious end of its own measured accuracy over recorded trials — the 95% Wilson lower bound — so a stage claims at or below what it has been shown to deliver, and an audit of every stage confirms that none overstates. The pooled headline is 0.9968: The nominal 95% Wilson lower bound of 1,190 historical prompt-v8 uncorroborated MATCH verdicts with zero recorded errors. The 2026-08-29 population pools six input shapes with repeated company families and calibration/evaluation overlap. Its independence and transfer to new customer inputs are unproven. Legacy replay cannot certify current calibration. The adjudicating model's own confidence is carried separately and is uncalibrated — a self-report with a large calibration error against outcomes, never thresholdable, and never comparable with the calibrated field. Where the model was never asked about a candidate, that field is null, and null means "not asked" rather than "asked and unconvinced". ### Is the non-match side as reliable as the match side? No. The historical prompt-v8 rejection population has 54 verdicts and four errors. Its bound authorises no accepted match; other non-match populations carry their own values. The bound is 0.8245, it is reported but not gated, and a wrong non-match costs you a duplicate entity rather than a wrong merge — the cheaper failure, but still a failure. There is a second asymmetry worth reading in full, because it is the one that catches people: a new entity minted because nothing matched is not a match and is not subject to the floor. Its reject-side confidence can fall as low as 0.6752, on 27 observations. That row rests on 27 observations and is attached to the riskiest branch of the API, so never read "everything this endpoint returns is above the floor" into the floor. ### Do you publish cluster metrics as well as pairwise ones? Yes, and never one without the other. Pairwise numbers describe individual comparisons and can look excellent while the clusters they build are wrong, so the house rule is that a pairwise figure is never published without the cluster figure beside it. On the representative fixture, how well the groups the system built match the true groups, member by member — B-cubed cluster precision — measured 1.0000. Cluster F1 0.9646 on the same 317-company representative recording of 2026-08-29. Pairwise numbers are never published without these. The same rule runs across the board: precision never appears without the automation rate, because 100% precision at 0% automation is trivial, and no rate appears without its interval and sample size. A figure on this site without its denominator is a bug in the page. ### Does the answer depend on how we chunk the file? Within one batch, no: resolution is order-independent by construction. Every record is proposed against the store as it stood when the batch began, the links made inside the batch are then folded so that everything transitively connected lands in one group, and the same sixty records in four different orders produce an identical partition — against an earlier implementation that produced four. Across batches, yes. The same twenty-four rows produced fifteen entities and seven review items as one batch, and eight entities as twenty-four batches of one. Smaller batches collapse more duplicates; one large batch buys order-independence within it. Both directions are real, so chunking is a decision to take deliberately rather than a detail to leave to a loader. [Open Accuracy and evidence as its own page →](https://entitybind.com/faq/accuracy) ## The API 03 of 08 · 11 questions Getting a key, what one call returns, the difference between resolving and matching, how a file goes through in bulk, how to cap what a record is allowed to cost, and what the error model does when something goes wrong. Argued in full on [REST API](https://entitybind.com/api) and [How it works](https://entitybind.com/how-it-works). ### How do I get an API key? You create it yourself, in the Entity Bind workspace you create online. Create your workspace online and verify your email; it starts on the Free plan with no card. Create your workspace online at https://app.entitybind.com/app?signup=1 and verify your email. Once you are signed in, you create, list and revoke your own keys, each scoped to your tenant with read or resolve permission, and each raw key is shown once. Paid plans are available through Stripe in workspace Billing; registry checks follow your active plan. The evaluation opens at a separately agreed activation date. The planned 1,000-credit evaluation runs at full research depth and is separate from the shallow Free plan. Keys are stored as SHA-256 digests. A read key reads what your tenant already holds and cannot resolve; a resolve key runs the data plane; review and admin credentials carry separate scopes. In your workspace, rotating a key is create, switch, revoke; the admin API's rotate endpoint adds a grace window, and an exposed key can be revoked. ### What does one resolve call return? A golden record, assembled field by field from whichever source has the best claim to each field — survivorship — with per-field provenance, a calibrated `match_confidence` and the basis behind it, a ranked candidate list, a `decision_id` receipt, the pipeline version, a `usage` block, and a set of flags that say what actually happened: `replayed`, `depth_capped`, `entity_created`, `escalation_unavailable`, `no_identifying_signal`, `more_input_needed`. Every field of the golden record carries its source id, source URL, retrieval time, licence, first-seen and last-checked timestamps, a confirmation count and an age in days, and the entity reports the age of its stalest field rather than its freshest — because one recently confirmed value should not be able to make a stale record look current. ### What is the difference between /v1/resolve and /v1/match? `/v1/resolve` takes one record and binds it to an entity in your catalogue, or abstains. It persists: it mints a record id, may create or join an entity, and writes a decision receipt you can reopen. `/v1/match` is a stateless pairwise comparison of two records you supply — is this the same company as that one — and it persists nothing at all, so there is no record id, no entity id and no receipt to fetch afterwards. Both charge on the same rule. Use match when you are adjudicating somebody else's proposed pair and do not want a catalogue entry; use resolve when you want a golden record and an audit trail. ### Can I push a whole file through it? Yes, over `/v1/batch`, which takes up to 500 records per request by default, is `Idempotency-Key` aware, writes one ledger row per outcome rather than one summed row, and is order-independent