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AI PRODUCT ENGINEERING

AI features for health tech.

The agent, the RAG layer, the EHR integration, and everything underneath. Built with the discipline that takes AI past the demo.
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See our work

HealthyAI

90 days idea to production.

Kyruus

Enterprise RAG live in two weeks.

Rolai

Legacy to AI-enabled.

THE DIFFERENCE

Anyone can build AI.
Few take it to production.

Anyone can build AI. Few take it to production.

Specs before prompts.

Intentionally defined behavior, acceptance criteria, constraints, technical decisions, and guidance for AI agents, before the first line of code.

Evals before Releases.

Without a measurable definition of correct, the only test AI has to pass is ‘looks right.’ Behavior evals catch broken AI before merge.

Engineers who keep pace.

Many of us hold Claude certifications. We have shipped generative AI since 2023. The model layer moves fast and so do we.

What we build.

BUILD FROM SCRATCH

Your AI idea to production

Bring us the problem. We own the PDLC end to end, from design to deployment and push back when the idea needs sharpening.

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Your AI idea to production

Bring us the problem. We own the PDLC end to end, from design to deployment and push back when the idea needs sharpening.

  • Full stack, AI and non-AI, owned end to end
  • Healthcare compliance handled: BAAs, HIPAA, SOC 2, audit-ready evidence
  • Built around your end users, clinicians to payers
ADD TO WHAT YOU HAVE

AI in your existing product

Your customers get AI without waiting for a rebuild, and without destabilizing what’s working.
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AI in your existing product

Your customers get AI without waiting for a rebuild, and without destabilizing what’s working.
  • New AI-first modules, from full builds to lightweight browser extensions
  • Workflows reworked so AI does the heavy lifting, not the user
  • Changes shipped safely, so what's audited or live keeps working
AUTOMATE THE BUSYWORK

Agentic automations

The repetitive, high-volume work in healthcare ops is where agents earn their keep, humans in the loop.
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Agentic automations

The repetitive, high-volume work in healthcare ops is where agents earn their keep, humans in the loop.
  • Revenue cycle: prior auth, denials, eligibility and benefits
  • Clinical and documentation: intake, triage, coding, CDI
  • Every agent ships with oversight, audit trails, escalation paths
BUILT FOR PRODUCTION

We know what it takes to make AI features work.

A demo that works once isn’t a feature your users can trust. The gap is engineering, and in healthcare it’s mostly proving the thing is right.

Accuracy and safety

Evals, guardrails, and PHI-safe context, so it's right and stays right.

RAG

Answers grounded in your data, with traceable sources for every response.

Integrations

FHIR, HL7, EHRs, and the claims systems your product runs alongside.

MCP and the agent stack

Safe, governed access for agents, with observability and cost control.

BUILT FOR PRODUCTION

We know what it takes to make AI features work.

A demo that works once isn’t a feature your users can trust. The gap is engineering, and in healthcare it’s mostly proving the thing is right.

Accuracy and safety

Evals, guardrails, and PHI-safe context, so it's right and stays right.

RAG

Answers grounded in your data, with traceable sources for every response.

Integrations

FHIR, HL7, EHRs, and the claims systems your product runs alongside.

MCP and the agent stack

Safe, governed access for agents, with observability and cost control.

AI we have shipped.
In production, at scale.

See all our work
Medicare compliance AI

90%

reduction in Medicare compliance review time

Across 800,000+ minutes of call audio every month. Ninety days from idea to production. Four-engineer pod. Every Medicare Advantage sales call audited against CMS rules with audit-grade trace, on Ruby on Rails, React, AWS, Deepgram, and Anthropic.
Read the HealthyAI engagement
Enterprise RAG

2 weeks

to enterprise RAG live

10,000+ provider reviews across five enterprise customers. Two-engineer pod. Plug-and-play deployment inside Kyruus’s own HIPAA-compliant AWS. Reusable across every Kyruus service after install.
Read the Kyruus IAP engagement
AI workbench for healthcar

