← Case Studies · Government & PSU

Institutional knowledge,
on-premise and queryable.

A Language Intelligence deployment for a constitutional body. Natural-language search across decades of guidelines, circulars, and precedent decisions.

LlamaQdrantOllamaFastAPIPostgreSQLOn-Premise GPU

CLIENT CONTEXT

An apex regulatory body responsible for conducting elections across the world’s largest democracy. Every instruction, every circular, every precedent decision carries legal weight — and must be retrievable by field staff at the moment of need.

THE PROBLEM

Decades of policy documents in PDF form. Junior officers in the field spending hours searching for the right clause. No cloud-based AI service acceptable for this data. Existing keyword-based search returning either nothing or 200 results — neither of which answered the actual question.

How We Approached It

Four phases.
Each one auditable.

01

Document ingestion

All historical documents ingested, OCR-processed, chunked with metadata preserving document type, date, and citation. Each chunk hashed for integrity.

02

Embedding pipeline

Locally-deployed embedding model encoded each chunk. Vectors stored in an on-premise Qdrant instance. No data left the secure environment.

03

RAG pipeline

Llama-based LLM, fine-tuned on domain terminology, deployed on government GPU hardware. RAG retrieval grounded every answer in actual source documents.

04

Audit layer

Every query, retrieved chunk, and generated response logged with full traceability. Citations to document and page number rendered in every answer.

Institutional knowledge,on-premise and queryable.

Four phases.Each one auditable.

Your case study next.

Institutional knowledge,
on-premise and queryable.

Four phases.
Each one auditable.