within the batch. Your Entity Bind workspace also takes a CSV upload, up to 500 company rows by default, and a finished run downloads as CSV or JSON with its decision receipts. For a larger file in a Sprint, the import we run streams it through the same public API — so a successful import is evidence your own integration will work rather than a bespoke pipeline — and carries your key column through as `client_ref`, so the output joins back on your ids rather than ours. It is not a queue: batches go one at a time, and a 300,000-row file is an overnight run. Plan the window rather than the throughput. ### How do I cap what a record costs? With the `depth` parameter, which is a price and latency ceiling and can be set per record even inside a batch. `research` is the default and runs the whole cascade. `adjudication` stops before agentic research. `probabilistic` is a hard no-egress setting enforced in code at both egress points: no model provider and no registry, so a capped record reaches neither. There is a second, orthogonal control, `enrich=false`, which turns the registries off at any depth for a caller whose contract forbids the lookup. Capping never lowers precision — the alternative to a deep answer is an abstention rather than a cheap guess — so what you trade away is automation rather than correctness. ### Can our plan cap a run below the depth we asked for? Yes, and it is the trap worth knowing about before you read a resolution rate. The effective depth is the lower of what you requested and what your plan permits, so a `research` request on the Free plan executes at the no-egress stage instead — and because a new Entity Bind workspace starts on that plan, and because the clamp applies even when usage quota enforcement is off, a whole run can execute two stages shallower than the one it was quoted at. What stops that being invisible: `/v1/batch` echoes a `depth_ceiling`, the import we run for a Sprint keeps the shallowest value any batch reported and prints both on its run report, and resuming a run at a different depth is refused rather than blended. Read the ceiling before you read the rate. ### What are the rate limits? Per endpoint class, and bucketed per tenant rather than per key, so a customer with three keys has bought one allowance. The published defaults are 1,000 reads a minute, 300 resolves, 300 matches, 30 batches and 120 review calls, scaled by a plan multiplier. `RateLimit-Limit`, `RateLimit-Remaining` and `RateLimit-Reset` come back on success as well as on a 429, and a `Retry-After` is added when a request is refused. Two caveats worth reading before you size anything: the limits are deployment settings you should read from `GET /v1/reference/limits` rather than hard-code, and without a shared limiter store they are enforced once per replica. ### What does the error model look like? One envelope on every failure — `{"error": {"code", "message", "correlation_id"}}` — with a stable machine code from a published vocabulary you can fetch at `GET /v1/reference/error-codes`. Internal detail never reaches a caller: any 5xx message is replaced with canned text, so a driver error cannot leak SQL or bound parameters. Two choices are worth knowing. A wrong-tenant id returns 404 rather than 403, because a 403 confirms the id exists and turns every read into an enumeration oracle. And `deadline_exceeded` is a 504 rather than a 503, because nothing upstream is down — our budget for that request ran out, and the fix is a lower depth or a longer allowance. ### Is the API idempotent? In two different ways. `/v1/batch` takes an `Idempotency-Key` header: identical concurrent callers wait for and replay the sealed winner, the same key with different content is a permanent conflict, and row ids are derived deterministically so a replay mints the same ids. `/v1/resolve` needs no header, because its idempotency is a content fingerprint — the SHA-256 of the submitted record excluding the server-minted id — computed before the cascade runs. A byte-identical resubmission returns the same binding, is flagged `replayed`, and costs nothing. The binding has no expiry. It is per tenant and per exact content: one corrected postcode is a different fingerprint and a fresh resolution. ### Can we correct a merge you got wrong? Yes, within a deliberately narrow boundary. `POST /v1/entities/{id}/detach` moves exactly one attributed record, and every fact carrying that record's origin id, onto a fresh entity, in one transaction that also updates both of the indexes deciding which candidates get compared, the replay binding, your own crosswalk links and an immutable receipt. It requires a review-scoped credential, so an ingestion key cannot do it, and it fails closed with a 409 before any write if any of six preconditions does not hold. What it is not is a general unmerge: no arbitrary cluster split, no bridge analysis, no rollback to a historical cluster shape, no automatic reattach. Registry and research facts stay on the source rather than being guessed onto one side. ### How does the abstention queue actually get smaller? By settling items, and settling does two things rather than one. First, a verdict is applied rather than merely recorded: it merges through the same guard the cascade uses, and a non-match excludes the presented candidate and re-runs retrieval, binding an alternative only where that alternative independently clears the floor and the candidate-margin guard. The item reports whether it was applied, which entity it landed on, and a blocked reason where it was not, so "recorded" and "acted on" are distinguishable at a glance. Second, a settled verdict stops the same bytes re-queueing — and only the same bytes. A corrected postcode is a different record and can queue again for a company somebody settled last week. A queue that keeps refilling with the same names is a source system emitting drifting rows, and the fix for that is upstream normalisation rather than more reviewers. [Open The API as its own page →](https://entitybind.com/faq/api) ## The MCP server 04 of 08 · 5 questions The same pipeline reached over the Model Context Protocol, and the questions that only matter once a model is holding the credential: what it can spend, what it can delete, what it can mint, and what it gets back when the answer is genuinely ambiguous. Argued in full on [MCP server](https://entitybind.com/mcp) and [AI agents](https://entitybind.com/use-cases/ai-agents). ### What is the MCP server? The same pipeline exposed to agents as eleven tools over the Model Context Protocol, on local stdio or a remote URL. Two of the eleven are billable — resolving a company and comparing two records — and nine are free: