Healthcare AI development company

0

citation hallucinations

10+

clinical forms auto-filled

4

agents in one API

What we build

• Clinical multi-agent orchestration (LangGraph over GPT-4o)
• Medical-records RAG with strict citation contracts, so it never invents answers
• AI medical scribing: speech-to-text to structured forms (Deepgram Nova-2 Medical)
• Insurance eligibility automation (X12 270/271 EDI)
• Per-field confidence scoring for clinician review
• Tenant-scoped, encrypted credential handling (AES-256-GCM)

What healthcare AI development actually demands

Healthcare is the domain where the gap between a working demo and a deployable system is widest. A model that is right 95% of the time is impressive in a benchmark and unusable in a clinical workflow, because the 5% is indistinguishable from the 95% at the point of care.

Everything we build for healthcare therefore starts from the same constraint: the system must know what it does not know. Confidence scoring, grounded retrieval and clinician review are not features bolted on at the end, they are the architecture. A confident wrong answer in a medical record is worse than no answer at all, and that single fact drives most of the engineering decisions on this page.

Clinical multi-agent systems

Clinical products accumulate capabilities: note generation, eligibility checks, record search, routing. Built as separate endpoints, each grows its own conventions and its own copy of the prompt logic, and callers end up owning fragile routing code that has to know which endpoint handles which phrasing.

We build a single conversational entry point backed by a LangGraph orchestrator that routes each clinician request to a specialised sub-agent. For one healthcare client this collapsed four scattered endpoints into one API, removed roughly 80% of the code, and let clinicians express requests spanning two capabilities in one sentence.

Medical records RAG that cites its sources

Any answer about a patient that cannot be traced to the document it came from is unusable clinically, and a plausible-sounding unsourced answer is an active liability. Retrieval over medical records has to attach sources to every claim and refuse to answer when retrieval surfaces nothing supporting.

We build that as a structural property rather than a prompt instruction: answers are assembled from retrieved passages with sources attached, and an unsupported question returns that fact. On our clinical build this is why the records agent carries zero citation hallucinations.

AI scribing and documentation burden

Documentation is where clinician time disappears. The consultation is short; transcribing it and re-entering the same facts across ten or more mandatory forms is what consumes the day.

Transcription alone does not solve this, because a transcript still has to be read and re-entered. We extract clinical facts as structured data so a single fact populates every form that asks for it, with per-field confidence scoring so review is targeted. Our production scribe auto-fills more than ten mandatory forms from one consultation.

Where healthcare AI actually pays, and where it does not

Four shapes carry almost all the return we have seen, and they share one property: a clinician or an administrator was already doing the task, and the output is checkable.

**Documentation.** Clinical notes AI that drafts from the encounter rather than from dictation. The strongest case in healthcare because the burden is enormous, measurable, and directly tied to burnout. Teams comparing an AI medical scribe across vendors should note that the scribing is close to commodity now and the integration is not.

**Retrieval over clinical content.** Guidelines, formularies, prior records, trial protocols. Valuable precisely because every answer carries a citation a clinician can open, which makes it checkable in a way open-ended generation is not.

**Document and record processing.** Referrals, prior authorisations, faxed records, scanned histories. Unglamorous, high volume, and frequently the fastest payback in the portfolio.

**Triage and routing.** Deciding what something is and where it goes. Reliable, boring, and it removes queue time rather than clinical work.

Where we steer teams away: anything presented as clinical decision support without a regulatory pathway, anything where a plausible wrong answer reaches a patient unchecked, and any project whose success is described as engagement rather than as a task completed or a minute saved.

The data problem nobody scopes for

Healthcare AI projects overrun on data access rather than on modelling, and the pattern is consistent enough to plan around.

Getting to the data takes months, not weeks. Vendor approval, licence tiers, a security review, and a sandbox that behaves like production are each their own process. We start that in week one for exactly this reason, because the modelling work is worthless until it runs against real records.

Then the data itself is messier than described. The same patient exists three times under two spellings. Free text holds information the structured fields do not. Codes were entered for billing rather than for accuracy, so a diagnosis code reflects what could be claimed as much as what was found. Anyone building on coded data without understanding that will build something confidently wrong.

And the labels are expensive. Clinical ground truth needs clinician time, which is the scarcest resource in the building. Budget for it explicitly rather than assuming a retrospective chart review is free.

Regulatory posture, decided at the start

Three questions determine the architecture, and answering them late is what turns a working prototype into a system that cannot ship.

**Where may protected health information travel?** That draws a boundary through your system: which components sit inside, what gets de-identified on the way out, and whether a hosted model provider will sign a business associate agreement. A model that cannot sit inside the boundary has to run in your own infrastructure or receive de-identified input.

**Is this a medical device?** Software that informs a clinical decision may be regulated, and the answer depends on the claim you make rather than on the technology. A system that summarises what a clinician already has is different from one that suggests what they should do. Decide which you are building before you build it.

**What has to be reconstructable?** For any output that reaches a chart, you need the inputs, the model version and the policy in force at the time, retained for as long as the record is. That is an architecture decision, not a logging change.

HIPAA and the compliance surface

Audio, transcripts, records and generated notes are all PHI, so the compliance surface covers every hop. In practice that means BAAs with every service touching the data including the model provider, encryption in transit and at rest, role-based access control, consent capture and immutable audit logs.

We build to that discipline from the first commit because retrofitting it is substantially more expensive than designing for it. We are engineers rather than your compliance counsel: we build the controls, your compliance team owns the sign-off.

Why clinician review stays in the loop

The commercially attractive pitch is full autonomy. It is the wrong target for clinical documentation and decision support, and regulators are not the only reason.

Systems that escalate uncertainty get adopted. Systems that quietly fill fields get distrusted after the first bad note and then worked around, which destroys the time saving entirely. Designing review to be fast and targeted, rather than designing it away, is what makes these systems survive contact with a real clinic.

Proof, from delivered work

• OphyCare · Healthcare

  One conversational API over four agents

  4 sub-agents unified · 80% code cut in refactor · 0 citation hallucinations

  Read the case study →

• OphyCare · Healthcare

  AI medical scribe & form autofill

  10+ forms auto-filled · real-time speech-to-text

  Read the case study →

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