Healthcare and pharma
Healthcare and pharma AI built around sensitive data, not in spite of it
Clinical text structuring, coding support, risk and claims models, and agentic question answering over medical data, on HIPAA-compliant data platforms, for providers, payers, healthcare analytics companies and pharmaceutical teams.
Before you hire an AI partner
The questions we expect, answered up front
Providers, payers and healthcare analytics companies, primarily in the United States, and pharma commercial and medical affairs teams and the intelligence platforms that serve them.
- Will patient and regulated data stay protected?
- Sensitive fields are identified in the first phase. We apply de-identification, redaction, least-privilege access and audit trails, and can keep models and data inside your environment.
- Can clinicians and medical teams trust the output?
- Outputs cite the note, record or query they came from, risk models report the factors behind each prediction, and accuracy is measured on your data before launch.
- Who is accountable for a clinical decision?
- The clinician. Our systems prioritize, structure and suggest. They do not make clinical decisions, and the interface makes that clear.
- Will it fit our existing systems?
- We work with the data where it lives, including HL7 and FHIR sources, SQL databases and unstructured documents, and unify them into one governed layer.
What we build here
What do we build for healthcare and pharma teams?
Clinical NLP, risk and claims models, and agentic question answering over medical data.
- Structuring clinical notes with NLP and named entity recognition
- ICD-10 and HCC code suggestion against controlled vocabularies
- Readmission, deterioration and claims cost prediction models
- HIPAA-compliant processing and governance of large-scale healthcare data
- Agentic question answering over drug, practitioner and research data
Proof
Healthcare and pharma case studies
Clinical NLP and risk models for a US healthcare analytics provider
Clinical NLP, ICD-10 and HCC coding support, readmission risk and claims cost forecasting over large volumes of unstructured clinical notes.
Result: 500K patient records processed by clinical NLP
Read the case studyPlain-English questions against a governed life sciences database estate
A natural language analytics service for a commercial intelligence platform used by medical and field teams at global pharmaceutical companies.
Result: Read-only by policy: the pipeline cannot produce a write
Read the case studyServices
Which of our services fit healthcare and pharma teams?
- Core RAG and LLM systems Retrieval over private data, natural language to query and structured extraction, with citations and evaluation.
- Core Agentic AI Planner and worker agents with typed tools, bounded retries, cost ceilings and full tracing.
- Core Machine learning and forecasting Forecasting, risk, churn, anomaly detection and recommendation models, shipped with monitoring.
- Core Data engineering Pipelines, lakehouses, vector and graph stores with contracts, quality gates, lineage and access control.
- DevOps, MLOps and LLMOps CI/CD, Kubernetes, observability, model deployment, LLM tracing and cost control for software, ML and LLM systems.
FAQ
Healthcare and pharma: common questions
Do you sign business associate agreements?
If an engagement requires us to access PHI, a BAA is needed, and we will confirm in the first call whether we can sign yours for that scope. Where possible we design the work so our engineers never need PHI access, using de-identified data or working inside your environment. There is no official HIPAA certification for vendors, and we do not claim one.
What healthcare and pharma results can you share?
For a US healthcare analytics provider: clinical NLP that structured 500,000 patient records and mapped them to ICD-10 and HCC codes, 80 percent claim cost accuracy for small-bill customers, and a 30-day heart failure readmission model at 70 percent AUC. For a pharma commercial intelligence platform: an agentic question answering service over program databases, with read-only queries enforced in the query layer.
Will our patient data be used to train AI models?
No. Your data is processed only for your engagement. Where hosted model providers are used, we select enterprise terms appropriate to your requirements, and where that is not acceptable we deploy open-weight models inside your environment.
Can you name your pharma clients?
No. Our life sciences engagements are covered by confidentiality agreements, so we describe the client and its customers by sector only. We can discuss the work in detail under NDA.
How do we start, and how long does it take?
With a two to three week fixed-scope assessment that tests one real use case on your data and ends with a written architecture, risk register and costed roadmap. Build timelines are committed after the assessment, not before.
Start with a two to three week assessment
Send us the problem and the constraints. You get a written architecture, a risk register and a costed roadmap, and you keep them whatever you decide next.