Patient & clinician workflows
Products shaped around the real moments where patients, care teams, and operators actually work — designed before code is written.
8-Week Clinical AI System Build
Build healthcare products that are ready for the real world
We design, build, and scale healthcare products, workflow systems, and AI-enabled tools for clinical, operational, and patient-facing environments. From product strategy to launch-ready systems in weeks, not months.
50+ Healthcare Products DeliveredEHR Integrations Built InHIPAA-Aware FoundationsAI + Workflow Automation
Trusted by global innovators
Built for healthcare environments where products have to work in the real world
Healthcare products have to work inside real workflows, with real data, under real compliance pressure. That's where Agnotic operates.
50+
Healthcare products and systems delivered
8 Weeks
Idea to launch-ready clinical AI product
100%
Compliance-aware architecture from Week 1
Why healthcare AI is different
Most teams can prototype AI fast now. Healthcare is where products get hard.
AI-generated prototypes, patient-facing apps, copilots, and direct model integrations are easier to build than ever. What's not easier: passing compliance review, keeping outputs stable under real usage, integrating into clinical workflow, protecting PHI, and surviving cost-and-latency scaling.
We treat product strategy, integrations, automation, and AI as one compliance-ready foundation — not separate workstreams duct-taped together at launch.
What we build
Healthcare products, workflow systems, and AI-enabled tools
Patient-facing products, clinician tools, workflow systems, internal operational software, automation, and AI-enabled healthcare experiences.
- Patient-facing products — intake, navigation, engagement, education, and workflow experiences
- Health & wellness applications — consumer health, prevention, engagement, longitudinal health
- Clinician tools — documentation, decision support, workflow acceleration, EHR-connected surfaces
- Internal workflow systems — care coordination, handoffs, escalations, triage, team workflows
- Healthcare automation — intake, follow-up, routing, and operational process automation
- AI-enabled assistants — copilots that support product workflows, not isolated demos
- EHR and data integrations — connected products grounded in source systems and compliance realities
Built for real-world use
Five integrated layers, one compliance-ready foundation
Product strategy, integrations, automation, and AI are built into one compliance-ready foundation — not stitched together after launch.
Product experience
Interfaces, touchpoints, and workflow systems built to be usable in real clinical and operational settings — not just in demos.
Automation and AI assistance
AI-enabled tools and workflow automation used where they improve the product, not where they add noise or risk.
EHR and data integration
Connected products grounded in source-of-truth systems, longitudinal data, and operational context — FHIR-first, HL7 where required.
Compliance foundation
HIPAA-aware architecture, PHI boundaries, auditability, and technical foundations ready for hospital and payer scrutiny.
Cost & performance optimisation
Latency, throughput, and inference cost engineered across every layer — so AI doesn't scale into a cost or compliance crisis.
Workflow-native AI assistants
Copilots embedded into product workflow with clinician-in-loop, output validation, and audit-logged usage.
Operational automation
Repetitive work across intake, follow-up, routing, and operations automated with FHIR-triggered events and human-in-loop gates.
Reference architecture
How we run AI against PHI safely
Three architectures — pick per build based on regulatory posture, model choice, and data residency.
Pattern A · Tenant-isolated model in your VPC
- Model hosted inside your cloud account, no external egress of PHI
- Strongest control, highest ops overhead
- Fits regulated providers and enterprise health systems
Pattern B · BAA-covered managed AI service
- Azure OpenAI, Bedrock, or Vertex under signed BAA
- PHI crosses to vendor under contract; no training on customer data
- Fits most digital health startups and mid-market providers
Pattern C · De-identified upstream, re-identify downstream
- Safe Harbor or Expert Determination de-identification before model
- Re-attach identifiers inside tenant boundary
- Fits analytics, research, and high-volume cost-sensitive use cases
Risk classification
We classify every AI feature before we build it
Adapted from the IMDRF SaMD framework. The classification drives architecture, validation, and regulatory posture.
