AI engineering for Healthcare & Life Sciences

AI that prepares the work, clinicians who decide

We build AI that prepares the work around clinical and administrative decisions: notes, authorizations, coding, and intake. A credentialed person signs, and the evidence travels with the artifact.

See the sign-off workflow

Every deployment runs under a business associate agreement, keeps PHI inside your environment, and requires a credentialed signature before an artifact reaches the chart or the payer.

Documentation-to-payer flow Encounter 88213 · MRI lumbar spine
  1. Visit documented Note drafted from the encounter, every clinical claim cited
  2. Codes suggested ICD-10 and CPT drawn from the note, flagged for review
  3. Medical necessity drafted Chart evidence mapped to the payer's own criteria
  4. Clinician verifies Signature required before anything reaches the payer
  5. Submitted through the payer API Da Vinci CRD, DTR, and PAS exchange
    clock starts
  6. Payer decision Urgent 72 hours, standard 7 days, denial criteria cited
    72h · 7d
  7. Appeal drafted Built from the denial letter and the same chart evidence
    if denied

The payer clock starts when a person signs, and the appeal is drafted from the same evidence.

Clinical AI has to earn trust the hard way

The workflows that pay off first are administrative. The controls that make them safe are the ones regulators now expect to see.

Administration is where the hours go

AMA surveys put prior authorization at roughly 40 requests and 12 to 13 hours per physician per week. That is the workload AI can take off clinical staff first, and the one with a measurable baseline.

Clinical AI fails quietly

On NOHARM, a clinical safety benchmark, the best-performing models still produced potentially severe-harm recommendations in roughly 1 in 14 consultations, and omission errors drove most of the serious ones. Ambient note errors skew the same way, and clinicians tend to trust drafts.

PHI flows through new surfaces

HIPAA's technical safeguards were written for systems that query stored data. An agent reasons over PHI in memory, retrieves it into context, and emits it in an artifact. Each of those hops needs its own control.

The data is fragmented

Large networks run several EHRs, and the records that matter most sit in unstructured notes and external documents. Retrieval quality is decided at ingestion, before any model sees the data.

Regulation is now a design input

CMS-0057-F sets 72-hour and seven-day prior-authorization decisions with FHIR APIs to follow, ONC HTI-1 requires algorithm transparency for predictive decision support, and the FDA expects a predetermined change control plan for AI-enabled device software. Architecture decided late cannot absorb them.

Agentic AI fits work that prepares a decision

An agent can assemble, draft, and check the work around a decision. The decision stays with the person who holds the license and the accountability.

Prior authorization and payer workflows

Agents assemble the medical-necessity packet from the chart and the payer's own criteria, draft appeals, and track status, while a credentialed person verifies every clinical claim before submission.

  • PA packet assembly
  • Criteria mapping
  • Denial appeals
  • Status tracking

Clinical documentation and coding

Ambient documentation drafts the note for a clinician to review and sign. Coding suggestions arrive with the supporting text, and anything uncertain is flagged rather than filed.

  • Ambient drafting
  • Note structuring
  • Coding suggestions
  • Query responses

Patient access and coordination

Intake, records requests, referral routing, and scheduling work off the same retrieval layer, with access limited to what the requesting role may see.

  • Patient intake
  • Records requests
  • Referral routing
  • Care coordination

Life sciences and research operations

Protocol screening, regulatory documentation, and literature synthesis run over approved sources with citations, so a reviewer can check the claim rather than trust the summary.

  • Eligibility screening
  • Regulatory documents
  • Literature synthesis
  • Site support

No client results published

What a healthcare deployment has to produce

We are not publishing outcomes from healthcare or life-sciences engagements. What follows is the standard every deployment has to meet before go-live: a sign-off boundary written into the workflow, PHI controls at every hop, retrieval grounded in approved sources, and a decision record for each run.

The constraints below are commitments we make on every engagement. They are not a case study, and no client results are claimed here.

  • BAA in place before data moves
  • PHI stays inside your environment
  • Credentialed sign-off on every clinical artifact
  • Decision record for every run

Our path to a governed deployment

We start from the workflow that consumes the most clinician time, then build the boundary, the controls, and the evidence around it.

  1. 1

    Name the workflow and its owner

    Pick the workflow with a measurable baseline and a named sign-off owner, and write down what the system prepares against what a person decides.

  2. 2

    Set the boundary and map PHI

    Write the authority boundary into the workflow, then trace every hop patient data takes: model endpoint, retrieval index, telemetry, and review queue.

  3. 3

    Prove retrieval on your documents

    Ingestion, chunking, and citation coverage tested against your own notes, policies, and payer criteria before anyone relies on an answer.

  4. 4

    Build the gate and the record

    The review surface, the policy checks that run before an artifact moves, and the decision record that keeps sources, versions, and reviewer actions together.

  5. 5

    Validate with your clinicians

    Clinician-graded sets score accuracy, omission rate, and escalation behavior on your material, and the same suite runs before each release.

  6. 6

    Operate and revalidate

    Drift, override rates, and cohort-level outcomes are reviewed on a cadence, with revalidation triggered by model, data, or workflow changes.

The controls sit outside the model

Policy, access, and sign-off are enforced by a service the model cannot reason around, and each verdict is recorded before an artifact moves.

