Entity Bind vs Tamr
Machine-learning mastering at enterprise scale. It learns your labels — including the wrong ones.
Keep Tamr for the matching you already do inside your own estate. Entity Bind takes the rows your columns cannot settle: we go to the register for the deciding fact, refuse below a published floor, and write a receipt somebody can read months later.
Your company master has to survive somebody else’s audit, and every merge in it needs a reason a person outside your team can read. This page compares on that axis: what each side is for, where the alternative is stronger, and what we would do in your place.
Tamr: Machine-learning mastering at enterprise scale, across suppliers, customers and parts, improving as your experts label. Entity Bind: No labelling programme. The register is the label, every decision carries a receipt, and we will contradict your own history with a citation.
5 of the 12 rows in the table below go to Tamr and 7 go to Entity Bind. The rows on their side are the reason to keep what you already run; the rows on ours are what we would add beside it.
The short answer
Tamr masters data at enterprise scale using machine learning with people in the loop: your experts label examples, the models learn your domain, and what comes back is a curated master across suppliers, customers or parts rather than a pile of scored pairs. For a large organisation with millions of supplier rows, subject-matter experts who can label, and a mandate to produce one authoritative dataset, that is a real capability and nothing here replaces it — run the two together. The trade is the one every supervised system makes: the model learns your labels, including the assumptions inside them, and the confidence it emits is a statement about that model on that training. Entity Bind starts from the register instead. It brings outside evidence to a single decision, cites it, refuses below a published floor and hands back a receipt — with no labelling programme, no training round and no expert time, and with a scope narrowed to companies and organisations.
The answer for your own rows needs your own rows: a few hundred of them, with the matches you already have beside them.
Start freeOn this page
What Entity Bind adds beside Tamr
Run both, and this is the half we do. Each of these is a job the other side of this page is not built for.
- You have no labelled sample and no expert time, and the register can act as the label instead.
- The evidence that would decide your hard rows is outside your data entirely.
- You need a decision to carry an evidence trail that reopens at a URL a year later.
- You want an outside policy that will contradict the merges your own history already contains.
- You want a measured answer on a real file this quarter rather than after a training round.
A stewardship queue and a supplier file before a payment run want different halves of that list. Create your Free workspace to inspect your own rows, or contact us for a guided review of the checks your workflow needs.
Start freeThe case for Tamr
Read these before the table. On some evaluations one of them settles the question on its own, and if it settles yours, keep what you have and put us beside it.
- Human-in-the-loop learning that genuinely improves with feedback. Expert labels compound into a model that fits your domain rather than a general one.
- Multidomain mastering: suppliers, customers, products and parts. Parts and product catalogues are a domain no register touches and one we cannot help with at all.
- Scale. Hundreds of millions of records in enterprise deployments, with the operational experience that implies. Our fixtures are three orders of magnitude smaller.
- The deliverable is a mastered dataset with curated golden records, not a per-record answer somebody still has to assemble into one.
- It fits idiosyncrasies a general product will not — your industry's naming conventions, your historical merges, your own error costs.
- Enterprise standing: references, assurance documentation, and a procurement path that already exists inside large organisations.
Requirements that point to Tamr
- The deliverable is a mastered dataset across a large estate, not a decision on a record.
- You are mastering products, parts or materials, where none of our data or accuracy language applies.
- You have subject-matter experts with time to label, and a programme that can wait for the model to learn.
- Volume is at the scale where a per-decision price does not work.
- Procurement needs an established enterprise vendor with production references.
If one of those is your requirement, say so when you write to us. We will tell you where the answer is Tamr, and what running Entity Bind beside it would add: an independent verdict on each match, with the register page that supports it. Cheaper for both of us than finding out in month three.
Entity Bind and Tamr, dimension by dimension
Every row carries a verdict. 5 go to Tamr, 7 to Entity Bind and 0 are even. Both columns are filled in on every row, so a verdict can be read against what each side actually does about it.
| Dimension | Tamr | Entity Bind | Edge |
|---|---|---|---|
| Domains covered | Suppliers, customers, products, parts and materials. | Companies and organisations only. A product catalogue is a different ontology and nothing on this site transfers to it. | Tamr |
| Scale demonstrated | Hundreds of millions of records, in enterprise production. | Fixtures of 317 companies and the continuous-integration gate's own fixture. We have not been run at their scale. | Tamr |
| What is delivered | A mastered, curated dataset with golden records. | A decision per record, with evidence, a confidence and a receipt. Assembling those into a master is your job or your MDM's. | Tamr |
| Fit to your domain | Models trained on your experts' labels, so the system learns your conventions and your error costs. | An outside policy, fitted on our fixtures, with out-of-fold generalisation published. Where your domain is genuinely unusual, a model trained on it will beat this. | Tamr |
| External validation | Enterprise deployments, references and assurance material. | Internal fixtures only. No customer holdout, no independent validation, no SOC 2, and a self-assessed readiness of 74 out of 100. | Tamr |
| Cold start | Labels first. The model needs expert time before it is good. | No labelling round. The register is the label, and the first step is a free preview we run over your file, which calls nothing and spends nothing. | Entity Bind |
| Evidence outside your records | Enrichment and curation over the sources you connect. | The register of incorporation read directly, cited per field with source URL, licence, first-seen date, last-confirmed date and confirmation count. | Entity Bind |
| Inheriting your own mistakes | A supervised master learns the merges your experts already make, including the ones that are wrong, and it learns them confidently. | An independent admission policy that will contradict your history and show you the register page that says so. It has no view of what you did before and that is the point. | Entity Bind |
| What the confidence means | A model score under that model's training. | How often decisions like this one turned out right, read at the cautious end of the range: the 95% Wilson lower bound over the population the decision belongs to, recomputed from recorded counts so an edited constant fails the build, and keyed by model and prompt version so a model change fails closed. 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. | Entity Bind |
| Behaviour on genuine ambiguity | An escalation to a curator, on thresholds you own. | A match below 0.90 confidence is unrepresentable. The record comes back as an abstention with a ranked shortlist instead. Free at every stage, with a ranked shortlist, a typed cause and the column that would settle it. | Entity Bind |
| Auditability of one decision | The mastered dataset and its curation history. | A receipt per decision, including refusals, queryable as a population by pipeline version, threshold and date, and re-scorable from the stored features. | Entity Bind |
| What you may store and resell | Your deployment, your sources, your contracts. | A licence tier on every fact, a write path that refuses to persist what may not be warehoused, and resale rights on the open-licensed subset on the Scale plan. | Entity Bind |
Three limits sit behind every comparison in this section — what we hold, where we stand and how ready we are — and the comparison hub states all three at full size. The figures in our own column were measured on internal fixtures; the accuracy page carries the method and the population behind each one.