read an entity, read a crosswalk, discover enrichment fields, search, redeem a decision receipt, read usage, and the three review-queue tools. It delegates to the same application services as the HTTP API, so a resolve over MCP is the same resolve, at the same price, against the same rate-limit bucket. Limits are shared rather than doubled. Deliberately absent: batch, catalogue export, erasure and provisioning, all of which stay on the HTTP surface. ### Do I need OAuth to connect an agent? No, and static bearer is the path to reach for first. The `rk_` API key you create in your Entity Bind workspace authenticates the remote MCP transport unchanged as an `Authorization: Bearer` header — the option credential vaults call `static_bearer`. OAuth exists, with PKCE S256 required, one issuable scope and operator-registered clients, but there is an integration gap to know about before you plan around it: the listener publishes no RFC 9728 protected-resource metadata and answers an unauthenticated tool call with a tool error rather than a 401 challenge. A client that starts its OAuth flow from a 401 will never start it. Discovery-driven clients work only behind a shared origin. ### Can an agent spend money without us noticing? Every billable tool returns a `usage` block — credits charged, credits remaining, deep attempts remaining, plan, and whether billing is enforced — and that block is the only place an agent can learn what a call cost, because a tool call has no response headers to read. So an agent can audit its own spend rather than be told it is cheap. The credential also caps depth before the cascade runs, so a key pinned to `probabilistic` cannot reach agentic research by choosing this surface. And the abstention rule holds here too: Free-tier or not, an `unresolved` costs zero credits, so an agent asking a question nobody can answer is not a bill. ### Can an agent delete data or create credentials? No, structurally rather than by policy. There is no erasure tool and no provisioning tool on the MCP surface, and an admin-scoped key is refused outright before a caller object is even constructed — so the credential that can mint credentials is unreachable by a model. Tenancy is derived per call from the credential and is never a request parameter; every repository method requires a tenant id as a keyword argument with no default; and a cross-tenant read reports 404 rather than 403. An erasure obligation is discharged by a person who can be asked why, which is a support conversation rather than a step in an agent's plan. ### What does an agent get back when the answer is ambiguous? An `unresolved`, with everything it needs to decide what to do next: the ranked shortlist with calibrated per-candidate probabilities, a review id, a decision id, and four flags that carry different instructions. `depth_capped` means you stopped this and a deeper call may resolve it. `escalation_unavailable` means a dependency failed and a retry is worth trying. `more_input_needed` means the paid stages could not separate the shortlist and retrying deeper will not change it — send a domain, a registration number or an LEI instead. `no_identifying_signal` means the fields normalised to nothing, and it is the only one the agent can fix by itself. [Open The MCP server as its own page →](https://entitybind.com/faq/mcp) ## Pricing and credits 05 of 08 · 8 questions What a real file costs, why an abstention is free, why a replay is free, what the free plan genuinely does and does not do, and the fact a pricing page usually leaves out: usage has no automatic overage charges, because usage metering runs in shadow mode. Argued in full on [Pricing](https://entitybind.com/pricing) and [The audit pack](https://entitybind.com/audit-pack). ### How much does a 100,000-row file cost? It depends on the columns rather than the row count, and you can work it out yourself. A row decided by identifier rules or probabilistic scoring is 1 credit; a row that needed adjudication or research is 5; an abstention, a byte-identical replay and a row that normalised to no identifying signal are all 0. So 100,000 rows all carrying company numbers is 100,000 credits, and the same file where every row went deep is 500,000. At the Scale rate of $0.032 a credit, the cheapest paid rate we publish, that is roughly $3,196 and $15,980. That is more credits than any priced plan includes and there is no overage rate, so a file this size is a Scale conversation, quoted against your volume. Before any quote, we run a free preview over your file. It makes no model or registry calls, tiers your rows and reports composition, never a predicted resolution rate. Then we talk it through. Start free, then choose a paid plan in Billing. ### Are you charging cards today? Yes, for paid monthly subscriptions purchased through Stripe in workspace Billing. Review the recurring price, applicable VAT and payment terms before purchase; Stripe provides the subscription invoices. Free needs no card. Usage metering is separate: calls are recorded in the ledger, and usage creates no automatic usage or overage charge. The usage block states whether quota enforcement is enabled for the deployment; an `enforced: false` response means the call is metered without enforcing the usage quota, not that a paid subscription is free. The plan prices are defined in code and served from the same constants that meter, so the number in a response and the number on the page cannot drift apart. ### Why is an abstention free? An unresolved identity costs zero credits even when adjudication or research did the work. Deep attempts are tracked separately, because those calls still carry a provider cost. Existing monthly plans can reduce depth when their attempt quota runs out. The planned 1,000-credit evaluation instead requires an explicit limit response or a disclosed pause when research cannot continue, so a budget limit cannot be mistaken for an evidence-based abstention. That change is not live yet. ### Do we pay twice for the same record? No. Resolution is bound to a content fingerprint — the SHA-256 of the submitted record with the server-minted id excluded — so resubmitting the same bytes returns the same binding at zero credits, on every plan, with no expiry. It is not a cache with a 24-hour window, which would be a promise about our infrastructure that you cannot verify; it is a property of your data. Two caveats travel with that claim. It is per tenant, so there is no cross-customer discount for a company somebody else has already resolved. And it is per exact content: one corrected postcode, one added phone number or one trailing space is a different fingerprint and a fresh resolution. ### Can I resell