Private AI Knowledge Assistant
Turn your company manuals, SOPs, and dense document archives into an interactive AI assistant with source-grounded answers and verifiable page citations.
The Knowledge Bottleneck
Crucial operational knowledge is trapped in 200-page PDF handbooks, Notion pages, and Slack threads. Senior staff waste hours answering repetitive questions, and generic AI tools hallucinate answers when queried on internal specifics.
The Grounded RAG Solution
A private, isolated Retrieval-Augmented Generation (RAG) pipeline. Files are parsed into semantic chunks, indexed with dense vector embeddings and BM25 keywords, and queried with exact page-level citations for every single answer.
What This System Automates
Extracts precise operational steps and guidelines from multi-section employee handbooks and compliance manuals.
Empowers tier-1 support reps to answer complex customer inquiries in seconds with exact product manual references.
Every generated answer includes clickable footnotes pointing to the exact document filename and page number.
Background re-indexing pipelines ensure that modified or deleted files are immediately reflected in search results.
Who Is This Built For?
Instantly retrieving warranty policies, troubleshooting manuals, and SLA details while on the phone with customers.
Searching across past client deliverables, proposal archives, research decks, and internal operational playbooks.
Querying architecture decision records (ADRs), API specifications, onboarding guides, and legacy codebase notes.
Hybrid Retrieval Architecture
Tenant & Role Isolation
Access-controlled vector filtering ensures users only receive results from documents they have permission to see.
Dense + BM25 Hybrid Search
Combines semantic vector embeddings with BM25 keyword matching and Reciprocal Rank Fusion (RRF) for high precision.
Grounding & Citations
Structured prompting forces the LLM to output verbatim references, page numbers, and bounding-box citations.
Case Study: Waraq AI Multi-Tenant Hybrid RAG
Waraq AI is a deployed multi-tenant hybrid RAG system engineered by Ahsaan. Built with FastAPI and Qdrant Cloud, it combines dense vector search with BM25 keyword indexing to provide pinpoint page citations with 55 automated tests verifying retrieval integrity.
How We Build It (10–14 Days)
- Day 1–3: Document sample audit, parsing strategies, and chunk size optimization.
- Day 4–8: Vector store indexing, BM25 integration, and hybrid fusion tuning.
- Day 9–11: Citation verification tests, grounding evaluations, and latency tuning.
- Day 12–14: Web UI handover, role permissions setup, and production deployment.
Fixed Scope & Fixed Pricing
Includes parsing pipeline setup, Qdrant vector database setup, hybrid search, grounded answer generation, interactive UI, and 14 days of tuning.
Frequently Asked Questions
How do you prevent the AI from hallucinating incorrect policies?
We enforce strict grounding prompts with negative constraints: if the ingested documents do not explicitly contain the answer, the model outputs an honest fallback. Furthermore, every statement must cite an exact source chunk with page numbers.
Is our internal company data safe and kept private?
Yes. Document vectors are stored in isolated collections with tenant metadata filtering. Data is never used to train public models, and API requests to models are made under strict privacy and zero-data-retention terms.
What file formats and sources can be ingested?
We support PDF manuals, Word documents (.docx), Markdown files, Google Drive folders, Notion workspaces, and database exports.
Why use hybrid retrieval instead of simple vector similarity?
Vector embeddings alone struggle with exact part numbers, acronyms, and legal terminology. Hybrid search combines dense semantic embeddings with BM25 sparse keyword matching and Reciprocal Rank Fusion (RRF) to ensure exact terms are never missed.
What is the delivery timeline for a custom knowledge base?
A typical deployment takes 10 to 14 business days, including document chunking tuning, hybrid index construction, citation verification testing, and frontend handover.
Ready to turn your documents into an AI assistant?
Let's discuss your document structure and build a verifiable knowledge base.