- 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.
A table cannot weigh those rows against your file, and neither can a fixture of ours — your own records are the only ones that settle it.
Start freeBuilt for, and wrong for
Neither of these depends on which alternative you are weighing. They are the two answers that decide whether the comparison above is worth your time at all.
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.
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.
The method behind our half of this comparison
3 articles that set out how the decision is made and measured, rather than how it compares.
- GuideSurvivorship and golden recordsMerging records is only half the job. Field-level survivorship, source precedence, temporal facts, per-field provenance, and why last-confirmed beats first-seen.
- GuideCalibration and confidence scoresWhat a calibrated confidence actually means, how expected calibration error is computed, why a similarity score is not a probability, and why an LLM's self-reported confidence is neither.
- GuideHow to measure entity resolution accuracyPrecision, recall, abstention, pairwise versus B-cubed cluster metrics, Wilson intervals and out-of-fold evaluation. Why a single accuracy number for entity resolution is always meaningless, with worked arithmetic.
Prefer to put it to a person? Talk to us instead →
The other options you are weighing
The other 11 comparisons, on the shelves the hub uses. Four are not vendor contests: two are the options that beat most vendors most of the time — the fuzzy match you already run, and building it yourself — and two are categories we refuse rather than compete in.
Data providers and registers
Entity Bind vs OpenCorporates
2/9 rows to themAn excellent register mirror. A register mirror is not a decision.
Entity Bind vs Companies House
3/12 rows to themFree, authoritative, and the right answer for a lot of UK teams. It is one of our sources too.
Entity Bind vs PermID
4/11 rows to themFree permanent identifiers, and largely unrivalled in capital markets. We do not issue them.
Resolution engines and platforms
Entity Bind vs Senzing
5/12 rows to themA mature engine you run yourself, at no cost per decision. It resolves what you give it.
Entity Bind vs Splink
5/12 rows to themFree, open source, and it weighs the evidence properly: Fellegi–Sunter, done right. Our scoring stage is the same family.
Entity Bind vs AWS Entity Resolution
5/12 rows to themCheap, inside your own account, and it needs no new vendor. It also matches only what you loaded.
Entity Bind vs MDM platforms
5/12 rows to themThey are the system of record. We are a decision layer beside one, and we write nothing.
What you would otherwise do
Not what we are
Questions about Entity Bind and Tamr
Is this a mastering platform?
No. There is no curation workspace, no product or parts model, no golden-record publication and no expert labelling loop that compounds over time. What comes back is a decision per record with the evidence behind it: which company this row refers to, at what confidence, on what citation, or a documented refusal. If the requirement is a mastered dataset across a large estate, that is the platform's job and not ours.
We would rather train a model on our own data. Is that not better?
For fit, frequently yes. A model trained on your labels learns your naming conventions and your error costs in a way a general system will not. The two weaknesses are worth naming anyway: it learns your existing mistakes with the same confidence as your correct calls, and it can only weigh evidence that is already in your records. Neither is fixed by more labels, and both are what outside evidence and an independent floor are for.
Could the two run together?
Yes, and the useful split is by row rather than by system. Let the mastering platform do what it is good at across the estate, and route the company records it is unsure about — or a sample of the ones it merged confidently — through here for an outside adjudication. What comes back is per-row: accept, withhold or contradict, each with a citation. Some teams also use the reviewed outcomes as an evaluation set for their own model, which is a legitimate use of this.
How would we know whether the outside opinion is worth anything?
Measure it on your file rather than on our fixture. Send a sample of records together with the matches your master already holds, and the output is a per-provider, per-jurisdiction, per-evidence-tier breakdown of where we agree, where we would have withheld and where we contradict — with each contradiction citing a register page you can open in about thirty seconds. That produces a number about your data. Our own figures are internal fixtures and are not a substitute for that number.
Settle it against Tamr on your own rows.
Create your workspace, verify your email and run your first company file. The Free plan needs no card. Create API keys in your workspace when you need an integration.
300 credits. No card.
For a guided review of your file, explore the Merge Safety Sprint →