the data? On the open-licensed subset, on the Scale plan, and with attribution. It is enforced by the write path rather than by a clause: every fact carries a licence tier, and the repository raises before any write on a pass-through fact, so a violation cannot even partially persist, and the field-by-field assembly of the golden record drops pass-through material before the record is built. Resale rights are published per field — `licence_permits_resale` is a property of the data's own licence, `resellable` is that plus the right your plan bought — and an unreadable licence reads as not-resellable rather than as permitted. Companies House is OGL v3.0 and SIRENE is Licence Ouverte 2.0, and both require attribution. Talk to us before you build a redistribution product on it. ### What does the free plan actually do? The existing Free plan includes 300 credits a month, no deep resolutions, and a probabilistic depth cap. It makes no model or registry calls, so it is useful for wiring an integration but does not test research coverage. Every Entity Bind workspace starts on it, and a CSV upload in Matching mode runs there. The planned 1,000-credit evaluation is a separate one-time offer with full research depth. Request access to test that pipeline on your own companies; a sample file is optional. The full-pipeline evaluation is arranged separately and is not activated by signup. Request it for your workspace; we confirm availability, sources and the start date before activation. ### Why 5 credits for a deep resolution rather than 2 or 10? Because it is a price rather than a measured cost ratio, and the code comment says so. The measured input is narrower: one LLM adjudication was measured at £0.0240 over 243 live calls, and the cost model carries $0.0293 per adjudication verdict; at about 1.73 verdicts per escalated record, that is about $0.051 for a record that escalates. Per thousand rows it is about $0 for an identifier-rich file and about $50 for one that is all names, because escalation is the whole cost: on the gated acquisition fixture 0 of 250 identifier-bearing records and 1 of 250 digital-tier records escalated, against 205 of 205 name-only ones. The 5:1 multiple is charged identically whether the answer came from one adjudication or a full research pass, and research costs several times more. Shadow-mode metering exists to check that ratio against reality before anybody is billed on it. ### How does the pricing shape compare with an incumbent's? Badly today, better over time, and the reason is structural. An incumbent's marginal cost per lookup is a database read, a hundredth of a cent, because the database is already built. Ours is per-query inference on the records that escalate. What differs is the direction of travel: a company already held in the catalogue with its identifiers binds deterministically for free, so our marginal cost falls as the catalogue fills, while theirs is already at the floor. That is a reason to send a file rather than a reason to believe a projection, and it is why we cap depth per record and publish the credit rule instead of quoting a blended per-record price. [Open Pricing and credits as its own page →](https://entitybind.com/faq/pricing) ## Coverage and sources 06 of 08 · 9 questions Which registers are wired and answering, which are built and silent, and which countries are simply absent. The gaps are named here rather than left for an integration to discover, because a coverage answer without its gaps is a sales answer. Argued in full on [Coverage and sources](https://entitybind.com/coverage) and [Reference data](https://entitybind.com/use-cases/reference-data). ### Which countries do you actually cover? Five registers carry measurements: the United Kingdom (Companies House, the baseline), the United States via Colorado and via SEC filers, France (SIRENE) and Norway (Brønnøysund). Combined three-region parity against the UK baseline measured 0.999955 against a pre-declared 0.9565 bar, at zero model spend, with no false binds in any register. Adapters for Finland, Czechia and Poland are wired but carry no evaluation fixtures, so they have no measured quality and we do not sell them as covered. Everything else in the source inventory is a planned build with a cost in engineer-days rather than a present capability. The claim is per register, never per continent. ### Do you cover Germany? No, and it is a hole rather than limited coverage. Germany has no lawful bulk source: the commercial register offers no API, no bulk product and no stable per-record URL, and its terms exclude reuse. Every commercial German workaround we found is a scraper inheriting those terms, which fails the bar this product sets for itself — a fact we cannot cite to something a third party can open is worse than no fact, because it reads as an answer. The EU High-Value Datasets regulation has required member states since June 2024 to publish basic company data openly, and that may change the position. Until enforcement does, the disposition is not to build, and the answer to a German file is no. ### Do you have US coverage? Scoped, and the scoping matters. There is no federal company register. SEC filers are wired through EDGAR, and at state level only Alaska, Colorado, Connecticut and Ohio publish bulk data free with no account; Colorado is the one with a measured fixture, at 2,191,009 extant legal persons and 97.76% name uniqueness. Delaware, the most important formation state in the country, sells no bulk product and no API at any price. There is also no quality claim for SEC filers yet: the wide digital tier measured lower there because closed-end fund families share near-identical names at one address. That is a finding about the register's shape rather than about the engine, which is why it is reported beside the combined figure instead of blended into it. ### Why does France need an extra column? Because a third of French companies share their exact name with another live company. The name-uniqueness census puts France at 67.96% against the United Kingdom's 99.53%: 1,003 live companies sit on one name. That is a property of the register rather than of the engine — no resolver can resolve a genuinely ambiguous bare name, and the answer there is the shortlist. Add a town or a postcode and it resolves: on the postcode tier, a French fixture measured 2,998 of 3,000. So a French file wants a postcode or commune column mapped, and if you do not have one, that is the finding rather than the failure. ### Where does the data come from? Public registers and cited web research, not a purchased corpus. Live and exercised today: UK Companies House including persons with significant control, SEC