What we build,
end to end.
This is the full map of what UIT Software builds — healthcare technology, AI and agentic systems, product engineering, interoperability, cloud infrastructure, data and analytics, UX, security, and the work of modernizing what already runs. Nine disciplines, one engineering organization, applied together on every engagement.
Software for how care actually gets delivered
Healthcare technology is our starting discipline, not a vertical we layered on afterward. We build EHR/EMR-adjacent systems, care management platforms, and clinical workflow software that live inside real operational and regulatory constraints — not the simplified version of healthcare most software assumes.
The problems this solves are familiar to any care organization: care teams working across fragmented systems, workflows built for a tool instead of around a task, and reporting that lags what's actually happening on the floor.
- EHR/EMR-adjacent clinical systems and chart-based workflows
- Care management platforms across RPM, CCM, BHI, RTM, and TCM
- Clinical workflow automation and task orchestration
- Patient engagement and provider-facing applications
- Long-term, post-acute, and specialty care workflows
Outcome: software a clinical or operational team adopts because it fits how they already work, not software they route around.
Built around the workflow
Clinical accuracy, operational throughput, and patient trust designed as one system
From AI assistants to orchestrated AI workforces
Generative AI, copilots, and multi-agent systems that assist people at the point of decision — designed with human oversight, not around its absence. Every agent operates inside defined boundaries, with escalation back to a person wherever judgment is required.
Problems this replaces
Generative AI
Foundation-model applications tuned to clinical and operational language.
Conversational AI & Copilots
Natural-language interfaces embedded directly in clinical and back-office workflows.
Multi-Agent Orchestration
Specialized agents coordinating through a governed AgentOS layer.
AI Memory & Knowledge Systems
Persistent, retrievable context so agents reason with history, not a single prompt.
Workflow Automation
Rules- and AI-driven automation of repeatable operational work.
Human-in-the-Loop Governance
Escalation and approval paths built in wherever clinical or financial judgment is required.
Outcome: AI whose boundaries a compliance officer can explain and a clinician can trust — because they were designed in, not discovered after launch.
Discovery through the system that runs in production
We take products from a first architecture conversation to a system carrying live clinical, operational, or financial workflows — and stay engaged as it scales. Product engineering here means owning outcomes, not shipping a milestone and moving on.
This is what it replaces: roadmaps that stall between prototype and production, architecture decisions made under deadline pressure that a team lives with for years, and vendors who hand off a build and disappear before it's actually running.
- Product discovery and stakeholder & workflow analysis
- Architecture and system design built for scale
- MVP build-out through production hardening
- Agile delivery with continuous client visibility
- Ongoing ownership — monitoring, iteration, evolution
Outcome: a product roadmap that survives contact with real usage, not just a demo.
A data fabric, not another silo
Healthcare data lives across EHRs, claims systems, labs, devices, and patient hands. We build FHIR-native integrations for the EHR and practice-management ecosystems healthcare organizations already run — including Epic, Cerner, and Athenahealth environments — and normalize what comes out of them into canonical models AI and applications can actually use.
This is where prior authorization, eligibility checks, and claims workflows stop depending on brittle, one-off, point-to-point integrations.
- HL7 v2 and FHIR R4-native integration
- FHIR-native connectivity for EHR/EMR and practice-management ecosystems
- Claims and EDI connectivity — eligibility, prior auth, remittance
- Canonical healthcare data models
- Healthcare API design and data normalization
Outcome: one normalized layer underneath your applications, instead of a new point-to-point integration every time a data source changes.
Canonical Data Fabric
Normalized and ready for AI & applications, regardless of source
Infrastructure that holds under real clinical load
Cloud-native architecture across AWS, Azure, and GCP-class environments — containerized, multi-tenant, and built for elastic scale. Infrastructure gets the same architectural rigor as the application, because a platform carrying live patient or claims data can't treat it as an afterthought.
Problems this solves
Cloud-Native Architecture
AWS, Azure, and GCP-class design patterns suited to healthcare workloads.
