Centauri AI
Centauri AI · Coding · Business
Centauri AI is an AI data platform for investment teams. It turns credit agreements, servicer reports and spreadsheets into structured DataCubes, then drafts analysis with citations you can check.
About Centauri AI
What Is Centauri AI
Centauri AI is a San Francisco company founded in 2023 by He Lu and Milan Shen. Its product serves institutional investors, mainly teams that work with structured finance assets such as mortgage backed securities, whole loans and asset backed securities. Analysts upload the files they already receive and let an AI agent pull out the tables, map the fields and build a dataset they can query later.
The product sits in the document intelligence category. The workflow follows a familiar ETL pattern: extract the tables, transform the fields into your target schema, analyze the result and present it as a dashboard or a formatted Excel sheet. What changes is who does the mapping. The agent reads the documents, explains each field in plain language and writes the code that moves data from one format to another.
The daily problem is easy to describe. A loan tape arrives as a PDF, a servicer report arrives as an Excel file and a credit agreement arrives as a long contract. Someone has to read all three, match the fields and rebuild the same spreadsheet every month. Centauri AI compresses that work into minutes, and the output stays queryable, so the next report starts from a saved DataCube instead of from scratch.
The limits matter before you book a call. Pricing is quote based and there's no self serve tier, so a small team can't test it with a credit card. The product is built around finance documents and alternative investments, which means a marketing or support team gets little from it. Public API documentation isn't published, so integration work goes through the vendor.
Getting Started
- Book a demo with the Centauri AI team and bring two or three real documents, not sample files.
- Upload the set to the workspace. The agent indexes structured tables and unstructured text in one pass.
- Name the output fields you need, then read the mapping the agent proposes before you accept it.
- Build the first DataCube, run an analysis, and export the result as a dashboard or a formatted Excel sheet.
Product Information
A quick look at Centauri AI's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Structured finance analysts
- Private credit and private equity deal teams
- Portfolio managers and trading desks
Tasks
- Cleaning and validating a loan file
- Checking covenant compliance
- Drafting a deal memo
Scenarios
- Monthly servicer reporting
- Diligence on a new pool
- Portfolio monitoring across deals
Key features
Document indexing for PDFs, Excel and CSV files
You drop files into an upload hub and the agent separates structured tables from unstructured text. Loan files, credit agreements and servicer reports land in the same workspace, so one search covers the whole deal. The index keeps a page reference, which means an answer can point back to the source.
DataCube as a reusable dataset
A DataCube is the structured layer that comes out of the mapping step. It holds cleaned fields that you can query again, reuse in a new analysis or export. Teams that run the same report every month stop rebuilding it, and that's where most of the time saving sits.
Plain language data mapping
The AI agent explains what a field means, then writes the code that maps it to your target format. You read the explanation and correct it before anything is committed. This matters in structured finance, where two files often use different names for the same column.
Branching analysis and reviewable writeups
An analysis run produces a report you can rewind or branch at any point. If a total looks off, you go back to that step and try another path without losing the rest. The writeup itself is built for review, with source citations attached to the claims.
Citations and audit trails
Every output keeps a link to the document and clause it came from. That's the difference between an AI answer you can send to an investment committee and one you can't. Audit trails also help when a colleague or a regulator asks how a number was produced.
Deployment and security controls
Centauri AI runs as SaaS, as a single tenant instance or inside your own VPC. The company says its controls are SOC 2 Type II assessed, and workflows stay reviewable so a person approves the output. For finance teams, the deployment choice often decides whether a tool can be used at all.
Pros and cons
Pros
- Purpose built for structured finance documents, so field mapping fits loan tapes and servicer reports.
- DataCube output is reusable, which cuts the monthly rebuild that eats analyst time.
- Citations and audit trails make the analysis shareable with investment committees.
- Flexible deployment covers SaaS, single tenant and private cloud.
Cons
- No public pricing and no self serve signup, so evaluation takes a sales call.
- The product targets finance workflows, which limits its use for other departments.
- No published API documentation, so custom integration depends on the vendor.
Frequently asked questions
It turns finance documents such as credit agreements, servicer reports and loan files into structured data, then runs analysis on that data and writes a report with citations.
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