01
Class I — Informational
Example: Documentation summarisation, admin triage
Approach: Product-grade QA, privacy review, human-in-loop by default.
02
Class II — Drive non-critical action
Example: Care gap reminders, risk score suggestions
Approach: Clinician review sampling, explainability, drift monitoring.
03
Class III — Drive clinical action
Example: Imaging triage, acute alert models
Approach: Formal validation file, subgroup fairness, prospective study data.
04
Class IV — Diagnose / treat
Example: Standalone diagnostic AI
Approach: SaMD QMS, regulatory submission pathway, change-controlled releases.
Prototypes vs real-world products
What teams build fast vs what real-world use actually demands
Prototypes are easier than ever. Real healthcare products are still hard. Here's the gap we close.
| Capability | What teams build fast | What real-world use demands |
|---|---|---|
| AI-generated prototypes | Working demo in days | Outputs drift under real usage — needs validation, monitoring, and retraining |
| Patient-facing apps | Generic UI shipped to App Store | Compliance review, accessibility, and workflow fit before scale |
| Internal workflow tools | Spreadsheet replacement | Workflow mismatch slows adoption — needs clinician co-design |
| Copilot-style assistants | Wrapped LLM behind a chat box | PHI exposure risk — needs BAA, audit, and clinician-in-loop |
| Direct model integrations | OpenAI key in the codebase | Costs and latency scale badly — needs caching, routing, and cost controls |
You don't rebuild after this. You scale from it. That's the point of the 8-week clinical AI system build.
What we build
Healthcare product categories we ship into
Patient-facing products, clinician tools, workflow systems, internal operational software, automation, and AI-enabled healthcare experiences.
Patient-facing products
Intake, navigation, engagement, education, and workflow experiences patients can actually use under real conditions.
Health & wellness applications
Consumer health, wellness, prevention, engagement, and longitudinal experiences designed for real users and healthcare constraints.
Clinician tools
Documentation, decision support, workflow acceleration, and EHR-connected surfaces built around care delivery.
Internal workflow systems
Operational tools for care coordination, handoffs, escalations, triage, and team workflows that need to run daily.
Healthcare automation
Products that reduce repetitive work across intake, follow-up, routing, and operational processes — without bypassing clinical review.
AI-enabled assistants
Assistants and copilots that support product workflows instead of living as isolated AI demos.
The Bitsol Build System
From strategy to launch-ready system
How we take healthcare products from strategy through design, engineering, compliance-aware architecture, and launch — all in one engagement.
Step 01
01 · Product strategy
Define the product, users, workflows, and highest-risk assumptions before build starts. The biggest cause of healthcare product failure is the wrong product — we de-risk that first.
Step 02
02 · Workflow design
Shape the product around clinical, operational, and patient-facing behaviour that has to work in the real world — not the demo world.
Step 03
03 · Engineering and integrations
Build the product, data flows, automations, and EHR-connected behaviour as one working system. FHIR-first integrations, HL7 where required.
Step 04
04 · Compliance-aware architecture
Design PHI handling, auditability, permissions, and technical foundations so the product is ready for scrutiny from Week 1.
Step 05
05 · Launch-ready delivery
Ship a product foundation that can launch, integrate, and improve under real usage. Not a Phase 1 throwaway — a foundation you scale from.
After launch
Embedded healthcare product pods for teams ready to scale
Launch isn't the end. Edge cases appear, workflow friction slows adoption, costs increase, reliability degrades, compliance pressure grows. The Product Pod handles all of it.
Weekly iteration on real usage
Ship weekly against real usage data — features, fixes, AI calibration, workflow refinements.
Embedded healthcare product specialists
Senior healthcare engineers and product leads who learn your codebase and stay consistent.
Workflow and product ownership
Pods own outcomes, not just tickets — workflow refinement, clinician feedback, adoption metrics.
Integrations, automation, and feature scaling
EHR integration depth, workflow automation expansion, and AI capability scaling — all under one pod.
Systems designed to run daily
This is how your product becomes something people rely on daily — not a quarterly demo.