3 universities

live, faculty workload halved

Live at Providence College and Virginia Tech, including their nursing and healthcare programs. Cross-functional pod of fourteen. Twenty-plus model providers, RAG, agentic workflows, SOC 2 from day one.
Read the Rolai engagement
Medicare compliance AI

90%

reduction in Medicare compliance review time

Across 800,000+ minutes of call audio every month. Ninety days from idea to production. Four-engineer pod. Every Medicare Advantage sales call audited against CMS rules with audit-grade trace, on Ruby on Rails, React, AWS, Deepgram, and Anthropic.
Read the HealthyAI engagement
Enterprise RAG

2 weeks

to enterprise RAG live

10,000+ provider reviews across five enterprise customers. Two-engineer pod. Plug-and-play deployment inside Kyruus’s own HIPAA-compliant AWS. Reusable across every Kyruus service after install.
Read the Kyruus IAP engagement
AI workbench for healthcare

3 universities

live, faculty workload halved

Live at Providence College and Virginia Tech, including their nursing and healthcare programs. Cross-functional pod of fourteen. Twenty-plus model providers, RAG, agentic workflows, SOC 2 from day one.
Read the Rolai engagement

How we work.

What every Incubyte engagement carries.
01

Embedded in your team.

Engineers join your standups, your repo, your code review. They work as part of your team, and bring our craft practices with them.
02

Accelerated with AI.

AI-accelerated software craft, not AI on autopilot. Specs, evals, audit traces, and reviewer ladders that keep speed honest.
03

Yours from day one.

Code in your repo. CI in your pipeline. IP on your infrastructure. Every engagement leaves your team handoff-ready.
Same engineers, same discipline, throughout the engagement.
TALK TO US

Have a product idea in mind?

Tell us what you’re aiming to ship.
Talk to us
Field notes from the engineering team.
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Questions we get from
health tech engineering leaders.

Questions we get from health tech engineering leaders.

