Sovereign Enterprise RAG for Banks, Telcos, and Public Sector
Pak-LLM's enterprise RAG ingests an organisation's own documents, chunks and embeds them, and answers questions with citations back to the source passage. It can run fully on-premise so documents never leave the organisation's network, which is the configuration used where data residency is non-negotiable.
- Written for:
- Banks, telecom operators, insurers, universities, and public-sector bodies with large internal document estates.
- Last reviewed:
- Author:
- Saad Bin Farrukh
Why does RAG matter more than a bigger context window?
A context window lets a model read a lot at once; RAG lets it find the right passage among millions of pages and prove where the answer came from. For policy and compliance work, the citation is the point — an answer nobody can trace is not usable evidence.
- Answers carry passage-level citations back to the source document.
- Retrieval is scoped by role so users only see documents they are entitled to.
- Document updates are re-indexed without retraining the model.
- Hybrid keyword plus vector search handles Pakistani legal and regulatory vocabulary better than embeddings alone.
What does the ingestion pipeline handle?
The pipeline ingests PDFs, scanned documents with OCR, Office files, web pages, and structured exports, then chunks them semantically and stores embeddings in PostgreSQL with pgvector. Scanned Urdu documents are a first-class input rather than an afterthought.
- OCR for scanned Urdu and English documents.
- Semantic chunking that respects headings, clauses, and tables.
- Metadata capture for department, classification, and effective date.
- Incremental re-indexing when a source document changes.
How is access controlled?
Retrieval is filtered at query time by the user's role and document classification, so a model cannot surface a passage the user is not entitled to read. Every query and citation is logged for audit.
- Role- and classification-based retrieval filters.
- Per-tenant isolation in multi-tenant deployments.
- Audit log of query, retrieved passages, and answer.
- Optional human-approval gate before an answer is exported.
What does on-premise deployment involve?
An on-premise deployment ships as an appliance inside your network: the model weights, retrieval store, and API all run locally, with no outbound dependency for inference. Hardware sizing depends on document volume and concurrency, and is scoped during a pilot.
- No document leaves the customer network.
- Air-gapped operation supported for defence-adjacent environments.
- Model and index updates delivered as signed offline bundles.
- Pilot typically runs on a single department before wider rollout.
Frequently asked questions
Can Pak-LLM RAG run without any internet access?
Yes, in the on-premise configuration. Model weights, the retrieval index, and the API run inside your network, and updates are delivered as signed offline bundles for air-gapped sites.
Does it answer in Urdu?
Yes. Documents can be ingested in Urdu or English, questions can be asked in either language, and answers can be returned in the language of the question with citations preserved.
How accurate are the citations?
Citations point to the specific chunk retrieved, with document and section metadata. Accuracy depends on ingestion quality and chunking; pilot on a representative document set and measure citation correctness before rollout.
Which vector store is used?
PostgreSQL with the pgvector extension, combined with keyword search for hybrid retrieval. This keeps the retrieval layer inside the same database boundary as the rest of the application.