Health data foundations that bring clinical, claims and operational sources into one governed picture, ready for AI and audit alike.
- Clinical and claims data integration
- Governed, AI-ready data modeling
- Interoperability and audit readiness
Policy documents parsed into working, schema-compliant rules by agents, with clinical SMEs approving instead of transcribing.
- Policy parsing and rule extraction agents
- Medical code mapping at scale
- Clinical SME review checkpoints
Research assistants grounded in your biomedical sources, with every answer cited to where it came from.
- RAG over research, trials and internal sources
- Domain-tuned models for biomedical language
- Cited, clinical-grade responses
Clinical and regulatory documents processed at scale: parsed, summarized and surfaced when decisions need them.
- OCR, parsing and summarization at scale
- Regulatory-grade content workflows
- Exception routing to human reviewers
AI-assisted triage and resolution across member and care operations, keeping queues falling as volumes grow.
- AI triage and routing of requests
- Knowledge-grounded support assistants
- Human handoff built into every flow
Machine learning engineered to production grade for imaging, detection and clinical analysis workflows.
- Imaging and detection model engineering
- Unified analysis platforms replacing tool sprawl
- Automated reporting on model outputs
Payment Integrity · Health Plans · Agentic Insurance Rules Engine
The Challenge
- Lengthy, complex and abstract policy documents
- Manual SME interpretation of every policy
- Slow mapping of rules to medical codes
- Payer onboarding measured in months
Our Approach
- A rule agent parsing policy into coverage and rules
- A code agent generating schema-compliant rules
- Reviewer agents with humans in the loop
- Platform-native build on a managed cloud AI stack
Outcomes
Pharma Research · Intelligent Research Assistant
The Challenge
- Massive biomedical data across research and trials
- Time-consuming manual research across resources
- Clinical and regulatory grade content required
- Limited traceability and explainability
Our Approach
- Intelligent processing with OCR, parsing and summarization
- RAG with vector and semantic search
- A domain-tuned LLM for biomedical language
- Agent orchestration across research sources
Outcomes
Clinical ML· Imaging · Neuron-Imaging Analysis Platform
The Challenge
- Five separate analysis tools in the research workflow
- Manual, time-consuming neuron-imaging analysis
- Reports written by hand after every run
Our Approach
- One unified neuron-imaging analysis platform
- ML engineered for early detection workflows
- Automated report generation replacing manual write-ups
Outcomes
Production-Grade Delivery
Human in the Loop, Always
Traceability as a Design Input
Production, Not Pilots
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Design
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Run & Scale
The systems are decision support, not decision makers: every output is cited to its source and reviewed by your qualified people before it matters.
We design for data remaining in your environment under your controls, with the access management and audit trails healthcare review expects.
That is the bar we build to: domain-tuned language, grounded retrieval and a citation on every answer, so the content can face review.
Your clinical experts do, through approval checkpoints built into the workflow itself rather than written into a policy memo.
Yes. We integrate with the clinical and claims systems you run today, and model the data so your AI and your auditors read the same truth.
