Four public reference implementations demonstrate how 1:1 AgileTech approaches AI workflows, model operations, healthcare data engineering and governed AI delivery. They are evidence of patterns and judgment—not claims of client deployment.
Local-first synthetic healthcare data platform that turns FHIR-shaped records into tested dbt models, care-gap marts, reliability metrics and data-quality triage.
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A working local AI triage console that combines runbook retrieval, structured model output, deterministic safeguards, human-review routing and held-out evaluation.
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A reusable execution layer that centralizes model calls, budget controls, routing, retries, usage logging and comparison across model configurations.
Explore the project →An MVP skill pack for turning rough AI ideas into phased PRDs, spike plans, implementation handoffs, evaluation plans and governance reviews.
Explore the project →Whether the problem is a data pipeline, an AI workflow or a model-execution layer, the same principles repeat: define the problem, make evidence visible, create explicit review points, measure the outcome and preserve operational boundaries.
Expose source data, retrieved context, cost facts or quality signals.
Use validation, budgets, review queues and explicit decision gates.
Evaluate usefulness, quality, reliability and cost before scaling.
We can use these patterns as a starting point for architecture, modernization, AI delivery, analytics or governance work.
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