Applied AI & GenAI
Applied AI and GenAI for regulated teams: ambient clinical documentation, clinical decision support, and autonomous medical coding — governed and in production.
The problems that bring teams to us
Pilots that never reach production
The demo impresses, then stalls: there's no reference architecture, evaluation harness, or path from a notebook to a monitored, governed system.
No data foundation for AI
Fragmented, ungoverned data — not the model — is usually the limiting factor. Without clean retrieval and access control, quality and compliance both suffer.
Model risk you can't defend
Regulated teams can't ship what they can't explain, evaluate, or audit. Hallucination, drift, and PHI exposure are board-level and legal concerns, not edge cases.
What we deliver
Most AI stalls between a promising demo and a system a regulated team can actually run. We design the architecture first — data, retrieval, guardrails, and evaluation — then build to it, so generative AI, LLMs, and machine learning reach production and stay accountable. In healthcare that means ambient clinical documentation, clinical decision support, and medical-coding automation you can put in front of auditors.
Ambient clinical documentation
Ambient AI scribes that draft the clinical note from the visit and push a structured draft back to the EHR for clinician sign-off — reducing after-hours charting without replacing the clinician's judgment.
Medical-coding automation
Computer-assisted and autonomous coding that suggests CPT, ICD-10-CM, and HCC codes from the documentation, with confidence thresholds and coder review to cut denials and DNFB days.
Clinical decision & risk support
Care-gap, risk-adjustment, and decision-support models built on governed clinical data, surfaced in the workflow with the evidence a clinician needs to trust the recommendation.
Generative AI & LLM/RAG systems
Retrieval-augmented LLM systems grounded in your own data, with prompt design, citations, and guardrails so answers are accurate, sourced, and safe to act on.
Machine learning & MLOps
Predictive and classification models with the pipeline around them — versioning, automated retraining, drift detection, and monitoring — so models keep working after launch.
AI governance & evaluation
Evaluation harnesses, red-teaming, bias and hallucination checks, PHI controls, and audit trails so applied AI holds up to model-risk and regulatory scrutiny.
AI that earns its place in production
We take models from data through training to deployment and monitoring, with governance and guardrails built in — not a pilot that never ships.
Outcomes you can take to your board
From use case to production
A focused path from the highest-ROI use case to a deployed, evaluated, monitored system — not another pilot that dies in the lab.
Clinician and coder time back
Ambient documentation and coding automation take the keyboard work off clinicians and coders, so they spend time on judgment, not transcription.
Governed and auditable
Evaluation, guardrails, human-in-the-loop review, and drift monitoring so AI stays explainable, HIPAA-aligned, and defensible.
Architecture first, then a system you can run
AI fails in production for architectural reasons, not modeling ones: no retrieval strategy, no evaluation, no access control, no monitoring. We design those first — the data foundation, the RAG or model pipeline, the guardrails, and the human-in-the-loop review points — then build and instrument the system so it can be measured and defended.
That discipline is what lets a regulated team move an ambient-documentation or coding pilot into daily use instead of leaving it stuck in a proof of concept.
Applied AI for healthcare and financial services
In healthcare, the highest-value use cases are operational: ambient documentation that gives clinicians their evenings back, coding automation that recovers revenue and shortens the billing cycle, and risk models that close care gaps. In financial services, the same governed approach powers fraud scoring, underwriting support, and document intelligence.
- Ambient clinical documentation with clinician sign-off in the EHR
- Autonomous and computer-assisted CPT/ICD-10 coding with coder review
- RAG assistants grounded in your own clinical or policy content
- Fraud, underwriting, and document-intelligence models for finance
- clinical notes, clinician-signed
- Ambient AIclinical notes, clinician-signed
- CPT · ICD-10 with coder review
- Autonomous codingCPT · ICD-10 with coder review
- evaluated, drift-monitored, auditable
- Human-in-loopevaluated, drift-monitored, auditable
Have an AI use case in mind?
Start with an Architecture Assessment — we'll map the highest-ROI use case, the data and guardrails it needs, and a realistic path to production.
Frequently asked questions
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Let's talk about your roadmap, your compliance needs, and where technology can move your business forward.