AI infrastructure for legal

Legal work is document-heavy, precedent-driven, and unforgiving about confidentiality, which makes it one of the harder sectors to build AI products for and one of the most valuable to get right. If you're building legal tech, a research tool, a contract platform, a discovery assistant, you're not short of model capability; you're short of the infrastructure underneath that keeps each client's matter isolated, gets clean text out of filings and contracts, and finds the relevant clause across thousands of pages.

This page is for developers and teams building those products. Exabase gives you per-client isolation through Bases, document extraction through Extract, and meaning-based search through Deep Search, so the foundation your legal product needs is one platform rather than four services you stitch together. The end users are your customers, the firms and lawyers using what you build, and each of them gets their own isolated environment without you building multi-tenancy yourself.

What you can build

Most legal AI products are one of a few shapes, and each maps to infrastructure that already exists.

A case research assistant that searches across thousands of filings, briefs, and judgments by meaning and carries matter context from one session to the next. That's the legal AI agents for case research pattern: Extract for the documents, Deep Search across the library, Memory for matter continuity.

A contract analysis tool that finds clauses, dates, obligations, and parties across an entire body of agreements, "every non-compete across all vendor contracts" returning real results. That's contract analysis and search, built on extraction and meaning-based search over a corpus.

A discovery or due-diligence assistant that ingests tens of thousands of pages and lets a lawyer find the relevant passage in minutes rather than days, the document-heavy end of legal AI agents combined with document extraction at scale.

A matter-aware copilot that remembers the parties, the key dates, and the state of the work across every session, so a lawyer returning to a matter after weeks picks up where they left off rather than rebuilding context, the long-term memory layer scoped per matter.

Legal problems, solved

The problems legal builders run into are consistent, and each has a specific answer.

Client confidentiality and matter isolation. Each client's, and often each matter's, data has to be sealed off from every other, and "we filter by client ID" is not an answer that survives scrutiny. Bases make isolation structural: each client or matter gets its own environment, and an operation scoped to it can only ever see that environment, so cross-contamination isn't something you guard against, it's something the architecture prevents. This is the multi-tenant memory pattern applied where the stakes are professional and ethical.

Getting text out of filings and contracts. Legal documents are PDFs and scans in inconsistent formats, often hundreds of pages. Extract turns them into clean text with page numbers, including scans, so a found passage traces back to its exact page, and it processes large volumes through one API rather than a parser you maintain.

Finding the clause that never uses the obvious words. A non-compete might never say "non-compete"; an indemnity is worded six ways across six agreements. Deep Search matches by meaning rather than keyword, and holds quality across a large corpus where naive search collapses at scale, which matters most when completeness is the point.

Context that survives across a long matter. Matters run for months. Memory holds the parties, dates, and state of the work and keeps it current as things change through contradiction resolution, so the agent reasons from where the matter actually stands.

Showing what an agent did and knew. Every memory carries creation and modification timestamps, so an agent's knowledge at the time of an action 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 a given deployment meets your specific professional and regulatory obligations is a matter for your own review.

The infrastructure underneath

Four primitives carry most legal products. Bases give you per-client and per-matter isolation from a single API call. Extract turns filings, contracts, and discovery into clean, searchable text. Deep Search finds clauses and passages by meaning across large corpora. Memory holds matter context across sessions and keeps it current. You reach all of it through one API key, rather than assembling a vector database, file storage, a memory layer, and a search engine and keeping the seams aligned.

Built to scale across your clients

A legal product built on this foundation gets stronger as it grows, in a way a hand-built stack doesn't. Adding a client is one API call to create their isolated Base, so onboarding the thousandth firm is the same as the first, and the structural isolation doesn't get more fragile as you add clients or features. Each client's corpus and matter memory compound within their own environment: the more they use the product, the more complete their searchable library and the sharper their matter context, while your cost to serve them stays flat. The undifferentiated infrastructure stays the platform's problem, and your effort goes into the legal product itself.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: legal AI agents for case research, contract analysis and search, and multi-tenant memory for SaaS for the isolation pattern. There's a free tier to build against.

FAQs

Can it search scanned contracts and filings, not just digital PDFs?

Yes. Extract reads scans as well as native PDFs and returns clean text with page numbers, so a found clause traces back to its exact page in the document.

How do I keep each client's data isolated?

Give each client, or each matter, its own Base. A Base is structurally isolated, so operations scoped to it can only see that client's data and cross-contamination isn't possible through the API. It's the multi-tenant SaaS pattern applied to legal.

Can it find a clause that doesn't use the obvious term?

Yes. Deep Search matches by meaning, so a non-compete or indemnity surfaces however it's worded, and it's hybrid, so a specific defined term or party name still matches exactly.

Will search stay complete across tens of thousands of pages of discovery?

That scale is where naive vector search degrades through semantic collapse. Deep Search is built to hold retrieval quality as the corpus grows, which matters when finding every instance is the goal.

Does it remember a matter between sessions?

Yes. Memory holds the matter's parties, dates, and state, and keeps it current through contradiction resolution, so an agent resumes where it left off rather than starting cold.

Is it HIPAA or otherwise certified for regulated legal work?

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 legal tool or something I build on?

Something you build on. Exabase is the infrastructure, isolation, extraction, search, and memory, and you build the research tool, contract platform, or discovery assistant on top, for your own clients to use.

Does Exabase provide legal advice or assess a contract's effect?

No. Exabase is data infrastructure. What an agent does with the material, and any judgement about a contract's meaning or enforceability, is for you and qualified legal review, not Exabase.