AI infrastructure for healthcare

Patient data is sensitive, scattered, and tightly regulated, which is exactly why building AI products for healthcare is hard in ways that have nothing to do with the model. If you're building clinical agents, patient-facing assistants, or care-coordination tools, the work that slows you down is the infrastructure underneath: keeping every patient's data strictly isolated, getting structured information out of medical records, remembering a patient's history across visits, and being able to show what an agent did and knew.

This page is for developers and teams building those products. Exabase gives you per-patient isolation through Bases, medical-record extraction through Extract, persistent clinical memory through Memory, and audit trails built into how memory is stored. Your end users are the clinicians, patients, and care teams using what you build, and each patient gets their own isolated environment without you building the separation yourself.

A note on scope before going further: this page is about the data infrastructure beneath a healthcare product, not clinical decision-making or any judgement about what a given deployment must do to meet its obligations. Exabase is HIPAA compliant and has passed CASA Tier 2 review, but whether your specific use meets your regulatory requirements is a determination for your own compliance review.

What you can build

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

A clinical agent with patient context that holds a patient's medication history, past interactions, and stated preferences, scoped entirely to that patient, and recognises them across visits rather than starting cold. That's the healthcare agents with patient context pattern: per-patient Bases, persistent memory, audit trails.

A medical-record processing tool that extracts structured information from records, intake forms, and clinical documents in whatever format they arrive, the document extraction at scale pattern applied to clinical paperwork.

A patient-facing assistant that answers from a patient's own documents and remembers the conversation across sessions, a per-patient RAG pipeline with memory, isolated per patient.

A care-coordination tool where context about a patient is shared among the people and agents involved in their care, built on a shared per-patient environment, the coordination side of multi-agent coordination.

Healthcare problems, solved

The problems healthcare builders hit are consistent, and each has a specific answer.

Per-patient data isolation. One patient's data surfacing in another's session isn't a quality issue, it's a breach. Bases make isolation structural: each patient gets a sealed environment, and an operation scoped to it can only ever see that patient's data, so cross-contamination isn't something you mitigate, it's something the architecture prevents. This is the multi-tenant memory pattern where the stakes are clinical.

Getting information out of medical records. Records, intake forms, and clinical notes arrive as PDFs and scans in every format. Extract turns them into clean, structured text through one API, including scans, so the information is usable rather than locked in documents.

Remembering a patient across visits. A patient's context shouldn't be reconstructed from scratch each visit. Memory holds medication history, past interactions, and preferences, and keeps the picture current through contradiction resolution, so a discontinued medication is treated as discontinued rather than lingering alongside the current one.

Showing what an agent did and knew. Every memory carries creation and modification timestamps, so the agent's state of 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 your deployment meets your specific obligations is a matter for your own compliance review.

The infrastructure underneath

Four primitives carry most healthcare products. Bases give per-patient isolation from a single API call. Extract turns medical records into clean, structured text. Memory holds clinical context across visits and keeps it current, with the timestamps that make it auditable. Deep Search finds the relevant passage in a patient's documents by meaning. All of it through one API key, rather than assembling and isolating four services yourself.

Built for patient-scale isolation

A healthcare product on this foundation scales without the isolation getting more fragile. Adding a patient is one API call to create their sealed Base, so the separation that matters most holds whether you have a hundred patients or a million, and it doesn't weaken as you add features, because individual operations can't breach a structural boundary. Each patient's record and context compound within their own environment: the more an agent works with a patient, the more complete and current its picture, while the isolation between patients stays absolute. The compliance-critical properties, separation and an inherent audit trail, are the ones that hold steady as you grow, which is the opposite 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: healthcare agents with patient context, compliance and audit trails for AI, and multi-tenant memory for SaaS for the isolation pattern. There's a free tier to build against, and any healthcare deployment should go through your own compliance review before production.

FAQs

Is Exabase HIPAA compliant?

Yes. 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.

How is one patient's data kept separate from another's?

Each patient gets their own Base, a structurally isolated environment. Any memory or search operation scoped to a patient's Base can only see that patient's data, so separation is a property of the architecture rather than a filter applied at query time. It's the multi-tenant SaaS pattern applied to patient data.

Can it extract information from scanned medical records?

Yes. Extract reads scans as well as native PDFs and returns clean, structured text, so records and intake forms become usable regardless of format.

Can I reconstruct what an agent knew at the time of a past interaction?

Yes. Every memory carries creation and modification timestamps, so you can establish the agent's state of knowledge when it acted, even after facts later change. This is the basis of the compliance and audit trails pattern.

How does it handle a discontinued medication or resolved condition?

The change supersedes the prior state through entity resolution, so a discontinued medication is treated as discontinued and the agent reasons from the patient's current state rather than a contradictory history.

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

Something you build on. Exabase is the infrastructure, isolation, extraction, memory, and audit trail, and you build the clinical agent, patient assistant, or coordination tool on top, for clinicians and patients to use.

Does Exabase make clinical decisions or give medical advice?

No. Exabase is data infrastructure. What an agent does with patient context, and whether a use is clinically and legally appropriate, is determined by you and your compliance review, not Exabase.