Centauri AI vs Chronograph (2026)
Depth against coverage at the widest spread in this lane's pairings. Centauri AI reads one class of document more closely than anything else here, interpreting provisions across the sections of a credit agreement to determine what a term means, with every extracted value linked to its source passage and a published benchmark of over 92 percent accuracy on named key terms, produced by a team of five with two documented deployments. Chronograph monitors the whole book, more than five point nine trillion dollars of invested client capital across roughly fifteen thousand funds and two hundred and fifty eight thousand portfolio companies, with a managing director at one of the largest listed alternative managers quoted by name on more than seven million data points captured. The integration surfaces run the same direction as scale: Chronograph lands a client's data in its own cloud warehouse and publishes a model context protocol connector so an institution reaches its portfolio from the assistant it already uses, while Centauri delivers queries, files and dashboards. The measurement runs the other way entirely. The five person company publishes its accuracy and cites every term; the platform monitoring trillions publishes no extraction rate, no error breakdown and no retrieval recall for the semantic search whose confident syntheses are only as complete as what retrieval surfaced. Scale has the evidence, the specialist has the numbers, and neither names what models do the reading.
- Your work is inside the agreement. Provisions are interpreted across sections, seniority determined rather than located, and every term links to its source passage so verification happens at the point of use.
- Your risk function wants numbers before names. Over 92 percent accuracy on key terms is published with the engagement described, and a period of operation security attestation is reported despite the company's size.
- Your stack is spreadsheets and email today. Output lands as database queries, structured files and live dashboards, with natural language querying over past deals.
- Your book spans thousands of holdings. More than five point nine trillion dollars of invested client capital is monitored across roughly fifteen thousand funds and two hundred and fifty eight thousand portfolio companies, with named executives quoted on quantified results.
- Your allocator and manager operations both need serving. Separate limited partner and general partner products cover collection, reconciliation, valuation and reporting, with private credit workflows including compliance certificate extraction and add backs.
- Your data should reach your own tools. A warehouse product lands monitoring data in your own cloud environment, and a published model context protocol connector reaches your data from the assistant you already use.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Centauri AI and Chronograph 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
| Centauri AI | Chronograph | |
|---|---|---|
| Primary category | Capital Markets & Research AI | Capital Markets & Research AI |
| Founded | 2023 | 2016 |
| Headquarters | San Francisco, California, United States | New York, United States |
| Website | centauri-ai.tech | www.chronograph.pe |
Side by Side
| Axis | C Centauri AI |
C Chronograph |
|---|---|---|
| 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
Centauri AI
Centauri AI reads one class of document more closely than anything else in its lane, interpreting provisions across the sections of a credit agreement to determine what a term means, with every extracted value linked to its source passage and a published benchmark of over 92 percent accuracy on named key terms, alongside a reported period of operation security attestation. The AI FinTech Index records the measurement culture as the inversion of the scale: a team of five publishes the numbers its trillion monitoring counterpart does not. The index records the readings beside the credit: 92 percent plainly means roughly one term in twelve needs correction, a rate the material does not discuss, the evidence base is two documented deployments with neither customer named, and at five people the underlying language model is almost certainly an external service clients cannot identify.
Source: AI FinTech Index, 2026
Chronograph
Chronograph monitors the whole private markets book, more than five point nine trillion dollars of invested client capital across roughly fifteen thousand funds and two hundred and fifty eight thousand portfolio companies, with a managing director at one of the largest listed alternative managers quoted by name, a data warehouse route landing client data in the client's own cloud, and a published model context protocol connector reaching the portfolio from the assistant an institution already uses. The AI FinTech Index records the integration surface as genuinely unusual and records the measurement running the other way: no extraction accuracy, no error rate by document type, and no retrieval recall for a semantic search whose sharpest risk its own shape creates, since a confident synthesis built from whatever retrieval surfaced looks complete when a document was missed, with no coverage signal saying what the search did not see. Its attestation evidence rests on a third party compilation worth confirming directly.
Source: AI FinTech Index, 2026
Common questions
Where do Centauri AI and Chronograph overlap?
They meet in private credit documents and diverge everywhere else. Centauri interprets individual agreements deeply for investment teams, while Chronograph monitors entire private markets books for allocators and managers, so the choice is depth on one document class against coverage of the whole portfolio. 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.
Which vendor publishes accuracy?
Centauri, with over 92 percent on named key terms and citation links on every value. Chronograph publishes reconciliation and a trusted dataset as claims with no accuracy, error or retrieval recall figure, which the AI FinTech Index grades C on the model risk axis.
What is the specific risk in Chronograph's semantic search?
A confident answer assembled from incomplete retrieval. Nothing tells the user what the search did not surface, so an absent document produces a complete looking answer, and no recall or coverage measure is published. 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.
Whose model supply chain is more visible?
Chronograph's, which names the external model provider reachable through its published connector, while leaving its internal extraction stack unnamed. Centauri identifies no provider, hosting arrangement or subprocessor beyond stating domestic servers. 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 Wealth & Advisory AI page.
The pairing repeats the lane's measurement inversion at a wider spread. Chronograph publishes named client quotes, precise scale and a genuinely unusual integration surface, and no extraction accuracy rate, no error rate by document type and no retrieval recall for its semantic search, whose sharpest risk its own product shape creates: a natural language query returns a confident synthesis built from whatever retrieval surfaced, and an omitted document produces an answer that looks complete and is not, with no coverage signal telling the user what the search did not see.
Centauri publishes the accuracy figure and per term citations, on an evidence base of two unnamed deployments and a team of five, and its 92 percent means roughly one term in twelve needs correction. Chronograph's security posture rests on a third party compilation reporting both service organisation control report types, worth confirming directly, and its supply chain discloses the external model provider reachable through its connector while leaving the stack behind its own extraction unnamed. Centauri names no provider at all, and at its size the model is almost certainly an external service its clients cannot identify.