Article: Local-First AI Inference: A Cloud Architecture Pattern for Cost-Effective Document Processing

Details

The default cloud document processing approach — sending every document to a managed AI API — is wasteful when input corpora have structured layouts. InfoQ describes a Local-First AI Inference pattern with three tiers: fast local deterministic extraction for clear-cut inputs, cloud AI for ambiguous cases, and human review for edge cases. In a production deployment processing 4,700 engineering-style documents, this routing strategy sent only 20–30% of inputs to Azure OpenAI, cutting costs by 75% and overall processing time by 55%. The critical decision engine is a composite scoring function combining spatial, anchor, format, and contextual criteria. A single criterion fails; the composite catches false positives such as title blocks vs. revision history tables that score similarly on any individual metric (98 vs. 66 on the same character). Prompt engineering mattered significantly: five targeted iterations of refinement raised accuracy from 89% to 98%, each addressing a specific error class including revision table confusion and grid reference false positives. The article also challenges the assumption that newer models are always better. GPT-5+ showed no accuracy improvement over GPT-4.1 on their 400-file domain-specific validation set, avoiding an unnecessary and costly migration. The key lesson: validate model upgrades against your own task-specific data, not vendor benchmarks.