AI infrastructure for banking

Banking is where data isolation stops being good practice and becomes the whole game. A customer-facing banking agent has to know one customer's account history in detail while having no possible route to another's, process KYC and onboarding documents at volume, and leave a record of what it did that stands up to scrutiny. If you're building banking AI, the model isn't the obstacle, the infrastructure underneath is: isolation that can't leak, document processing that scales, memory that persists, and an audit trail that's there by default.

This page is for developers and teams building those products. Exabase gives you per-account isolation through Bases, KYC and document processing through Extract, persistent account-history memory through Memory, and audit trails built into how memory is stored. Your end users are the banks and their customers using what you build, and each account gets its own sealed environment without you building the separation yourself.

What you can build

Banking AI products tend to be one of a few shapes, each on infrastructure that already exists.

A customer-facing banking agent that knows a customer's account history and answers from it, scoped entirely to that customer, with no path to anyone else's data, per-account Bases plus persistent memory.

A KYC and onboarding processor that extracts structured data from identity documents, statements, and forms automatically, the document extraction at scale pattern applied to onboarding paperwork, with Workers handling a continuous intake stream.

An account-history copilot for support or relationship teams that holds the full context of a customer across every interaction, so the agent doesn't ask a customer to repeat what the bank already knows, the customer support agents pattern in a banking context.

A compliance-grade agent whose every action is reconstructable from a timestamped record, the compliance and audit trails pattern, isolated per customer.

Banking problems, solved

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

No data leaks between customers. This is non-negotiable, and "we filter by customer ID" is not an answer that survives an audit. Bases make isolation structural: each account gets a sealed environment, and an operation scoped to it can only ever see that account's data, so a leak between customers isn't something you guard against, it's something the architecture doesn't permit. This is the multi-tenant memory pattern where the stakes are regulatory.

KYC and document processing at volume. Onboarding and compliance generate a flood of identity documents, statements, and forms. Extract turns them into structured data through one API, including scans, and processes the volume, while Workers handle the continuous stream.

Persistent account history. A customer relationship spans years. Memory holds account history and context across every interaction and keeps it current through contradiction resolution, so an agent reasons from the current state rather than a stale snapshot.

Auditable by default. Every memory carries creation and modification timestamps, so what an agent knew and did at the time of an action is reconstructable without a separate logging system, 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 banking products. Bases give per-account isolation from a single API call, with no route between customers. Extract turns KYC and onboarding documents into structured data at scale. Memory holds account history across interactions, with the timestamps that make it auditable. Deep Search finds the relevant information in a customer's documents by meaning. All through one API key, rather than assembling and isolating four services and proving the walls hold.

Isolated and auditable as you scale

A banking product on this foundation keeps its most important properties as it grows. Adding a customer is one API call to create their sealed account environment, so isolation holds whether you serve thousands of customers or millions, and it doesn't weaken as you add features, because individual operations can't breach a structural boundary. The audit trail builds itself as agents work, so there's no growing backlog of un-logged actions and no point where someone reconstructs missing history. Each customer's account context compounds within their own environment while the separation between customers stays absolute. The properties regulators care about most, isolation and auditability, are exactly the ones that hold steady at scale rather than degrading, which is the reverse of what happens when they're maintained by hand.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: multi-tenant memory for SaaS for isolation, compliance and audit trails for AI for the audit layer, document extraction at scale for KYC processing, and long-term memory for any agent for account history. There's a free tier to build against.

FAQs

How do you guarantee no data leaks between customers?

Each customer's account lives in its own Base, a structurally isolated environment. An operation scoped to a Base can only ever access that Base, so cross-customer access isn't prevented by filtering you have to get right every time, it's prevented by the architecture. It's the multi-tenant SaaS pattern applied to accounts.

Can it process KYC and onboarding documents at scale?

Yes. Extract turns identity documents, statements, and forms into structured data through one API, handling scans as well as native PDFs, and Workers process a continuous intake stream on a schedule.

Does an agent remember a customer's account history?

Yes. Memory holds account history and context across interactions and keeps it current through contradiction resolution, so the agent works from the current state rather than reconstructing it each session.

How is it auditable?

Every memory carries creation and modification timestamps, so what an agent knew at the time of an action is reconstructable without a separate logging system. This is the compliance and audit trails pattern, and the audit trail forms as the agent works.

Is it secure and compliant enough for banking?

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 banking product or something I build on?

Something you build on. Exabase is the infrastructure, isolation, document processing, account memory, and audit trail, and you build the customer-facing agent or processing tool on top, for banks and their customers to use.