How do you handle PHI and PII?
PHI and PII never leave your infrastructure. Everything we build runs inside your HIPAA-compliant private cloud, every AI action lands in an audit trail, and redaction is applied where it belongs. Doctors, nurses, and reviewers keep the PHI visibility their work depends on.
We set acceptance criteria before the first prompt, run behavior evals in the pipeline, and put reviewer ladders on every high-stakes action. Agent-generated code clears the same bar your best engineer would set.
Yes. Rolai shipped SOC 2 from day one, HealthyAI runs inside Medicare Advantage audit boundaries, and Kyruus IAP runs inside the customer’s HIPAA-compliant AWS.
We treat injection as a contract to build against, not an edge case. Tools are allowlisted, action layers are isolated from user input, and high-stakes actions route through reviewer approval. The audit trail makes any attempt visible.
Most engagements run 60 to 180 days. HealthyAI went idea to production in 90, and Kyruus IAP went live in under two weeks because the spec was sharp.
Pods are scoped to outcomes, with two to fourteen engineers depending on the lane. We give you a pricing range on a 30-minute call.
Same engineers, start to finish. The pod that opens your engagement is the pod that ships it.
We are India-headquartered, with working hours that overlap US business hours every day. We are live in your standups, your code reviews, and your release windows.
Almost certainly. We have shipped AI across mental health, revenue cycle management, EHR integration, payer admin, behavioral health intake, clinical documentation, and more. If your scenario is not on that list, send it over.
It is our build practice for US health tech: Custom AI Products, Agents, Conversational AI, RAG, MCP, and Integration. Most engagements draw on whichever of those the build at hand needs.
Yes. We are model and vendor agnostic, and we have shipped on Anthropic, OpenAI, AWS Bedrock, in-house models, and combinations of them.
We build document, structured, hybrid, and private-cloud RAG, with retrieval evaluations baked in. IAP is one path to faster RAG, not the only one.
This is the lane where most AI features stall. We have shipped into Epic, Athena, eCW, and Oracle Health (Cerner), and the claims clearinghouses behind them: HL7 v2 and FHIR R4 in both directions, 837 and 835 on the claims side, with identity and consent boundaries built clean from day one.
Yes. MCP lets us expose your existing APIs to Claude, ChatGPT, and the assistants your team uses, without rewriting the legacy stack. AI gets built in alongside what’s already running.
Yes. Embedded pods slot in next to in-house teams cleanly.
The acceptance criteria we set in week one become the scoreboard, and production metrics stay in the open from day one. HealthyAI cut review time 90%, Kyruus IAP had enterprise RAG live in two weeks, and Rolai runs at three universities with faculty workload halved.
You get a working spec, an acceptance criteria set, and a mapped integration boundary. Engineers are in your repo by day two, and the first commit lands in week one.
Yes. Kyruus IAP is a multi-tenant deployment pattern by design, reusable across every Kyruus customer after install.
Load more
Load less
How do you handle PHI and PII?
Almost certainly. We have shipped AI across mental health, revenue cycle management, EHR integration, payer admin, behavioral health intake, clinical documentation, and more. If your scenario is not on that list, send it over.
We set acceptance criteria before the first prompt, run behavior evals in the pipeline, and put reviewer ladders on every high-stakes action. Agent-generated code clears the same bar your best engineer would set.
Yes. Rolai shipped SOC 2 from day one, HealthyAI runs inside Medicare Advantage audit boundaries, and Kyruus IAP runs inside the customer’s HIPAA-compliant AWS.
We treat injection as a contract to build against, not an edge case. Tools are allowlisted, action layers are isolated from user input, and high-stakes actions route through reviewer approval. The audit trail makes any attempt visible.
How long does an AI feature engagement take?
Most engagements run 60 to 180 days. HealthyAI went idea to production in 90, and Kyruus IAP went live in under two weeks because the spec was sharp.
Pods are scoped to outcomes, with two to fourteen engineers depending on the lane. We give you a pricing range on a 30-minute call.
Same engineers, start to finish. The pod that opens your engagement is the pod that ships it.
We are India-headquartered, with working hours that overlap US business hours every day. We are live in your standups, your code reviews, and your release windows.
Can you build an AI-native application for my scenario?
Almost certainly. We have shipped AI across mental health, revenue cycle management, EHR integration, payer admin, behavioral health intake, clinical documentation, and more. If your scenario is not on that list, send it over.
It is our build practice for US health tech: Custom AI Products, Agents, Conversational AI, RAG, MCP, and Integration. Most engagements draw on whichever of those the build at hand needs.
Yes. We are model and vendor agnostic, and we have shipped on Anthropic, OpenAI, AWS Bedrock, in-house models, and combinations of them.
We build document, structured, hybrid, and private-cloud RAG, with retrieval evaluations baked in. IAP is one path to faster RAG, not the only one.
How do you handle integration with our EHR or claims platform?
This is the lane where most AI features stall. We have shipped into Epic, Athena, eCW, and Oracle Health (Cerner), and the claims clearinghouses behind them: HL7 v2 and FHIR R4 in both directions, 837 and 835 on the claims side, with identity and consent boundaries built clean from day one.
Yes. MCP lets us expose your existing APIs to Claude, ChatGPT, and the assistants your team uses, without rewriting the legacy stack. AI gets built in alongside what’s already running.
Yes. Embedded pods slot in next to in-house teams cleanly.
How do we measure success on an AI feature engagement?
The acceptance criteria we set in week one become the scoreboard, and production metrics stay in the open from day one. HealthyAI cut review time 90%, Kyruus IAP had enterprise RAG live in two weeks, and Rolai runs at three universities with faculty workload halved.
You get a working spec, an acceptance criteria set, and a mapped integration boundary. Engineers are in your repo by day two, and the first commit lands in week one.
Yes. Kyruus IAP is a multi-tenant deployment pattern by design, reusable across every Kyruus customer after install.