The rigor of healthtech,
at AI’s pace.
We embed engineers trained in the craft that keeps AI-accelerated engineering safe, compliant, and built to last.
- Spec-driven development
- Test-driven development
- Continuous delivery
Companies that
trust us
trust us
AI is only as good as the team using it.
AI compounds whatever a team already brings. We spent years building the discipline it now amplifies.
Spec before
code.
The thinking happens before a line is written, so the build stays on target and rework stays small.
Spec before
code.
The thinking happens before a line is written, so the build stays on target and rework stays small.
Clean code,
by habit.
We refactor as we go, so the codebase stays fast to work in long after we hand it over.
AI in production. Here's what it took.
AI products we shipped for US health tech, built with AI and the judgment to know when it’s wrong.
Ideation to live MVP in three months.
A retrieval platform with no-code agentic workflows, wired to 20+ language models. Now live at Virginia Tech and Providence College, with manual academic work cut in half.
90% review time cut. Across 800,000+ minutes a month.
The AI workflow that reviews Medicare sales calls for compliance, flags the risk, and leaves an audit trail a reviewer can trust, at a volume a manual team never could.
Compliant code, at AI speed.
Anthara is the runtime layer we built so AI-generated code stays inside policy as it’s written. We run it on our own regulated work, and now it ships as a product.
AI in production. Here's what it took.
AI products we shipped for US health tech, built with AI and the judgment to know when it’s wrong.
Ideation to live MVP in three months.
A retrieval platform with no-code agentic workflows, wired to 20+ language models. Now live at Virginia Tech and Providence College, with manual academic work cut in half.
90% review time cut. Across 800,000+ minutes a month.
The AI workflow that reviews Medicare sales calls for compliance, flags the risk, and leaves an audit trail a reviewer can trust, at a volume a manual team never could.
Compliant code, at AI speed.
The AI workflow that reviews Medicare sales calls for compliance, flags the risk, and leaves an audit trail a reviewer can trust, at a volume a manual team never could.
AI work that compounds across your engineering organization.
Adoption, agent experience, or feature work. The engineering craft underneath is the same.
AI Enablement
The infrastructure that makes AI adoption safe, measurable, and consistent across your engineering organization. Productivity that holds past the first quarter.
AI Enablement
- Guardrails and governance
- Cost and control
- Shared tooling
- SDLC workflow agents
AI Modernization
The artifacts that make your codebase legible to AI agents, so they read your repo like senior engineers from the first prompt.
AI Modernization
- CLAUDE.md and AGENTS.md setup
- Architecture Decision Records (ADRs)
- Codebase indexing and semantic search
- Code health and hotspot baseline
AI Product Engineering
AI features built into your product by senior engineers who use AI well. Shipped to your standards, paired and tested.
AI Product Engineering
- AI agents and copilots
- RAG and retrieval
- MCP and tool use
- EHR integration and data
Built for the realities of US health tech.
Three health tech verticals where regulation is the design starting point
PAYER TECH
Claims, eligibility, member AI.
Build and modernize systems that drive productivity, and are defensible across CMS and Medicare audit cycles.
PROVIDER TECH
Clinical workflows. EHR-native.
Engineer HIPAA compliant products that work with Epic, Cerner, and Athena rather than around them, so clinicians get tools that fit how they already work.
BEHAVIORAL HEALTH TECH
Therapist matching. Member engagement.
Build for the most sensitive data in healthcare, with privacy and consent engineered in, so matching and engagement scale without exposing patients.
- SPOTLIGHT · OPEN-SOURCE PLUGIN
Our engineers ship faster because our Claude plugin codifies our craft.
Bee is the Claude Code plugin we built for ourselves and open sourced. It knows our practices, enforces our standards from discovery through review, and ships with every engineer on every client engagement. It’s also the template for what we build for your team. When we set up your AI infrastructure, we draw from what we’ve already lived.
- Spec-driven
- Open source
- Built for Claude Code
Your foundational model may be replaceable.
Your forward-deployed engineers aren’t.
Foundation models are commoditizing. Defensibility shifts to forward-deployed engineers embedded inside your team. That bench is the moat.
OPTION 01 · ACQUIRE
$4 billion
Provider directory, pre-procedure engagement, and ambient documentation, EHR-native against Epic, Cerner, and Athena.
OUT OF REACH
OPTION 02 · BUILD
18 months
Hire, vet, train, deploy, and maybe ship in eighteen months. By then your customers have moved to a platform that already has the bench.
TOO SLOW
OPTION 03 · FOUNDRY
Build. Operate. Transfer.
We build a captive of eight to twelve FDE-grade engineers, branded as yours, trained on your platform, embedded with your team, and transferred to you in 24 to 36 months.
THE THIRD PATH
Partner with Incubyte to build your FDE bench, branded as yours and transferred when it’s ready.
The team behind the rigor.
Founded 2020 by Sapan and Rushali.
140+ engineers. Fully remote. Founder-led.
140+ engineers. Fully remote. Founder-led.
Tell us what you’re building.
What you’re building and what’s getting in the way. A founder reads every message and replies within two business days.
Tell us what you’re building.
What you’re building and what’s getting in the way. A founder reads every message and replies within two business days.
What we have written
Three pieces from the engineering team on shipping AI inside US health tech.
