AI-First Healthcare Engineering. Idea to Clinical Pilot in 12 Weeks.
We turn clinical AI ideas into HIPAA-compliant, EHR-integrated production systems — from a 6-week prototype to a fully deployed system your clinicians actually use. Built by the same team that has shipped 785+ healthcare implementations since 2013.
Healthcare AI prototypes from $45K · Production AI from $120K · EHR AI integrations from $140K
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An AI healthcare development company builds HIPAA-compliant artificial intelligence applications for hospitals, clinics, payers, and digital health startups — covering generative AI, clinical copilots, ambient documentation, predictive analytics, and EHR-integrated AI features. Taction Software is an AI-first healthcare engineering company specializing in prototype-to-production delivery, with 785+ healthcare implementations since 2013 and integrations across Epic, Cerner, Athena, and Allscripts.
Most AI development shops in 2026 can spin up a generative AI prototype in a week. Almost none of them can ship one inside a HIPAA-audited, EHR-integrated, BAA-papered production system. That gap is where most healthcare AI projects fail — not at the model, but at the compliance, integration, and clinical-workflow layer wrapped around it.
Generic AI shops typically retrofit compliance after the build. They sign no BAAs with foundation-model providers, leak PHI into prompt logs, and have never deployed inside a hospital firewall. Their prototypes look impressive in demos and die before reaching a single clinician.
Healthcare AI engineering is the opposite discipline: HIPAA-by-design from day one, BAAs signed with model providers before a single token of PHI enters a prompt, audit logging on every inference, and SMART-on-FHIR integration so the AI feature lives inside Epic or Cerner rather than alongside them. This is what we mean by AI-first healthcare engineering — and it’s the foundation underneath every service on this page.
The work itself sits inside the broader practice we describe on our healthcare software development company page, but the AI cluster is a distinct discipline with its own toolchain, compliance requirements, and delivery timeline.
We deliver eight focused AI services — five core build services and three cross-cutting capabilities that show up inside almost every engagement.
We pressure-test your clinical AI use case against real (de-identified or synthetic) healthcare data, build a working concept, and deliver a written go/no-go for production. Three named tiers: $45K Discovery Sprint (4 weeks), $95K MVP Sprint (8 weeks), $145K Pilot-Ready Sprint (12 weeks, EHR-integrated). The MVP and Pilot-Ready tiers are where most buyers should aim — a prototype proves an idea, but an MVP earns clinical pilot data and a Series A round.
The first is healthcare delivery depth measured in years, not quarters. Our team has been engineering healthcare software since 2013 — over a decade of HIPAA-by-design SDLC, FDA pathway navigation, and Epic/Cerner/Athena/Allscripts integration. A generalist AI shop with 18 months of healthcare exposure cannot replicate that pattern recognition.
The second is named EHR integration experience. The shops that get past sales but fail in delivery almost always lack hands-on FHIR R4 and SMART-on-FHIR experience. We’ve shipped 200+ documented EHR integrations using HL7 v2, FHIR R4, SMART on FHIR, and Mirth Connect.
The third is BAA capability with foundation-model providers. Most AI shops in 2026 cannot sign a BAA at all, let alone a chained BAA covering both the hospital and the cloud LLM provider. We have BAA paperwork in place with hospitals, AWS Bedrock, Azure OpenAI, and Anthropic — and we publish our PHI flow documentation as part of every engagement.
The fourth is HIPAA findings as a verifiable claim. Zero HIPAA findings on shipped software, across 785+ healthcare implementations. Not a marketing line — a verifiable claim that any auditor can confirm.
The fifth is clinical workflow literacy. Our engineers have worked alongside clinicians long enough to know what a SOAP note actually contains, why ED triage is fundamentally different from outpatient triage, and what a discharge summary needs to satisfy HRRP penalty rules. Generic AI shops build features that look right and fail in production because they were never grounded in real clinical workflow.
The sixth is multi-region delivery with US engineering oversight. We deliver from US offices in Chicago, Cheyenne, Austin, and Sacramento, with engineering capacity in Noida, India — cost-efficient without offshore-shop quality risk. Every engagement is scoped, architected, and reviewed by US-based senior engineers.
We don’t sell prototypes that die in slide decks. We sell the route from healthcare AI idea to clinically usable MVP. Each phase commits only after the previous phase has earned the buyer’s confidence.
Weeks 1–4: Discovery Sprint ($45K). We pressure-test your AI use case against real clinical data. By week 3 you have enough confidence to commit (or not commit) to MVP delivery. 100% satisfaction guarantee on this tier; full refund if you’re not satisfied.
Weeks 5–8: MVP Sprint ($95K). We build a clinically usable MVP — deployable code, working AI features, evaluation harness, audit logging. AI-driven engineering tooling compresses what generalist shops deliver in 6 months.
Weeks 9–12: Pilot-Ready Sprint ($145K total package). We deliver the MVP integrated with one EHR — Epic, Cerner, Athena, or Allscripts — ready for a real clinical pilot. SMART on FHIR app, BAA paperwork, monitoring dashboards. Take it to your CMIO. The deeper mechanics of this delivery model are documented in our healthcare MVP development playbook.
The reason this 12-week timeline works — when generalist shops still quote 6 months for the same scope — is that we’ve built the AI-augmented healthcare SDLC as a reusable foundation. PHI never leaves controlled environments. Eval harnesses include clinical accuracy metrics, not just task accuracy. Prompt-injection defenses are designed in. BAA paperwork is signed with the AI providers used in development. This is the bundle no generalist shop offers.
