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Inseed

AI for healthcare apps

Healthcare apps, made smarter without a rewrite.

We add the AI features that matter in clinical products, patient-facing assistants, clinical document understanding, remote-monitoring signal analysis, and EHR-aware summarization, to the app you already ship.

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You have a working app. Leadership wants AI in it.

Your app is in production. It passed review, it handles PHI, and clinicians or patients use it every day. Now leadership wants AI inside it: an assistant that answers patient questions, triage that reads a lab report, a summary that saves a nurse twenty minutes per shift. The idea is right. The way most shops respond is not.

Every quote you get back starts with "first we rebuild". In a regulated product that is not a quarter of work, it is a re-validation of everything: the BAA, the security review, the data flows your compliance team already signed off on. The real constraint in healthcare is not the model, it is touching a system that is already audited. So we do not touch it. We add AI as a layer beside the app you have, and the parts that passed review stay exactly where they are.

What we build

Four AI features that earn their place in a clinical app.

These are the patterns we see land in healthcare products. Each one sits beside your existing workflow, not inside the path a regulator already approved.

Patient-facing assistants

An in-app conversational assistant that answers questions about care, medications, and appointments, grounded in your own approved content rather than the open web.

What it changes
A patient gets a clear answer at 11pm without waiting on the nurse line, and low-value questions stop landing in a clinician inbox.
How it is built
Retrieval over your vetted content, with guardrails that refuse out-of-scope medical advice and hand off to a human when a question crosses the line.

Clinical document understanding

Structured extraction from the documents your users already upload: lab PDFs, referral letters, intake forms, insurance cards.

What it changes
A nurse stops retyping a lab report by hand. The app reads it, pre-fills the fields, and flags what it is unsure about.
How it is built
A document-parsing step plus an LLM extraction pass, with a human-review checkpoint before anything is written to a record.

Remote-monitoring signal analysis

Turning continuous device or wearable signal into summaries and flags: trends, anomalies, and a plain-language readout for a care coordinator.

What it changes
A coordinator watching two hundred patients sees the five who moved, not two hundred raw charts to scroll.
How it is built
Aggregation and thresholds compute the numbers first; model-generated summaries sit on top and never become the sole basis for a clinical decision.

EHR-aware summarization

Pulling the relevant slice of a patient record through FHIR and summarizing it for the moment: pre-visit, handoff, or discharge.

What it changes
A clinician opens a visit with a one-paragraph summary instead of scrolling six months of history.
How it is built
A FHIR read scoped to the encounter, summarized by a model that cites back to the source entries so a human can verify.

Proof

What we have actually shipped in healthcare.

We name only work we can stand behind, and we are precise about who delivered it.

VocalMD

Built by our founder

A HIPAA-compliant telehealth app connecting US doctors and patients, shipped to both stores and live in production for over two years. It pulls patient medications, conditions, and lab reports from hospital systems via EPIC FHIR, and keeps all sensitive data end-to-end encrypted.

  • 100+ doctors and 500+ patients onboarded in the first month
  • Used across 200+ hospitals
  • EPIC FHIR integration and end-to-end encryption (Virgil)

Nightingale Labs

Delivered by our team via Toptal

A remote-monitoring platform built around an under-mattress bed sensor that reports heart rate and ballistocardiography to clinical dashboards. Our founder and team built the cross-platform frontend: a hand-built charting engine rendering the same clinical charts on web, iOS, and Android from one codebase, RBAC across five clinical roles, and QR-based device onboarding.

  • ~40 screen modules across 114 API endpoints
  • One React Native + React Native Web codebase to web, iOS, and Android
  • Founded by a US cardiologist

Hazel Health

Delivered by our team via Andela

Frontend engineering across a US school-based telehealth platform serving students. Work our team delivered spanned the OpenTok and Vonage video-visit stack, EHR charting with ICD-10, and the referral, scheduling, and insurance-capture intake flow.