What usually goes wrong
Common clinical AI failure modes — and how we avoid them
Challenge
Pilot purgatory — AI works in slides, never reaches care
Agnotic approach
We scope to a clinical workflow integration point from day one, with a target clinician user and measurable clinical lift.
Challenge
Undocumented PHI exposure in prompts or training data
Agnotic approach
PHI segregation architecture, prompt-level redaction, and a BAA-covered inference path baked into the SDLC.
Challenge
Model drift silently erodes performance after launch
Agnotic approach
Continuous evaluation pipelines, shadow-mode gold standards, and alerting on subgroup regression.
Challenge
SaMD ambition without SaMD delivery discipline
Agnotic approach
Early risk classification and an explicit go / no-go on the regulated pathway, with a QMS-ready file if we proceed.
Challenge
Costs and latency scale into a crisis at production load
Agnotic approach
Inference cost modelling, request routing, caching, and model tiering designed before launch — not after the bill arrives.
Standards we build against
Clinical AI standards in our SDLC
HIPAAGDPRFHIRHL7FDA (SaMD)MDR (EU)SAMHSAHITRUST
Clinical AI Built for Healthcare Scrutiny, Not for Demos
Every clinical AI system we ship is risk-classified, PHI-safe, and engineered with auditability from the first commit. Compliance is architecture — never a launch-week scramble.
Health Insurance Portability and Accountability Act
Protect PHI with privacy-first architecture, encrypted storage and transmission, strict access controls, and traceable audit logs.
General Data Protection Regulation
Implement lawful consent flows, data minimization, retention controls, and secure processing for sensitive reproductive and health data.
Fast Healthcare Interoperability Resources
Enable standardized health data exchange across apps, care teams, and systems through robust FHIR-ready APIs and mappings.
Health Level Seven International
Support enterprise-grade interoperability with HL7-based integrations for records, events, and clinical messaging workflows.
Health Information Trust Alliance
Align security programs to healthcare-specific controls and risk management practices trusted by providers and partners.
Health Information Technology for Economic and Clinical Health Act
Design with breach notification readiness, digital record safeguards, and operational controls that support regulated care programs.
Food and Drug Administration Software as a Medical Device
Plan software quality, traceability, and documentation pathways for products that may require SaMD review and regulatory submission.
Medical Device Regulation (European Union)
Prepare EU market-ready processes for risk classification, evidence tracking, and lifecycle governance under MDR expectations.
Substance Abuse and Mental Health Services Administration (42 CFR Part 2)
Apply confidentiality controls and consent-aware sharing models for behavioral and mental health related data experiences.
We Are Technology-Agnostic
With a diverse technology stack, we deliver solutions using a technology-Agnostic approach to meet your unique needs.
Wireframe & Ideation
User Experience
Real-Time Projects
Voices of Success
We don't just build products; we forge lasting partnerships. See how we've helped industry leaders transform their vision into technical reality.
"I can clearly see how Agnotic has a unique way of handling end-to-end development. They are always active on quick chat and provide support quickly."
Aaron Phelan
Founder, Benchmark
"Agnotic is the best technical team we evaluated. Their engineering excellence made our work dramatically easier and allowed us to stay focused on what matters most for maternal care outcomes. They took full ownership of the technical execution, and we are always happy to continue working together."
Kim Smith
Founder, My Lauren
"Agnotic combines deep technical expertise with strong domain knowledge. They understand the business context, anticipate challenges, and make collaboration smooth and effective."
John Pasmore
Founder, Latimer
Frequently Asked Questions
General AI agencies optimise for working demos. We optimise for products that survive real clinical environments — compliance review, workflow fit, PHI handling, drift monitoring, and cost control. The architecture, validation, and SDLC are different from day one.
Build the right healthcare product before you build the wrong one
Product strategy session for teams building clinician tools, patient-facing products, workflow systems, and AI-enabled healthcare software. We map the product, workflow, compliance path, and technical plan before expensive mistakes compound.