Unconstrained prompt engineering cannot guarantee output formats, numerical accuracy, or regulatory compliance. In enterprise domains like construction material estimation, a hallucinated decimal point is unacceptable.
Instead of relying on the training weights of the model, RAG pipelines query high-dimensional vector embeddings over verified company documentation, injecting exact factual context into the system prompt.
We utilize instructor and native OpenAI / Anthropic JSON mode with strict Zod schema parsing. If the returned output violates the schema, automated retry loops repair the structure before it reaches application layers.
Every AI extraction is assigned a confidence score. High-confidence records process automatically, while borderline items route to a dedicated human review queue for single-click operator approval.
Enterprise AI is not about flashy chat widgets; it is about automating structured data extraction and knowledge discovery with mathematical certainty and verifiable audit trails.
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