EDGAR submissions and XBRL, GLEIF's LEI records, Norway's Brønnøysund and France's SIRENE. The bar every source has to clear is verifiability at record level: a stable resolvable URL or documented API endpoint for the specific record, not a homepage, not a session-bound search form and not a bulk file the reader has to download and grep. Sources that fail it are recorded with reasons rather than quietly dropped. Where two sources disagree, the register of incorporation wins — and a live probe found a paid vendor placing HSBC in New Hampshire where Companies House had it in Canary Wharf. ### How often is the data refreshed? There is no cadence. Nothing revalidates on a schedule: a fact's `last_checked_at` advances only when a resolution happens to re-observe the same value, and a background revalidation job does not exist. What you get instead is the age, stated. Every fact carries when it was first seen, when it was last confirmed and how many times, the API exposes an age in days per field, and the entity reports the age of its stalest field rather than its freshest. A stale fact is served with its staleness visible, never silently refreshed and never silently withheld. If you need scheduled re-checking, that is a roadmap conversation rather than a feature. ### Do you have ownership or UBO data? Some, cited, and bounded. Ownership rides along from GLEIF Level 2 relationship records and the UK persons-with-significant-control register, with every edge carrying its basis and its certainty — because GLEIF's "parent" means accounting consolidation while a credit bureau's means majority legal ownership, and rendering both as one parent field is wrong in a way nobody can see. Traversal defaults to one hop and may be raised to ten, is cycle-safe, fails closed on ambiguous or name-only owners, and always populates what it did not walk, so you cannot infer depth from an empty field. It is bounded by LEI adoption, so an incomplete path is an absence of coverage rather than an absence of owners. ### Do you have company financials? For UK companies, headcount and balance sheet yes, revenue essentially no — and the reason is law rather than implementation. Only about 2% of the live UK register has to file a profit and loss account at all, and under 1% of filings in a sampled day carried a machine-readable turnover figure. Over 18,894 real filings, average employees reached 90.3% coverage and net assets 85.9%. Turnover for large issuers lives in ESEF instead, tagged on roughly 83% of regulated-market issuers. For a German GmbH, a French SARL or a US LLC there is no revenue, assets or profit available from any open source at any price. Quote headcount and balance sheet; treat revenue as a bonus. ### Do you screen for sanctions? No. Sanctions, adverse media and risk scoring are out of scope, and there is no risk boolean anywhere in the schema by design. A screening result is an assertion about a matcher rather than a fact about a company: false-positive rates of 90–99% are normal in production know-your-business screening, and writing one into a record as "sanctioned" would manufacture a defamatory fact with a citation attached. What a screening tool needs from us is the identity underneath it — which company this row actually is, with an evidence trail — so that its list matching runs against a resolved counterparty rather than a spelling. That is the layer we sell, beside a screening vendor rather than instead of one. [Open Coverage and sources as its own page →](https://entitybind.com/faq/coverage) ## Security and data protection 07 of 08 · 9 questions Isolation, retention, erasure, sub-processors and what reaches a model provider, each answered with the mechanism that enforces it. The first answer in the group is the one most vendors bury: there is no SOC 2 and no ISO 27001. Argued in full on [Security and trust](https://entitybind.com/trust) and [Privacy](https://entitybind.com/privacy). ### Do you have SOC 2? No. Not SOC 2 Type I, not Type II, not ISO 27001. It has not been started, no auditor is engaged and there is no target date. There is also no third-party penetration test and no bug bounty; the security review is internal and adversarial, but it is our own. If your procurement floor requires any of those, you cannot buy this today, and it is worth establishing that on the first call. What does exist is a trust document naming the file, test or architectural decision that enforces every claim it makes, so each control can be checked rather than taken on trust. The gap between that and SOC 2 is the independent attestation that the controls operated over a period. ### How is our data isolated from another customer's? By application-enforced tenant scoping, and the exact shape of that matters. Every table carries a tenant id; every persistence method requires it as a keyword argument with no default, so a forgotten argument raises rather than silently reading a shared tenant; reads scope in SQL before retrieval rather than filtering after it; and the replay cache is keyed on a composite primary key, so byte-identical content from two customers resolves to two separate entities. A wrong-tenant read is a 404, never a 403. What it is not is database-enforced: one database, one schema, one runtime role, no row-level security, so a SQL-injection defect would cross every tenant at once. The internal workbench sits outside that guarantee. ### What happens when we ask you to delete something? Erasure runs per entity or per record, in one transaction across eight targets: the entity's facts, the keys that index it for matching, every submitted record it holds, open review items embedding one of those records, settled review items redacted rather than deleted, the replay fingerprints, cached responses quoting it, and the entity row. There is deliberately no bulk delete and no "delete my tenant" button, because a single call that empties a tenant turns one mistyped id into an unrecoverable loss. One limit to read before you rely on it: the decision audit trail survives, and a surviving decision row can carry cited evidence and an adjudication rationale naming the company. It expires on the retention window rather than on request. ### How long do you keep data? Four separate retention windows, each its own command — decisions, records and usage default to 2,555 days, and open review items to 365. Seven years is chosen to clear the money laundering regulations' five-year duty to retain the evidence behind a customer due-diligence decision; below about five years, a know-your-business customer loses what they are legally required to produce. The windows are environment variables you can change: set record retention to 90 