Clinician in the loop, or it doesn't ship.

Why now

Administrative load, not diagnosis, is where healthcare AI pays for itself today: intake, prior authorisation, coding, discharge instructions and the endless chase for a missing document. These are also the safest places to start, because a wrong answer produces a delay rather than a harm — provided the design keeps it that way.

• 01

  Intake is a phone queue

  Scheduling, insurance verification and pre-visit forms consume front-desk hours that no amount of staffing fixes, and every dropped call is a no-show.

• 02

  Documentation outlives the appointment

  Clinicians finish notes after hours. Draft-and-review beats blank-page, but only if the draft is grounded in the encounter rather than invented.

• 03

  Prior authorisation is a paperwork tax

  Assembling the same clinical evidence into a different payer's format, repeatedly, by hand.

• 01

  PHI does not go in a prompt by default

  We de-identify at the boundary and re-associate after, so the model sees the clinical question without the identity attached. Where full context is unavoidable, it runs under a BAA or inside your own infrastructure.

• 02

  The system drafts; a clinician decides

  Nothing reaches a patient without review on any pathway that touches clinical content. That gate is a hard architectural boundary, not a configuration toggle someone can turn off later.

• 03

  Refusal beats a plausible answer

  Out-of-scope questions get an explicit hand-off to a human with a callback, not a hedged paragraph. We test the refusal path as carefully as the answer path.

• Epic & FHIR APIs
• Athenahealth
• HL7 v2 feeds
• Twilio & WhatsApp Business
• Zendesk
• Snowflake
• S3 with encryption at rest
• Practice-management and RCM systems

• RAG for healthcare and clinical documentsRetrieval over clinical guidelines, payer policy and internal SOPs — with the citation, the version, and an explicit refusal when the library doesn't cover the question.Read
• AI agents for healthcare operationsAgents that handle scheduling, reminders, insurance verification and document chase across voice and messaging — escalating anything clinical to a person immediately.Read
• Healthcare AI governance and readinessUse-case triage against clinical risk, a data-handling design that survives a HIPAA or DPDP review, and an oversight model your clinical governance body will actually accept.Read
• 30-day AI sprint for healthcarePick the administrative workflow that hurts most. In 30 days you get working software, an eval score, and an honest recommendation — including the recommendation not to proceed.Read

1. Step 1 · Validate

   30-day Sprint

   Prove the use case before you commit. Working prototype on real data, eval scores, and an honest signal in 30 days. Fixed scope, fixed fee.

   $4,500 fixed

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2. Most teams land here

   Step 2 · Build

   Prototype → Production

   Turn the validated prototype into a real product. Auth, DB, payments, tests, monitoring, deployed. Sprint fee credits toward this engagement.

   from $6,000

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3. Step 3 · Scale

   Managed Retainer

   Ongoing operation, eval cycles, model iteration, and cost guards. We keep the system improving so your team can focus on growth.

   from $750/mo

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FAQ

AI in healthcare: the questions we get.

• Will you sign a BAA?

  Yes, and we build to it: we deploy inside your account where the data already lives, restrict the model set to providers whose terms permit PHI, and keep an access log for anything our engineers can see during the engagement.

• Can this write clinical notes?

  It can draft, grounded in the encounter record, for a clinician to edit and sign. We won't build a pathway where a generated note reaches the chart without a signature — that's the line between a documentation tool and an unlicensed practitioner.

• What about patients who ask medical questions?

  The agent answers only from your approved patient-education material with a citation, and hands anything else to a human with the conversation attached. Triage-shaped questions route immediately; the escalation path is the first thing we test.

• AI for Fintech & BankingMost banking AI pilots die in review, not in testing. We build the boring parts first — decision logs, evals, human approval gates — so the model has somewhere safe to be useful.Read
• AI for Retail & E-commerceA bot that says 'let me check that for you' and can't is worse than no bot. We connect agents to the order, the inventory and the returns policy so they can finish the job.Read
• AI for SaaSThe model is the easy part. Multi-tenancy, per-seat cost, evals and a permissions model that doesn't leak across tenants are what turn a demo into something you can charge for.Read