  • Multi-portal app: patient, provider, support, admin, school
  • Real-time video visits with interpreter and phone bridging
  • 152 authored commits over roughly seven months

Straight answer on the gap: we have not yet shipped a production LLM feature inside a HIPAA-regulated app. What we have shipped is both halves separately, production healthcare apps that handle PHI, FHIR, and end-to-end encryption, and AI features (retrieval, embeddings, vibe search, voice) in other live mobile products. The audit is where we prove the combination on your app, in a throwaway prototype, before you commit to a build.

Borrowed proof, labeled honestly

React Native holds up under clinical load.

React Native runs in production healthcare at Doctolib, Hinge Health, and Oscar. These are not our clients. They are proof that the framework we build on is already trusted in serious, regulated healthcare at scale, so the question for your product is the feature, not the foundation.

How it starts

It starts with an audit, not a rebuild.

We do not open with a six-figure proposal. We open with one week of work that tells you exactly where AI fits in your app, what it costs to run, and whether it is worth building at all.

Step 1

Mobile App Audit

$5K to $8K

1 week

A written assessment of where AI fits in your app, a working proof-of-concept of the top feature, and a board-ready cost model. The fee credits toward a Sprint signed within 30 days, or is refunded if the report is not useful.

Step 2

AI Integration Sprint

$15K to $30K

2 to 4 weeks

One AI feature shipped into your existing app, as a thin integration layer with fallbacks, caching, and guardrails. Your architecture stays yours.

Step 3

AI-Native Mobile MVP

$25K to $75K

6 to 12 weeks

A production-ready AI-native mobile MVP when the answer is a new product rather than a feature. Fixed price, fixed milestones, both stores.

See full pricing

FAQ

Healthcare questions, answered straight.

Can you sign a BAA?

Yes. As your development partner we will sign a Business Associate Agreement, and we design the data flow so the list of parties that ever touch PHI stays as short as possible. Where PHI reaches a model provider, that provider needs to be under a BAA too, which is the next question.

How is PHI handled when a prompt goes to an LLM, and which providers will contract for it?

This is the part most teams get wrong. There are two safe patterns: de-identify the data before the call so no PHI leaves your boundary, or send it only to a provider that signs a BAA and runs a HIPAA-eligible, no-training endpoint. Azure OpenAI, AWS Bedrock (which serves Claude and others), and Google Vertex AI all offer BAAs and HIPAA-eligible configurations, and OpenAI signs BAAs for its API under specific terms. We default to a BAA-covered endpoint with training disabled, and we log every field that leaves the app so the data path is auditable.

Have you passed a hospital or payer security review?

Not as Inseed, and we will not claim otherwise. The honest version: our founder built VocalMD, a HIPAA telehealth app that has run in production across 200+ hospitals for over two years with EPIC FHIR and end-to-end encryption, so we have shipped into environments that carry that bar. We have not completed a payer's formal security questionnaire (SOC 2 or HITRUST) as a vendor. If your review requires one, we will tell you plainly what we can and cannot attest to before you sign anything.

Can you work with Epic, Cerner, or FHIR?

Epic via FHIR, yes: our founder built exactly that in VocalMD, where patients log in with hospital credentials and the app pulls their medications, conditions, and lab reports. We have not integrated Cerner directly, but both speak FHIR, so the integration pattern carries over. If your EHR exposes FHIR, we can read from it.

Can you add AI to an app already in a regulated workflow?

Yes, that is the whole point of how we work. The AI feature sits beside the regulated workflow as a separate layer, not inside the validated path, with a human-review checkpoint before anything writes to a record. The parts of your app that already passed review do not change, so you are adding a feature, not re-opening an audit.

Is React Native robust enough for a clinical product?

Yes, and we can point at live evidence. VocalMD has run in production across 200+ hospitals for over two years. Nightingale Labs renders dense clinical charts on iOS, Android, and web from one React Native codebase. Hazel Health runs real-time video visits for student telehealth. Beyond our work, Doctolib, Hinge Health, and Oscar run React Native in production healthcare.

Ready to add AI to your healthcare app?

30 minutes, free. We will look at your app and tell you honestly where AI fits, what it costs, and what we would build first. Shuhel will be on the call.

Book a 30-minute call