days and you have 90-day retention. One caveat outranks the rest, and our own trust document states it first: nothing purges itself. Every window is a scheduled job an operator has to run, and an unscheduled deployment retains everything for ever. ### Does our data go to an LLM provider? Yes, on the records that reach adjudication or research, and it is worth being exact about what travels. The adjudication prompt renders the candidate record's populated fields — including the submitted address — plus the free-text context and the entries of the `extra` map, so a CRM column mapped into `extra` reaches the model verbatim. The research agent uses the provider's own server-side web tools, so search queries and fetched URLs reach them too; if "which companies is this customer looking at" is sensitive, that signal reaches them. Two things do not exist and should not be assumed: a zero-data-retention addendum, and any code-level enforcement of the provider's not-used-for-training term, which is contractual. Setting `depth=probabilistic` sends nothing at all. ### Who are your sub-processors? One model provider and a set of public registers, published as a table and kept honest by a build-failing test that reads the request URL off every wired adapter and fails continuous integration if a host is missing from the list. The registers are Companies House and its document service, GLEIF, SEC EDGAR, INSEE SIRENE via the French state search service, Brønnøysund, plus wired adapters for Finland, Czechia, Poland, EUIPO and USPTO. There is no reseller, and no analytics or session-recording tag anywhere in the product. One disclosure a reviewer needs before mapping a country column: the French service is on by default and needs no credential, so a record stating no country will have its name sent there unless you disable it. ### Where does it run, and can we run it ourselves? The artefact is one container plus a database, configured entirely by environment variables, and it runs wherever it is run — including a deployment we run for a customer. There is no managed multi-region offering and no region to pick on a form, so data residency here is a conversation about where that deployment sits rather than a checkbox somebody ticks. One thing not to infer from that: the sub-processor calls do not move with it. A deployment sitting in the EU still reaches a US-hosted model provider on the records that escalate, US federal hosts for SEC filings, UK and global hosts for Companies House and GLEIF, and a French state service for SIRENE. The only setting that sends nothing anywhere is the probabilistic depth cap. ### Can you sign a DPA? Not one that is already drafted and waiting, so there is no request-our-DPA button to press. A documented data processing agreement, standard contractual clauses and a zero-data-retention addendum with the model provider are all prerequisites for a regulated EU buyer, and all three are outstanding. The published position covers what the code does — the erasure and portability endpoints, the retention reasoning, the licence handling, tenant scoping and the sub-processor list — and stops there rather than asserting a lawful basis, a controller or processor determination, or a transfer mechanism it has not documented. Send the questions to our security address and you will get a straight answer, usually the same week. ### Can you prove you deleted something? Yes, for an engagement-scoped erasure. The maintenance command signs its receipt with a public-key signature, Ed25519, as well as an HMAC, so your own auditor can verify it against a published public key rather than take our word for it, and outside development the command refuses to run without a signing key. What is not on offer is automatic expiry: nothing deletes itself on a timer, so the claim is "we erase on request and hand you proof anyone can check" rather than "your data expires automatically". One limit: a redacted settled review item is still a row recording that an abstention happened and how it was settled. If your erasure duty extends to no trace at all, we do not meet it. [Open Security and data protection as its own page →](https://entitybind.com/faq/security) ## Buying and engagement 08 of 08 · 11 questions How an evaluation actually runs, what the Merge Safety Sprint is, where this sits beside an MDM platform, and the two answers that lose deals honestly: we will not write to your CRM, and we will not promise a count of dangerous merges before we have seen your file. Argued in full on [The audit pack](https://entitybind.com/audit-pack), [Pricing](https://entitybind.com/pricing) and [Contact](https://entitybind.com/contact). ### What is included in the 1,000-credit evaluation? 1,000 free credits. The full resolution pipeline. 1,000 one-time credits, valid for 30 days from your first resolution. No card required. Full resolution depth, with configured sources and their existing licence terms. It uses the same resolution pipeline and evidence rules as paid access. At the current credit prices, that means up to 1,000 shallow decisions or 200 deep decisions; the actual mix depends on your inputs. Abstentions, exact replays and evidence reads cost no credits. The planned allowance includes up to 1,000 deep attempts, including those that abstain. Credits and attempts are separate limits. Provider-spend protection may pause work explicitly; unused credits stay visible and the pipeline is never silently downgraded. The full-pipeline evaluation is arranged separately and is not activated by signup. Request it for your workspace; we confirm availability, sources and the start date before activation. ### Can my agent try it without a sales call? Your agent can read the public REST reference, the MCP setup and /api/quickstart.txt right now. Live calls need a key from your Entity Bind workspace and the base URL that comes with it. Today the workspace opens after email verification; once it is open, you create the key yourself and your agent uses it over the API or MCP. The full-pipeline evaluation is arranged separately and is not activated by signup. Request it for your workspace; we confirm availability, sources and the start date before activation. Signup is self-service. The separate full-pipeline evaluation remains request-only and requires confirmed availability and provider limits before activation. ### How do we start? Create your Free workspace online, verify your email and upload your own company CSV. Free includes 300 credits and shallow matching with no card; create your own API keys for REST and MCP, and choose a paid monthly subscription in Billing when you need greater depth or capacity. For an assisted Merge Safety