RAG · OLLAMA · PYTHON · NEXT.JS

RAG systems and internal AI assistants

I build custom RAG systems that semantically retrieve internal documents and deliver source-grounded answers. Privacy, retrieval quality, and integration with existing repositories are part of the architecture from day one.

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What this service covers

Document Processing

Extract, structure, segment, and enrich content with useful metadata.

Retrieval

Tune semantic search and ranking to real questions and document types.

Secure Application

Integrate permissions, source grounding, and controlled model access.

Evaluation

Measure answer quality with real test questions and improve it continuously.

Approach

Analysis

Map documents, user groups, questions, and privacy requirements.

Proof of Concept

Validate retrieval quality using real documents and questions.

Integration

Connect data sources, interface, permissions, and model operation.

Optimisation

Continuously improve answer quality using real-world feedback.

TECHNOLOGY & METHODS

PythonFastAPINext.jsOllamaPostgreSQLDockerRAG

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