Ezra vs Keye (2026)
Both vendors built their architecture as an argument against general purpose models, and the pair splits on claim hygiene: Ezra measured the competition, and Keye graded itself. Ezra spent a year benchmarking general models on private credit tasks, published the result, incorrect or unsupported answers roughly 30 percent of the time, named the three major tools it tested, and built a closed loop system grounding every output in the underlying deal documents in response, serving asset backed credit and project finance data rooms and drafting the memos, research and diligence question sets a credit team works from. Keye split its architecture so models read and structure while deterministic pipelines compute, attached source, formula and supporting quote to every figure, exports live formula spreadsheets a deal team can audit and reload, and then described the output as one hundred percent error free, an absolute that conflicts on the record with an independent buyer's guide for its own category, which holds that no artificial intelligence tool runs a full quality of earnings because the analysis turns on judgement. The claim postures matter because the architectures are equally serious. Both are grounded designs that make error findable, Ezra earning its liability standing through traceability that surfaces a mistake during review rather than after close, Keye earning the equivalent through arithmetic a reviewer checks in their own spreadsheet. But Ezra publishes the error rate of the approach it rejected and not the rate of the system it shipped, and Keye publishes a rate no system can honestly claim. Neither names a customer, and neither holds a published certification, which for Ezra is not merely its customer's risk decision, since the data rooms it ingests belong to third party borrowers under non disclosure.
- Asset backed credit and project finance are your paper. Data rooms across renewables, infrastructure, fintech and real estate become structured datasets, extracted terms, surfaced risks and drafted memos, research and question sets for credit teams.
- The design argument is published and falsifiable. A year of benchmarking found general models wrong or unsupported roughly 30 percent of the time on private credit tasks, with the tested tools named, and the closed loop grounding built in response cannot assert what the data room does not contain.
- Deal flow is part of the product. A network connecting companies raising capital with institutional lenders is being built alongside the analysis platform.
- Quality of earnings shape work in the deal team's own medium. Cohort and margin analysis, anomaly detection and structured cuts exported as live formula spreadsheets that edit and reload.
- The arithmetic cannot hallucinate. Deterministic pipelines compute while models read, and every figure carries its source, formula and supporting quote for review without vendor cooperation.
- The data terms are the shortest in the niche. No retention, no training on customer files.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Ezra and Keye are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI FinTech Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Ezra | Keye | |
|---|---|---|
| Primary category | Capital Markets & Research AI | Capital Markets & Research AI |
| Founded | 2021 | 2021 |
| Headquarters | San Francisco, California, United States | New York, New York, United States |
| Website | www.ezra.finance | www.keye.co |
Side by Side
| Axis | E Ezra |
K Keye |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model Risk Management and Transparency | ||
| Operational and Outcome Evidence | ||
| AI Safety and Data Stewardship | ||
| GLBA and Data Privacy Posture | ||
| Security Certifications and Trust Center | ||
| Regulatory Status and Licensure | ||
| AI Governance and Bias Disclosure | ||
| AI Liability and Recourse | ||
| Model Supply Chain Disclosure | ||
| Core Systems and Integration Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Institution and Segment Coverage |
The short version of each
Ezra
Ezra serves asset backed credit and project finance, grounding every output in the underlying deal documents and drafting the memos, research and diligence question sets a credit team works from, with its architecture justified by a published year long benchmark showing general purpose models answering private credit questions incorrectly or unsupported roughly 30 percent of the time. The AI FinTech Index records the asymmetry that follows: Ezra publishes the error rate of the approach it rejected and no figure for the system it shipped, names the models it benchmarked and none of its own ingredients, holds no published certification while ingesting third party borrowers' data rooms under non disclosure, and describes a lender matching network whose visibility criteria and tenant boundaries are unstated.
