01 Fractional Healthcare Data Platform Engineering
PromptFormr provides senior engineering expertise to healthcare organizations building and improving data platforms: the depth of a staff-level data platform engineer, scoped to what you actually need.
Data architecture
Platform and pipeline architecture designed for how healthcare data actually behaves: fragmented, messy, and regulated.
Data & pipeline engineering
Reliable, testable data pipelines built for long-term maintainability, not just the first successful run.
Entity resolution
Record linkage and identity resolution: patient matching, customer identity, and deduplication across fragmented systems.
Automation
Automating the manual data work: recurring loads, validation, reconciliation, and the operational glue between systems.
Distributed processing
Large-scale processing for clinical and claims data at volumes where single-machine approaches stop working.
Healthcare data integration
Interoperability across EHR, claims, and clinical sources: HL7, FHIR, flat-file realities and all.
Data quality systems
Validation, reconciliation, and monitoring so data problems are caught by the platform, not by your users.
Platform modernization
Incremental migration of legacy data systems to modern platforms without breaking what already works.
02 AI Workflow Engineering
PromptFormr builds production AI workflows where AI creates measurable improvements, engineered with the evaluation, testing, and observability that production systems require.
Clinical document extraction
Structured data from clinical notes and documents, including privacy-preserving deployments with local models.
AI-assisted data transformation
LLMs as components inside pipelines for enrichment, standardization, and mapping tasks that resist hand-written rules.
LLM-powered pipelines
End-to-end pipelines where model outputs are validated, versioned, and monitored like any other transformation.
Local & private model deployments
Small language models running inside your environment when PHI can't leave it.
AI evaluation systems
Golden datasets, regression suites, and evaluation frameworks so you know when a code change (human or AI written) makes things better, or worse.
Agentic development workflows
An agentic team of coding, review, and testing agents built into the development loop, including the repo management and context management that keep agents productive on a real codebase.
Knowledge graphs
Entity and relationship graphs over multi-source data, from patient identity across clinical systems to risk networks in financial data.
Human + AI workflows
Runtime review and approval loops: AI output gets validated and queued, and an engineer approves what ships to production data.
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