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This piece introduces a method for scoring the confidence of AI-generated structured outputs, like JSON. Unlike traditional focus on hallucinations in freeform text, this approach evaluates token-level certainty using the model’s internal probabilities, hesitation between top choices, and overall entropy. The resulting score is transparent, interpretable, and tunable—ideal for high-stakes use cases in business and government. Human feedback is used to calibrate and improve the score over time, ensuring it aligns with real-world expectations of accuracy and reliability

Scattered clauses, buried risks, and documents that don’t want to be read. In 2025, due diligence still relies on PDFs and prayers. This post unpacks why AI hasn’t fixed the problem… and what it takes to finally make your documents talk.

MCP isn’t just another protocol—it’s a paradigm shift.

Large Language Models like GPT have opened new possibilities for automating document understanding — but when applied directly to real-world business documents, they often fail to deliver reliable, structured data. We expose the limitations of one-shot extraction methods, especially in handling complex layouts and tables. Using a curated benchmark of 1,100+ tables, we reveal why OCR and Markdown inconsistencies are a major source of errors.

La inteligencia artificial generativa es la clave para transformar datos desestructurados en decisiones ágiles, resolviendo el caos informativo empresarial con eficacia.

El Premio Nobel 2024 celebra los avances de la inteligencia artificial en ciencia, marcando una nueva era de colaboración humano-máquina que transforma industrias y revoluciona nuestra comprensión del mundo

Supera el síndrome de Diógenes digital: transforma datos no estructurados en innovación y ventaja competitiva con inteligencia artificial generativa