Source: AI FinTech Index, 2026
Keye
Keye runs financial diligence on private equity targets with models reading and structuring while deterministic pipelines do the arithmetic, source, formula and supporting quote attached to every figure, and analyses exporting as live formula spreadsheets a deal team audits and reloads without the vendor. The AI FinTech Index records its claim posture as the caution: output described as one hundred percent error free conflicts on the record with the independent buyer's guide for its category, is warranted by nothing and accompanied by no accuracy measurement, while no customer is named, no certification is held, no supply chain is disclosed in a lane where comparable vendors disclose theirs, and no price or residency position is published.
Source: AI FinTech Index, 2026
Common questions
Is Ezra better than Keye for deal diligence?
Both built their architecture as an argument against general purpose models, and they serve adjacent jobs. Ezra works asset backed credit and project finance data rooms, drafting the memos, research and diligence question sets a credit team works from. Keye runs financial diligence on private equity targets with deterministic arithmetic and live formula exports. The real separation is claim hygiene: Ezra measured the competition and published the result, while Keye graded itself and published an absolute. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
What did Ezra's benchmark actually show?
Ezra spent a year benchmarking general models on private credit tasks, published that they produced incorrect or unsupported answers roughly 30 percent of the time, and named the three major tools it tested. That is the right way to justify an architecture. The catch is what never follows: Ezra publishes the error rate of the approach it rejected and no figure for the system it shipped, so the benchmark invites exactly the disclosure that is missing. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Does Keye's one hundred percent error free claim hold?
No. It conflicts on the record with the independent buyer's guide for its own category, which holds that no artificial intelligence tool runs a full quality of earnings because the analysis turns on judgement, and the vendor warrants nothing behind the description. The architecture underneath, models reading while deterministic pipelines compute, with source, formula and quote on every figure, is genuinely strong and does not need the absolute sitting on top of it. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
What should a lender ask about Ezra's network layer?
Ezra's network layer matches issuers to lenders, which is allocation by another name, with the visibility criteria unpublished. The platform also observes which institutions examine which deals and how hard, appetite and pipeline information no lender would hand a competitor, with tenant separation and inference boundaries unstated. A well resourced sponsor's organised data room will also read more accurately than a thin one, and nothing addresses that tilt. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
What evidence and assurance exists at either?
Neither names a customer, Ezra's six billion dollars of firms being design partners by its own careful wording, and neither holds a published certification, which for Ezra is not merely its customer's risk decision since the ingested data rooms belong to third party borrowers under non disclosure. Neither publishes a price or a residency position, and the supply chains share one asymmetry: Ezra names the models it benchmarked against and none of its own, Keye names neither. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
How does the AI FinTech Index grade Ezra and Keye?
Both are graded on the same fifteen capability axes from public sources, each grade traceable to its artifact. The AI FinTech Index records the pair as equally serious grounded architectures with opposite claim postures, one publishing the error rate of what it rejected, the other publishing a rate no system can honestly claim, and neither publishing the number its own posture makes conspicuous. The index publishes no composite score and declares no winner.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Capital Markets & Research AI page.
The missing number at each vendor is the one its own posture makes conspicuous. Ezra published the error rate of the approach it rejected, general models at roughly 30 percent, and no figure for the system it shipped, so the benchmark that justifies the architecture invites exactly the disclosure that never follows.
Keye published a rate no system can honestly claim, one hundred percent error free, conflicting on the record with its category's independent buyer's guide and warranted by nothing. Ezra's network layer needs separate scrutiny: matching issuers to lenders is allocation by another name, with the visibility criteria unpublished, and the platform observes which institutions examine which deals and how hard, appetite and pipeline information no lender would hand a competitor, with tenant separation and network layer inference boundaries unstated, while a well resourced sponsor's organised data room will read more accurately than a thin one.
Both supply chains share one asymmetry, Ezra naming the models it benchmarked against while saying nothing of its own ingredients, Keye naming neither, in a lane where the comparable vendors disclose theirs. Neither names a customer, Ezra's six billion dollars of firms being design partners by its own careful wording, neither holds a published certification, which for Ezra is not merely its customer's risk decision since the ingested data rooms belong to third party borrowers under non disclosure, and neither publishes a price or residency position.