Sprint, contact us with the file you are defending: a CRM export, a supplier master, a customer-success list or a post-merger overlap, with your own source ids preserved and, if you have them, the entity id, match and confidence your incumbent proposed. A person reads that request, and a short conversation scopes the optional assisted Sprint. What comes back is which of your rows are the same company, which of your existing matches we would not have made, and which we could not decide — where a further field would settle an unresolved row, it names that field. We write nothing into your systems, and you do not replace your current vendor to run it. The accuracy method is published in full at /accuracy. ### What is a Merge Safety Sprint? Send us a file. We tell you which of your rows are the same company, which of your existing matches are wrong, and what we could not decide — and we write nothing to your systems. The deliverable is the audit pack: twelve files, a receipt per decision, and a report that opens offline. It contains accepted matches, new entities, unresolved rows, withheld pairs, blocked merges, golden records, a reversal map, decision receipts, an attribution notice and a self-contained report that also prints to PDF. Every unresolved row carries a typed cause from a closed vocabulary of eight, and a column of yours to add or repair wherever a column is nameable — which is most rows and not all of them; where nothing we could name would help, the row says so rather than inventing a field. Two of the eight causes are ours rather than yours. The totals reconcile row by row across ten machine-checked identities, and if they do not partition, the run exits non-zero and no pack is delivered. It proposes actions; it never applies them. What it is not is a shadow run: the import behind it created entities in our catalogue, and the no-write property is scoped to your systems. ### How many dangerous merges will you find? We do not promise a count of dangerous merges prevented, and you should distrust anyone who does before they have seen your file. Zero is a normal, honest result: two rows carrying contradicting company numbers are refused outright and never reach the guard that would have counted them. The ledger only fills where the cascade proposed a merge and the guard refused it, so the well-identified files we qualify for are precisely the ones most likely to return nothing. What makes a zero readable is what sits behind it: the pack lists the pairs that were compared and not called, counts the pairs the comparison budget deferred, and states that the list was not truncated. A zero with that behind it is a finding. A zero without it is why we would not ship the file at all — a header-only file reading as "we found no dangerous merges" is the most misleading output this product could emit. ### Will you write to our CRM? No, and there is no writeback connector to switch on later either — automatic CRM or ERP writeback is explicitly not on the roadmap. What comes back is a set of files keyed on your own record ids, so the join happens on your side, under your change control. That is a commercial position as much as a technical one: the moment a supplier writes to your master, an argument about a bad merge becomes an argument about whose fault it is, and the receipt stops being the point. We propose actions and you apply them, or you do not. A report is never the system of record. ### Can you prove you are better than the vendor we already pay? Not in the abstract, and not from a benchmark we ran ourselves. What we can do is adjudicate their proposals on your data: send your record with the match your incumbent proposed, and we accept it, withhold it or contradict it independently, per provider, per jurisdiction and per evidence tier. That produces a number about your file rather than about a fixture, and it is checkable, because every contradiction cites a register page you can open. One thing we do not currently ship is a packaged incumbent-contradiction ledger inside the audit pack — that file is absent by declaration rather than empty — so the comparison is a working session today rather than a download. ### Another vendor quotes hundreds of millions of companies and a 96% match rate. How do you compare? Not on breadth. An incumbent-scale graph is not a battle a new entrant wins, and building a global proprietary company database is explicitly not the plan. Compare the numbers instead, starting with what a match rate has to carry before it means anything: the denominator, the fixture it was measured on, the automation rate beside it, and the shape the records arrived in. Every figure on this site carries all four, including the two that do us no favours. On the live-adjudicated fixture of 317 GB companies recorded 2026-08-29, the truncated-name shape resolves 58.10% (61/105) — our weakest input shape, published at the same weight as our strongest. Separately, on the continuous-integration gold set of 2026-09-05, 357/357 automated merges were clean at 63.10% automation, with a 95% Wilson lower bound of 98.94% — below the 99% anybody would rather quote. Those are two different instruments over two different fixtures, and they are never averaged. A vendor's figures are a vendor's figures, ours included; the way to settle it is to send the same file to both of us. ### We already have an MDM platform. Where does this fit? Beside it, as the layer that decides and evidences rather than the one that stores. A master data platform is very good at governance workflow, stewardship, hierarchy management and pushing a golden record to the systems that consume it, and none of that is on offer here. What it usually does not have is an independent admission policy: the matching rules are yours, tuned by your stewards, and there is nothing outside the platform to contradict them with a citation. Entity Bind is that outside opinion, with a receipt per decision and a floor it will not cross, feeding the stewardship queue rather than replacing it. If your stewards are drowning, the abstentions are the work list. ### What does an evaluation actually look like? Three steps and no obligation. First, we run a free preview over your file, which calls nothing and spends nothing: it tiers every row by the strongest retrieval signal that survives normalisation, tells you which of your populated columns are unusable and why, and prices a ceiling. Second, a sample run — usually a few thousand rows — that produces the audit pack and a set of rows you can check by hand against the register pages we cite. Third, a decision. If the preview shows your file already carries clean identifiers and nothing here would move, that is a valid