Questions teams ask before the first call.
What does Incubyte do?
Incubyte is a custom software engineering company for US health tech. We build and ship your product using AI, with engineering craft like test-driven development, pair programming, and spec-driven development. We also build AI features such as agents and retrieval-augmented generation directly into products.
What does building with AI actually mean here?
AI runs through our whole build, not just autocomplete. We plan with it, generate against specs and tests, and review its output the way we review a person’s. The craft decides what good looks like. AI helps us get there faster.
What is spec-driven development?
Spec-driven development means writing a clear, testable specification before any code is generated, then building and reviewing against it. It is the core of how we build with AI: the spec is the contract, so AI-generated code stays on target instead of drifting. Our open-source plugin Bee runs this workflow on every feature.
How do you do test-driven development (TDD) with AI?
We write a failing test first, then have AI generate the minimum code to pass it, then refactor. The test defines done, so the AI has a precise target and the output is verified, not just plausible. TDD with AI is what keeps speed from turning into silent bugs.
What makes Incubyte different from other firms now using AI?
We practiced craft first. Test-driven development, pairing, and clean code were how we worked for years before AI arrived, so AI amplifies real discipline instead of hiding its absence. We also built that craft into Bee, our open-source plugin, so you can read exactly how we work.
What is Bee?
Bee is Incubyte’s open-source, spec-driven development plugin for Claude Code. It puts a spec before the code, runs real test-first cycles, carries context across sessions, and reviews code against your standards and your git history. Every Incubyte engineer builds through it.
Is Bee really open source, and can we use it?
Yes. Bee is public and free to read, run, and fork. Point it at your own repository, or watch the walkthrough that takes a feature from first spec to final review. It is the clearest way to see how we build before you talk to us.
How fast can you ship an MVP?
Most products go from an empty repository to a live MVP in under 90 days, and several have shipped in under a month. We build in working slices from the first week, so you see real software early instead of waiting for one large release.
How do you keep quality high while moving that fast?
Quality is built into the workflow. We work spec-first, test-driven, and in pairs, with refactoring as a standing habit through Bee. The speed comes from code that does not break, so you are not paying it back later in bugs and rework.
How is this different from staff augmentation?
Staff augmentation places individual contractors on your rate card. We embed a small pod that owns an outcome and builds through Bee. You get shipped software and a team accountable for results, not seats filled and hours logged.
Do you build HIPAA-compliant software and AI?
Yes. We build HIPAA-compliant software and AI for US health tech, with PHI handling, access controls, and audit trails designed in from the start. We are SOC 2 Type II and ISO 27001, and we sign a BAA on every engagement that touches PHI.
How do you handle HIPAA, SOC 2, and PHI day to day?
Compliance lives inside the build, not at the end of it. Spec-first development, paired review, and version control mean every change traces to a person and a reason, and PHI handling is designed into the architecture rather than reviewed after the fact.
Can you integrate AI with Epic, Cerner, or Athena?
Yes. We build EHR-native, integrating with Epic, Cerner, Athenahealth, and other systems through the standard interfaces each supports, including HL7 and FHIR. The AI is designed around the clinical workflow rather than bolted on beside it.
What is retrieval-augmented generation (RAG), and do you build it?
Retrieval-augmented generation grounds an AI model in your own data so its answers are accurate and traceable instead of generic. We build production RAG systems for health tech, with the retrieval, evaluation, and guardrails that make answers defensible. Rolai, which we built end to end, is a RAG platform wired to more than twenty models.
How do you build AI agents for health tech products?
We build AI agents that take real actions inside your product, with the guardrails, evaluation, and audit logging regulated environments require. Agents are designed against a spec, tested, and reviewed like any other critical system, so they behave predictably in front of patients and auditors.
What kinds of products have you built?
AI products and platforms, including retrieval systems, agents, and model-backed features, plus the product and platform engineering underneath them. Recent work includes Rolai, an enterprise AI platform, and a Medicare compliance system that reviews call audio at scale.
What parts of US health tech do you work in?
Payer technology, provider and EHR-native systems, and behavioral health. We build the products and the AI on top of them with the accuracy, workflows, and compliance each of these areas demands.
What is forward-deployed engineering (FDE)?
Forward-deployed engineering means embedding engineers inside a customer’s product and reality, shipping against their real environment instead of from a distance. For AI platforms, that embedded bench is the moat. Through the FDE Foundry, we build that bench for you and transfer it to your team.
What is the FDE Foundry?
The FDE Foundry is our build, operate, and transfer model for founders. We stand up a forward-deployed engineering bench inside your product, run it until it is humming, then transfer it to your team. You own the capability, not rent it forever.
How does pricing and engagement length work?
We price to the outcome and the pod, not to hours. A first outcome usually takes weeks, not quarters, and larger platform or Foundry engagements run longer. We walk you through scope, shape, and price on the first call.
Do you work with early-stage startups or funded companies?
Both. We work with funded health tech startups building a first product and with established companies modernizing or adding AI. Engagements are scoped to the outcome and the pod, so the model fits a seed-stage MVP or a platform team.
Who founded Incubyte, and where is the team?
Incubyte was founded by Sapan and Rushali, who still lead the engineering culture. The team is fully remote and builds in the open, sharing tools like Bee and writing about what we learn.