The AI healthcare market in 2026 is no longer about whether to deploy AI — it’s about which use cases deliver measurable ROI fast enough to justify the engineering spend. These are the categories where we’ve shipped the most production code.
Generative AI for clinical content. Clinical note generation, discharge summary drafting, patient-facing education content, prior auth letter drafting. The deeper landscape, including 50 use cases already shipping, is mapped in our generative AI healthcare applications deep dive.
Healthcare AI chatbots. Symptom triage, appointment scheduling, insurance Q&A, post-discharge check-ins. These are the highest-volume entry-level AI deployments and the fastest path to ROI. Our complete build playbook is at healthcare AI chatbot development.
Hospital workflow automation. AI scheduling, clinical decision support, revenue-cycle automation, prior authorization. Hospitals embedding AI across these workflows see measurable cost and outcome improvements; the operational reality is documented in our AI automation in hospitals field guide.
Medical practice administration. AI medical coding (97% time reduction, $1.14M+ annual revenue recovery per practice in our deployments), insurance verification automation, claims optimization. The full ROI math is broken down in our medical practice automation guide.
FHIR-driven AI. Every modern healthcare AI feature lives or dies on its FHIR integration. Our FHIR API development in healthcare breakdown covers the technical foundation.
For buyers actively evaluating partners, our comparative analysis of the top AI healthcare software development companies lays out how to think about the decision.
Every healthcare AI engagement at Taction includes BAAs with the hospital or healthcare buyer, BAAs with the foundation-model provider used in the build (Azure OpenAI, AWS Bedrock, Anthropic via direct contract, GCP Vertex AI), and BAAs with all third-party services touching PHI — vector databases, audit logging providers, observability tools.
Beyond BAA paperwork, we deliver written PHI flow documentation: a diagram of every place PHI enters or exits a model context, signed off by the client’s compliance officer. We enforce encryption at rest (AES-256) and in transit (TLS 1.2+) on every PHI-touching surface, role-based access controls and audit logging on every model output, prompt-injection defenses including input sanitization and output validation, and retention and deletion policies aligned with the client’s HIPAA Notice of Privacy Practices.
This is not a sales claim. It’s the documented compliance baseline of every engagement, and we publish the framework as part of the contracting process.
We start every engagement with a 30-minute AI Discovery Call. Bring your use case, your data sensitivity profile, and your target EHR. We’ll respond with a preliminary scope, the right tier for your timeline, and a written go/no-go on whether the use case is feasible inside your compliance constraints.
HIPAA · SOC 2 (in progress) · Epic · Cerner
Yes. Our SDLC is HIPAA-by-design — BAAs with all model providers, PHI redaction at inference where required, audit logging on every model output, and 785+ healthcare implementations with zero HIPAA findings. The standard pattern is to use BAA-eligible cloud LLMs (Azure OpenAI, AWS Bedrock) or, for clients that can’t use cloud LLMs at all, on-prem Llama 3, Mistral, or Phi-3 deployments.
We use BAA-eligible deployments of these models — Azure OpenAI for GPT models, AWS Bedrock for Claude and Llama, and direct contractual arrangements with Anthropic where the engagement scope justifies it. Standard public-tier OpenAI and Anthropic APIs do not sign BAAs; the work has to flow through cloud providers that do.
Layered defenses: retrieval-augmented generation grounded in the client’s clinical knowledge base, citation requirements on every model response, output-validation classifiers, clinical-accuracy evaluation harnesses run on every release, and human-in-the-loop override UX on every clinical-decision-impacting feature. Hallucinations are not eliminated — they’re contained and audited.
Yes. We deploy Llama 3, Mistral, and Phi-3 on hospital infrastructure with appropriate hardware sizing, often air-gapped from public networks. Pricing starts at $130K for deployment; full deployments with fine-tuning and hardware procurement run $220K+.
Four weeks for the Discovery Sprint, eight weeks for an MVP, twelve weeks for an EHR-integrated pilot-ready system. Generalist shops typically quote six months for the same scope. The compression comes from AI-augmented engineering tooling plus reusable healthcare-specific foundations.
Two reasons. First, AI-augmented engineering tooling collapses 3–6x of the time-from-idea-to-working-code on the categories of work most healthcare AI MVPs consist of — form-driven UIs, FHIR integrations, simple LLM features, dashboards, CRUD workflows. Second, we’ve built reusable healthcare-specific foundations — BAA paperwork templates, FHIR integration libraries, eval harnesses with clinical accuracy metrics, prompt-injection defenses. Generalist shops rebuild this from scratch every engagement.
We’re a healthcare engineering company that does AI, not an AI company that’s trying healthcare. 13+ years of healthcare-only delivery, 785+ implementations, 200+ documented EHR integrations, zero HIPAA findings, BAAs with both hospitals and model providers. Generalist AI shops are typically less than two years deep in healthcare specifically and cannot sign the BAAs that healthcare buyers need.
Productized fixed-price tiers on prototyping ($45K / $95K / $145K). “Starting at $X” anchors on production, ambient documentation, copilots, predictive, EHR integration, and on-prem LLMs. For custom estimates, the cost calculator gives a usable starting range in under three minutes.
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