outcome and the right place to stop. ### Who is behind it, and how long has it existed? Entity Bind is built in London by its founder, James Rooney, and it is early. It is a product name rather than a company: the company you contract with, and its registration, are named in the terms at /terms. The engineering discipline is real and published — the cascade, the calibration, the licence guard at the persistence layer, the receipts, the erasure proofs — and the commercial track record is not: no named production reference, no multi-customer holdout, no independent validation. The internal readiness rubric scores the product 74 out of 100 against its own 95 target, and scores external validation 3 out of 15. If your evaluation requires calling a customer already running this in production inside a regulated firm, we cannot give you one, and that is the first thing to weigh. [Open Buying and engagement as its own page →](https://entitybind.com/faq/buying) ## The answers that lose us deals Two things end an evaluation on the spot: a procurement floor that requires an attestation, and a file of names cut short by a field limit. Better read here than discovered in week three. ### What we do not hold - No SOC 2. It has not been started. - No independent external validation of the accuracy figures. Every fixture is one we built. - No production customer holdout. The evidence is internal. Answered in full below: [Do you have SOC 2?](https://entitybind.com/faq#q-do-you-have-soc-2) and [Has any of this been validated externally?](https://entitybind.com/faq#q-has-any-of-this-been-validated-externally) ### The weakest input shape 58.10% (61/105 · truncated name) A name whose ending has been cut off by a field-length limit. 44 of 105 come back as an abstention with a shortlist, which is the correct answer for a name whose missing tokens were the discriminating ones. The lowest of the 7 input shapes in the per-tier recording. [What a names-only file gets back](https://entitybind.com/faq#q-what-happens-to-a-file-that-is-just-company-names), and [the other 6 rates, with their intervals →](https://entitybind.com/accuracy) Historical results on an internal UK fixture with a preloaded catalogue. The September 5 replay has no full-prompt-verified calls and cannot establish current calibration. No independent customer holdout or global coverage claim. Retention, erasure, tenant isolation and the rest of the residual risk a security review asks about are published in full on [the trust page](https://entitybind.com/trust). ## Your question, not the one we anticipated Grounded on the published position only, and instructed to carry the denominator with any figure it quotes. Where the honest answer is that something does not exist, it says so. On [the page](https://entitybind.com/faq) an ask panel stands here: it answers a typed question from the site's published pages and is not reproduced in this copy. ## The one question this page cannot answer Everything above is a question about Entity Bind. The questions worth a conversation are the ones about your data: which of your rows are the same company, which of your existing matches are wrong, and what the shape of your file does to the resolution rate. Send us a file. We tell you which of your rows are the same company, which of your existing matches are wrong, and what we could not decide — and we write nothing to your systems. We do not promise a count of dangerous merges prevented, and you should distrust anyone who does before they have seen your file. Zero is a normal, honest result: two rows carrying contradicting company numbers are refused outright and never reach the guard that would have counted them. Or write to hello@entitybind.com with the question this page did not answer. It reaches one person rather than a queue, and the answer usually ends up on this page. [Talk to us →](https://entitybind.com/contact) ## Evidence basis Internal fixtures only. The historical 2026-08-29 per-tier recording covers 317 GB companies with identifier-rich arrivals seeding the catalogue first; the context tier includes country and registered-town prose. Its legacy replay has no full-prompt-verified calls and cannot certify current calibration. The September 5 CI reports cover 1,176 labelled pairs and 705 records over 250 companies using offline stubs. These are not independent customer holdouts or live production validation; the 99% minimum-input product target remains unmet. Scope is company and organisation identity only — not people, vessels, bank-account ownership, sanctions or emissions. Results may not be used for eligibility decisions about individuals, including employment, credit and tenancy. ## When an answer needs to be longer than a paragraph Three families behind these answers: the definition of the word a question turns on, the article that argues it in full, and the register inventory itself. - [The word the question turned on](https://entitybind.com/glossary): The field's vocabulary one term at a time: the definition first, then what Entity Bind does about it — including the techniques it does not implement. - [The long version of an answer](https://entitybind.com/guides): Long-form method: blocking and candidate generation, Fellegi–Sunter, calibration, survivorship, and how to measure any of it on your own file. - [Where the coverage answers come from](https://entitybind.com/coverage): The registers a request can reach today, the licence instrument each publishes under, what may be stored rather than only returned, and the coverage this product does not have. ## Start with your own company records. The answers above are ours. Create your Free workspace and inspect your own CSV to see which rows match and which need review. Contact us for an assisted audit of existing matches or the separate full-pipeline evaluation. [Start free](https://app.entitybind.com/app?signup=1) · [Request free evaluation](https://entitybind.com/contact#evaluation) 300 credits. No card. The full-pipeline evaluation is arranged separately and is not activated by signup. Request it for your workspace; we confirm availability, sources and the start date before activation. For a guided review of your file, explore [the Merge Safety Sprint](https://entitybind.com/audit-pack). --- This is the Markdown copy of https://entitybind.com/faq, assembled from the same data the page renders and carrying every sentence, table row, figure and question the page's main content carries. The HTML page is canonical; it adds the layout, the navigation and the interactive parts (the ask panel, the forms), which a copy can name but not run. Every page on the site, with a line on each: https://entitybind.com/llms.txt.