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 and published the result, incorrect or unsupported answers roughly 30 percent of the time. It named the three major tools it tested and built a closed loop system in response, grounding every output in the underlying deal documents. It serves asset backed credit and project finance data rooms, 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. It attaches source, formula and supporting quote to every figure and exports live formula spreadsheets a deal team can audit and reload. It then describes the output as one hundred percent error free. That absolute conflicts with an independent buyer's guide for its own category, which holds that no AI tool runs a full quality of earnings because the analysis turns on judgment. The claim postures matter because the architectures are equally serious. Both are grounded designs that make error findable. At Ezra, traceability surfaces a mistake during review rather than after close; at Keye, a reviewer checks the arithmetic 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 back up. Neither names a customer, and neither publishes a certification. For Ezra that 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 short. 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
Select any grade to read the note behind it.
| 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. Its architecture rests on a published year long benchmark showing general purpose models giving incorrect or unsupported answers to private credit questions roughly 30 percent of the time. According to the AI FinTech Index, Ezra publishes the error rate of the approach it rejected and no figure for the system it shipped. It names the models it benchmarked and none of its own ingredients, and publishes no certification while ingesting third party borrowers' data rooms under non disclosure. It also 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. Models read and structure the data while deterministic pipelines do the arithmetic, every figure carries its source, formula and supporting quote, and analyses export as live formula spreadsheets a deal team audits and reloads without the vendor. According to the AI FinTech Index, Keye describes its output as one hundred percent error free, which conflicts with the independent buyer's guide for its category, and backs the claim with no warranty and no accuracy measurement. No customer is named, no certification is held, no supply chain is disclosed, 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 with the independent buyer's guide for its own category, which holds that no AI tool runs a full quality of earnings because the analysis turns on judgment, and the vendor warrants nothing behind the description. The architecture underneath is strong: models read while deterministic pipelines compute, with source, formula and quote on every figure. It 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 does Ezra's network layer leave unstated?
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 organized 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 are design partners by its own careful wording. Neither publishes a 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.
Where does each publish the most, and the least?
Ezra publishes most on its year long benchmark of general models and the closed loop grounding built in response. It publishes nothing on its own models, a certification, pricing or where data sits. Keye publishes most on its audit trail, with source, formula and quote on every figure, and on its data terms. It publishes no accuracy measurement, customer name, certification, price or residency position. 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.
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. The benchmark that justifies the architecture invites exactly the disclosure that never follows. Keye published a rate no system can back up, one hundred percent error free, which conflicts with its category's independent buyer's guide and is warranted by nothing.
Ezra's network layer raises its own questions. Matching issuers to lenders is allocation by another name, and the visibility criteria are unpublished. The platform also observes which institutions examine which deals and how hard, appetite and pipeline information no lender would hand a competitor. Tenant separation and network layer inference boundaries are unstated, and a well resourced sponsor's organized data room will read more accurately than a thin one.
Both supply chains share one asymmetry. Ezra names the models it benchmarked against and says nothing of its own ingredients, Keye names neither, and comparable vendors often disclose theirs. Neither names a customer; Ezra's $6 billion of firms are design partners by its own careful wording. Neither publishes a 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 residency position.