Containers & Orchestration
Kubernetes and Docker-based deployment for consistent, portable environments.
Multi-Tenant SaaS
Tenant isolation and data boundaries designed in from the schema up.
CI/CD Pipelines
Automated build, test, and release pipelines that shorten the path to production.
Infrastructure as Code
Reproducible environments defined and versioned as code.
Disaster Recovery & Resilience
Backup, failover, and recovery planning built into the architecture, not added after launch.
Outcome: architected for high availability and elastic scale, so infrastructure grows with adoption instead of gating it.
Data platforms that turn into decision support, not just dashboards
We build healthcare data platforms that unify clinical, claims, and operational data, then apply population-level analytics, risk stratification, and predictive modeling on top. Every model is built as decision support for a clinician or care team — it surfaces a signal and the reasoning behind it; the person acting on it makes the call.
This replaces data warehouses no one queries, risk scores with no workflow attached, and predictive models that never leave the notebook.
- Unified healthcare data platforms across sources
- Population health and risk stratification analytics
- Predictive modeling for clinical & operational decision support
- Quality measure and value-based care reporting
- Operational and financial analytics
Outcome: analytics built to change what a care team does next, not just what they can see.
Raw Data
EHR, claims, devices, and operational systems.
Models & Stratification
Risk scores, cohorts, and predictive signals.
Decision Support
Surfaced to a clinician or care team, with reasoning attached.
Design for the workflow, not just the screen
Clinical and patient-facing software fails when design treats a complex workflow like a generic form. We design around how a nurse actually triages, how a patient actually manages a chronic condition, and how a billing team actually resolves a denial — with accessibility built in from the first wireframe, not the interfaces designed around what's easy to build in a 12-hour shift.
Clinical Workflow UX
Interfaces designed around real clinical tasks and cognitive load, not idealized ones.
Patient-Facing Design
Engagement and self-management experiences built for varied health literacy.
Design Systems
Component libraries that keep large products consistent as they grow.
Accessibility
WCAG-aligned design considered from the first wireframe, not retrofitted before launch.
Mobile & Responsive Design
Consistent experience across clinical workstations, tablets, and phones.
Usability Research
Direct observation of clinical and patient workflows before design begins.
Outcome: software people choose to use, in environments where they usually don't have the choice.
Security as architecture, not a checklist
Access control, encryption, audit logging, and tenant isolation get designed into the architecture from the start — because in healthcare, security decisions made late are usually the wrong ones. This section is a summary; the full detail lives on our dedicated security page.
- Role-based and attribute-based access control
- Encryption in transit and at rest
- Comprehensive audit logging
- Tenant and data isolation for multi-tenant systems
Outcome: architecture that a security review doesn't have to fight against.
Architecture designed to support HIPAA-aligned environments
Built for the compliance reality of healthcare, reviewed as the system evolves
Modernizing what already runs. Extending the team that runs it.
Two disciplines, one practice: incrementally modernizing systems a healthcare operation already depends on, and embedding senior engineers directly into a client's team when the need is capacity, not a new build.
Legacy Modernization
Most healthcare organizations aren't starting from zero — they're running a system that works well enough to be dangerous to touch. We modernize incrementally: extracting services, replacing brittle integrations, and migrating data without stopping the operation that depends on it.
- Incremental service extraction from monoliths
- Database and platform migration
- Legacy integration replacement
- Parallel-run and phased cutover strategies
Dedicated Product Engineering Teams
When the constraint is engineering capacity rather than a new product, we embed senior engineers as an extension of your team — accountable to your roadmap, working in your systems, without the ramp-up of a traditional hire.
- Embedded senior engineers and full pods
- Direct integration into existing sprints and tooling
- Flexible scaling as scope changes
- Continuity across the engagement, not rotating staff
Outcome: whether the system already exists or the team just needs more hands, the engineering standard doesn't change.
Not sure where to start?
Let's map it together.
Tell us what you're building or modernizing — we'll help place it on this map and outline a path forward.