Review our governance approach
01

BAA and environment

Deployment runs under a business associate agreement, with PHI confined to infrastructure you control. Model endpoints are covered by your agreement, and customer data does not train a shared model.

02

Minimum necessary by design

Access is limited by role and purpose to what the request requires, which is HIPAA's minimum-necessary standard enforced in code rather than stated in policy. Scoped credentials and workload identity bound each agent to the records it may reach.

03

Pre-action checks

Policy runs outside the model before an artifact moves: citation coverage, access scope, and the rules that decide what needs a person. Runtime governance returns allow, deny, or escalate on each action.

04

The sign-off gate

Clinical and payer-facing artifacts wait for a credentialed signature, and the reviewer sees the sources, the checks, and the draft together. Uncertain cases escalate with the same package attached.

05

Records and access history

Every run produces a decision record with sources, versions, checks, and reviewer action, alongside the access history for the records it touched. The trajectory covers each step of the run.

06

Clinician-graded evaluation

Evaluation sets are built with your clinicians and score accuracy, omission rate, and escalation behavior on your own material. The eval harness runs before any model, prompt, or workflow change.

07

Monitoring and revalidation

Drift, override rates, cost per artifact, and cohort-level outcomes are reviewed on a cadence, with revalidation triggered by model, data, or workflow changes.

Standards and regulation

The rules that shape what a healthcare deployment has to produce, and where each one lands in the architecture.

HIPAA
Access, audit, and retention rules for health data, enforced in code rather than stated in policy.
ONC HTI-1
Algorithm transparency for predictive decision support in certified health IT, including intervention risk management.
CMS-0057-F
72-hour urgent and seven-day standard prior-authorization decisions, with denial criteria cited and FHIR APIs to follow.
FDA PCCP
How an approved AI-enabled device software function may change without a new marketing submission.
FHIR and HL7
The interfaces an agent reads and writes through, including the Da Vinci CRD, DTR, and PAS guides behind prior authorization.
ICD-10 and CPT
The coding systems a note is mapped to when suggestions are drafted, each requiring review before it is filed.
EU AI Act
Classifies many clinical AI systems as high-risk, which carries oversight, logging, and risk-management duties.

Flytebit products fit health-tech engineering teams

Use these products when review, tests, documentation, or release control constrain the teams building your clinical and payer software.

PASSRHealth-tech review slows the release

Review AI-generated changes to clinical and payer-facing software across eight categories before merge, with findings a security reviewer can act on.
TESTRCoverage trails the diff

Generate tests around changed functions in integration, claims, and scheduling code, then run them in your existing pipeline.
DOCKRRegulated documentation drifts

Regenerate technical and architecture documentation from the repository so the written record matches the deployed system.
Vibe Coding TransformationAI output outpaces the review process

Reshape requirements, review, testing, and release around AI-assisted engineering so throughput follows generation speed.

Technical context for clinical AI

Use these guides to examine the governance, validation, and integration work behind the page.

Questions clinical and technology leaders ask

Do you build systems that make clinical decisions?

No. The systems we build prepare work: drafts, packets, suggestions, and summaries. A credentialed person decides and signs. Where a system supports a clinical decision, human review is a design requirement rather than a setting someone can turn off.

How is patient data handled?

PHI stays in infrastructure you control, under a business associate agreement. Model endpoints are covered by that agreement, retrieval is limited by role and purpose, and customer data does not train a shared model. HIPAA rules and data residency apply to the retrieval index, the telemetry sink, and the review queue as well as the database.

Which workflow should we start with?

The one with a measurable baseline and a clear sign-off owner. Prior authorization and clinical documentation come up most often, because the hours are documented and the boundary is easy to define.

How do you validate a clinical AI system?

Local data, clinician-graded sets, and a defined cadence. Public benchmarks and vendor-reported performance inform screening, but they do not answer whether a tool is safe in your population. We score accuracy, omission rate, and escalation behavior on your own material, then revalidate after any model, data, or workflow change.

How do you handle FDA, HTI-1, and EU AI Act requirements?

They shape the architecture. An AI-enabled device software function needs a predetermined change control plan and lifecycle documentation, certified health IT with predictive decision support needs algorithm transparency under ONC HTI-1, and clinical systems classified high-risk under the EU AI Act need oversight and logging evidence. We build that evidence while the system is being built rather than assembling it afterward.

Can you integrate with our EHR?

Yes. We work through FHIR and HL7 interfaces and the vendor APIs available to you, including Epic, Oracle Health, and athenahealth, and through the payer-side systems behind prior authorization.

How do we measure whether this works?

At the workflow level: prior-authorization cycle time, time spent per note, edit and override rates, omission flags caught before sign-off, sign-off latency, and cost per completed artifact. Clinical outcomes are reviewed with your clinical leadership rather than inferred from throughput.

Can you review a clinical AI product we are evaluating?

Yes. An oversight engagement reviews the architecture, validation evidence, controls, and operating model while the other team builds or operates the system, with findings delivered at agreed milestones.

Find the first workflow worth putting under sign-off

We will examine the workflow, the sign-off boundary, where PHI moves, the validation approach, and the failure modes before recommending a build.

Reviewed by Jayaveer Bhupalam, Founder & CTO Last updated September 28, 2026