AI infrastructure for SaaS companies

Every SaaS product is being asked to add AI features, and most teams discover the same thing once they start: the model is the easy part. What slows you down is the infrastructure underneath, per-customer memory that stays isolated, search over your content, knowledge bases that don't go stale, and the multi-tenancy to keep every customer's data separate. That's months of backend work before a single feature ships, and it's the same work for every SaaS company doing it.

This page is for product and engineering teams adding AI to a SaaS product. Exabase gives you per-customer memory through Bases, search through Deep Search, self-maintaining content through Workers, and document processing through Extract, all behind one API key and multi-tenant from day one. Your customers each get an isolated environment without you building the partitioning, so you ship the feature instead of the foundation.

What you can build

The AI features SaaS teams want tend to be one of a few shapes, each on infrastructure that already exists.

A per-customer AI assistant inside your product that remembers each customer's context, usage, and history, isolated to them, the multi-tenant memory pattern with long-term memory scoped per tenant.

A searchable help centre or in-product support agent that answers from your documentation and a customer's own context, the customer support agents and RAG pipelines patterns.

A self-maintaining knowledge base behind a feature, kept current as your docs and content change without manual re-ingestion, the self-maintaining knowledge bases pattern.

A document feature that lets customers upload and search their own files inside your product, document extraction at scale plus per-customer Deep Search.

More broadly, this is the internal tools story turned outward: the same all-in-one foundation, used to ship customer-facing features rather than internal ones.

SaaS problems, solved

The problems SaaS teams hit when adding AI are consistent, and each has an answer.

Multi-tenancy you don't want to build. Per-customer isolation usually means partition keys threaded through every query and the standing worry that one missed filter leaks data. Bases make isolation structural and one API call: each customer gets a sealed environment, and the leak-on-a-forgotten-filter failure mode isn't expressible. It's multi-tenant memory for SaaS, which is the canonical version of this.

Assembling five services. A memory layer, a vector database, file storage, search, and background jobs, chosen, wired, and operated. Exabase is all of it behind one API key, so you build against one platform where the pieces are designed to work together rather than maintaining the seams between four.

Search that works. Deep Search finds content by meaning across your help centre or a customer's documents, and holds quality as content grows past where naive search collapses.

Content that goes stale. Help centres and knowledge bases rot as docs change. Workers keep them current automatically, re-processing updated content on a schedule, the self-maintaining knowledge bases pattern.

Memory that has to be isolated and self-improving at once. Memory gives each customer's feature a picture that sharpens with use through contradiction resolution, and because it's scoped to a Base, that personalisation is built only from that customer's data.

The infrastructure underneath

Four primitives carry most SaaS AI features. Bases give per-customer isolation and multi-tenancy from a single API call. Memory gives each customer a self-improving, isolated memory. Deep Search searches your content and theirs by meaning. Workers keep content current, and Extract turns customer documents into searchable text. One API key covers the lot, which is the difference between shipping a feature this quarter and building a platform first. Exabase follows strong security and privacy practices, including AES-256 encryption at rest and structural data isolation between tenants, and has passed CASA Tier 2 review.

Multi-tenant from day one, cheaper per customer over time

A SaaS feature on this foundation is multi-tenant from the first line, and the economics improve as you grow. Adding a customer is one API call to create their isolated environment, so onboarding the ten-thousandth costs the same as the first, and the isolation doesn't get more fragile as you add customers or features. Each customer's memory and content compound within their own environment, the feature getting more useful to them with use, while your cost to serve stays flat. And every new AI feature you build reuses the same foundation rather than re-assembling it, so the second feature is cheaper than the first. The undifferentiated infrastructure stays the platform's problem; your team ships product.

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 the isolation foundation, internal tools powered by AI for the all-in-one pattern, customer support agents for in-product support, and self-maintaining knowledge bases for help content. There's a free tier to build against.

FAQs

Do I have to build multi-tenancy myself?

No, that's the point. Bases give you per-customer isolation from a single API call, structurally rather than through partition keys you thread through every query. The class of bug where a forgotten filter leaks data across customers isn't expressible. It's the multi-tenant SaaS pattern.

What does the one API key replace?

Typically a memory layer, a vector database, file storage, a search engine, and background jobs, plus the glue between them. Instead of choosing, wiring, and operating five services, you build against one platform where those capabilities work together.

Can each customer have their own isolated memory and search?

Yes. Scope each customer to their own Base and their memory, documents, and search are fully isolated from every other customer's, with personalisation built only from their data.

How do I keep an in-product help centre or knowledge base current?

Use Workers to re-process updated content and add new content on a schedule, so the knowledge base stays current without manual upkeep. It's the self-maintaining knowledge bases pattern.

Is it secure enough to put customer data in?

Exabase follows strong security and privacy practices, including AES-256 encryption at rest and structural data isolation between tenants, and has passed CASA Tier 2 review. It's also HIPAA compliant. Whether a given deployment meets your specific obligations is a determination for your own review.

Is this for shipping customer-facing features or internal tools?

Both. This page is about customer-facing SaaS features, while internal tools powered by AI covers the same foundation used for internal ones. The infrastructure is the same; the difference is who the feature is for.