PolicyTrace
A Document AI workflow for UK motor insurance PDFs, with structured extraction, provenance, conflict resolution, and human review.
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AI Tool Stack
Where AI demos become working systems.
Practical builds, patterns, and lessons for AI systems that survive real workflows: evidence, evaluation, review loops, trust boundaries, deployment, and production architecture.
Why One Model Should Not Handle Every AI Task
Using one model for every AI task turns model choice into a hidden default. Production workflows need explicit model policies for task type, risk, latency, evidence, fallback, and evaluation.
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Token Economics: Why Prompt Bloat Kills AI Margins
Token waste is architecture debt. Oversized prompts, broad context, verbose outputs, retries, review, and eval runs compound into the cost per trusted completed unit.
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Semantic Caching Is Harder Than It Looks
Semantic similarity measures intent, not validity. Safe AI caching needs source versions, schemas, model policy, evidence, permissions, review state, and wrong-hit tracking.
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How to Trace a Failed LLM Run
When an AI workflow fails, the final answer is not enough. You need the input, route, prompt, model, context, schema errors, evidence gaps, review action, and eval case.
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Designing Fallbacks for AI Systems
AI failures need designed paths: retry, narrower scope, simpler model, stronger model, human review, partial answer, safe message, or stop.
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What Happens the Day After an AI System Launches?
Launch is not the finish line for an AI system. It is when review queues, failures, costs, user feedback, model changes, and ownership finally become real.
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