Managers ask in plain language; the agent explores the database, writes and fixes SQL, charts the result, and answers with findings and a recommendation.

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AI agent

Universidad del Ceibo

Survey & voice-of-customer analyzer

Reads hundreds of open-ended answers, builds the codebook, classifies topic and sentiment, cross-tabs by campus, pulls quotes, and delivers the report.

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AI agent

Fundación Cosecha Segura

Project evaluation assistant

Reads a project proposal, builds and validates the logical framework, recommends the impact evaluation design, and checks that the sample is feasible.

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AI agent

Universidad del Ceibo

AI academic tutor

A Socratic tutor that explains with the course material, generates exercises, gives hints without revealing answers, and feeds a teacher dashboard.

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AI agent

Cumbre Alimentos

Intelligent document reader

Reads the tender and the PDF proposals, checks every requirement with the verbatim quote as evidence, flags risky clauses, and recommends who to award.

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Signs you need this

Your team wastes hours searching for information in thousands of PDFs.
You want to use ChatGPT but can't risk uploading confidential data.
Customer service gives inconsistent answers depending on who replies.
Employees constantly interrupt experts to ask repetitive questions.

Before

Scattered knowledge, slow keyword searches through messy folders, and total reliance on people’s memory.

After

A private chat interface that answers instantly, citing the exact page of the manual or policy where it found the answer.

What we build

RAG (Retrieval-Augmented Generation) architectures, autonomous agents, and custom web apps built on today’s most advanced LLMs.

What you get

A private web app with secure sign-in, running on your own cloud (GCP/AWS) for maximum privacy.

What we need

Your document base. The AI can only answer well if the source documents are current and accurate.

First Pilot Scope

We start by isolating ONE specific use case (e.g. "Answer questions about the travel expense policy" or "Analyze tender documents"). We index 50 to 200 documents and launch a beta for 5 test users.

⏱️ Duration: 4-6 weeks

🎯 Focus: 1 specific corpus

How we measure value

• Adoption rate: weekly active users.
• Time saved per query compared with the old way.
• Correct answers vs. hallucinations (measured during testing).

Risks & Limitations

• LLMs hallucinate. That’s why we always show citations and sources; a person always makes the final call.
• If the document base is outdated, the app will give outdated answers.

View related prototype

Document Copilot: try a live RAG assistant.

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The Madriguera Method™

We don't build technology for the sake of it; we solve real problems through a structured 6-step process. Fast pilots, measurable results.

1. Assessment

2. Roadmap

3. MVP

4. Feedback

5. Delivery

6. 3-mo Impact