The Data Layer Your
Evaluation System Scores Against
We build the unified data infrastructure that makes your clinical data accessible, structured, and traceable for AI applications and the evaluation systems that measure them.
A well-built evaluation system will tell you that your AI is fabricating on a specific item type, or that retrieval is failing for a particular category of clinical documentation. That diagnostic is only useful if the trace leads back to a data layer where the problem can actually be identified and fixed.
Most healthcare data sits scattered across EHR systems, imaging platforms, lab interfaces, billing software, and legacy systems, each using different formats and standards. AI systems built on this fragmented foundation produce errors that are difficult to diagnose because the data lineage is unclear. When your evaluation system flags a documentation gap, you need to know whether that gap originated in the source system, in the ingestion pipeline, in the indexing layer, or in the retrieval configuration.
In a nutshell: We build the data infrastructure that gives your AI systems complete, structured clinical context and gives your evaluation systems the traceability to diagnose failures at the data level.
What We Build
02.
Medallion architecture with full data lineage
A bronze-silver-gold data layer that progressively cleans, validates, and structures raw clinical data into AI-ready formats. Bronze captures raw data exactly as it arrives from source systems. Silver applies validation, deduplication, and standardization. Gold produces the structured, enriched datasets your AI systems consume at inference time. Full lineage tracking at every layer, so when your evaluation system traces a failure back to the data, you can see exactly which transformations the data passed through and where the issue originated.
03.
Vector-optimized storage for retrieval-augmented generation
Hybrid architecture combining relational databases for structured data with vector databases for semantic search across clinical documentation. Your AI systems find clinically relevant information based on meaning rather than keyword matching alone, which is essential for RAG implementations where the quality of retrieved context directly determines the quality of generated output. When your evaluation system identifies a high fabrication rate, the retrieval layer is typically the first place the trace leads, and the vector storage architecture determines whether you can diagnose and fix the retrieval failure efficiently.
04.
Unified patient context layer
A comprehensive view combining data from every connected source system into a single, structured patient representation that preserves clinical context, maintains temporal relationships, and is accessible in real time. Instead of AI systems receiving isolated fragments, diagnosis codes without clinical notes or lab values without medication context, they receive the same complete clinical picture that informs human decision-making. This is the layer that determines whether your AI has enough context to generate accurate outputs or whether it will fabricate to fill the gaps.
How Data Infrastructure Connects To Evaluation
The evaluation system diagnoses.
The data layer fixes.
These two services are designed to work as a closed loop. The evaluation system identifies what is going wrong and categorizes each failure by type. Two of the four failure taxonomy buckets, fabrication and documentation gap, route directly to the data infrastructure layer for resolution.
When the data layer and the evaluation layer are architected together, these diagnostic loops operate efficiently. When they are built separately, tracing a failure from the evaluation system to its root cause in the data pipeline becomes a manual, time-consuming process.
Why This Investment Compounds
Every AI application you build runs on this foundation
Technical Environments
We engineer within the systems your organization already operates on rather than requiring migration to unfamiliar platforms.
How is this different from RPA or workflow automation?
What percentage of cases do agents handle autonomously?
Do agents replace clinical or administrative staff?
How long before agents are handling real workload?
Can we start with one workflow and expand?