AI infrastructure for fintech

Fintech products live on financial data, statements, filings, transactions, holdings, that has to be processed at scale, understood in context, and kept strictly separate per user or per fund. If you're building AI-powered financial products, a portfolio agent, a fraud or transaction analysis tool, a financial assistant, the model can reason about the data; what you need underneath is infrastructure that extracts it from documents at volume, remembers each customer's financial context, and isolates one user's or one fund's data from another's.

This page is for developers and teams building those products. Exabase gives you document extraction through Extract, persistent financial-context memory through Memory, and per-user or per-fund isolation through Bases, on one platform. Your end users are the people and institutions using what you build, and each gets their own isolated environment without you building multi-tenancy yourself. Exabase provides the substrate; your product provides the financial logic, the fraud rules, the portfolio analysis, the decisions.

What you can build

Fintech products span a wider range than most sectors, and each shape maps to infrastructure that already exists.

A portfolio or financial assistant that remembers a customer's holdings, goals, and history across sessions and answers from their own statements and filings, persistent memory plus a per-customer RAG pipeline.

A statement and filing processor that extracts structured data from financial documents at scale, the document extraction at scale pattern, feeding whatever analysis your product performs.

A fraud or transaction analysis tool where your detection logic runs on top of a substrate that holds each customer's transaction context and history, the memory and extraction layers underneath, with your product supplying the fraud models and rules.

A per-fund or per-client intelligence tool that keeps each fund's or client's data and context isolated while giving agents the full picture within each, Bases scoped to whatever tenancy shape your product uses.

Fintech problems, solved

The problems fintech builders run into are specific, and each has an answer.

Extracting data from statements and filings. Financial documents arrive as PDFs and scans in countless formats. Extract turns statements, filings, and reports into clean, structured text and metadata through one API, including scans, so the data is usable rather than locked in documents, and it handles the volume financial products generate.

Customer financial context that persists. A customer's financial situation evolves, and a stateless agent re-establishes it every session. Memory holds holdings, goals, and history across sessions and keeps it current through contradiction resolution, so the agent reasons from the current picture.

Isolation that flexes to your tenancy shape. Fintech tenancy isn't always per individual, it might be per user, per fund, per client, or per institution. Bases give structural isolation from a single API call regardless of what the tenant is, so you scope to whatever unit your product needs and one tenant's data can't surface in another's. This is the multi-tenant memory pattern.

A substrate for analysis, not the analysis itself. Fraud detection and transaction analysis are your product's logic; what they need is reliable context to run against. Exabase supplies the memory of a customer's transaction history and the extracted data from their documents, so your models and rules have a clean, current substrate rather than you building the storage, memory, and extraction beneath them.

Accountability. Every memory carries creation and modification timestamps, so what an agent knew and did is reconstructable, the compliance and audit trails pattern. Exabase is HIPAA compliant and has passed CASA Tier 2 review, with AES-256 encryption at rest and structural data isolation between tenants, though whether your deployment meets your specific regulatory obligations is a matter for your own compliance review.

The infrastructure underneath

Four primitives carry most fintech products. Extract turns statements and filings into structured data at scale. Memory holds customer financial context across sessions, with the timestamps that make it auditable. Bases isolate per user, per fund, or per client from a single API call. Deep Search finds the relevant information in a customer's documents by meaning. All through one API key, so the substrate your financial product runs on is one platform rather than five services.

Built to scale across users and funds

A fintech product on this foundation scales with both its data and its tenants. Extraction handles document volume that would overwhelm a hand-built pipeline, and adding a user or a fund is one API call to create their isolated environment, so the separation holds whether you have a thousand tenants or a million. Each customer's or fund's financial context compounds within their own environment, the agent's picture getting more complete with use, while isolation between tenants stays absolute and the audit trail builds itself. The undifferentiated substrate stays the platform's problem, so your effort goes into the financial logic that differentiates your product rather than the storage and memory beneath it.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: document extraction at scale for statements and filings, long-term memory for any agent for financial context, multi-tenant memory for SaaS for per-user or per-fund isolation, and RAG pipelines that actually remember for answering from customer documents. There's a free tier to build against.

FAQs

Does Exabase do fraud detection or transaction analysis?

No, and that distinction matters. Exabase provides the substrate, extraction from financial documents, memory of a customer's transaction history and context, and isolation, that your fraud or analysis logic runs on. The detection models and rules are your product; Exabase gives them clean, current data to work against.

Can it extract structured data from statements and filings?

Yes. Extract turns statements, filings, and financial reports into clean, structured text and metadata through one API, handling scans as well as native PDFs, at the volume financial products generate.

Can I isolate per fund or per institution, not just per user?

Yes. A Base isolates whatever unit you scope it to, a user, a fund, a client, an institution, so you match your product's tenancy shape and one tenant's data can't surface in another's. It's the multi-tenant SaaS pattern.

Does an agent remember a customer's financial context across sessions?

Yes. Memory holds holdings, goals, and history and keeps it current through contradiction resolution, so the agent reasons from the current picture rather than reconstructing it each session.

Is it secure and compliant enough for financial data?

Exabase is HIPAA compliant and has passed CASA Tier 2 review, with security and privacy practices including AES-256 encryption at rest and structural data isolation between tenants. Whether a given deployment meets your specific regulatory obligations remains a determination for your own compliance review.

Is this a finished fintech product or something I build on?

Something you build on. Exabase is the infrastructure, extraction, memory, isolation, and search, and you build the portfolio agent, fraud tool, or financial assistant on top, supplying the financial logic yourself.