Technical Advisory & Consulting Fixed-Scope Sprint / Milestone-Based
Enterprise RAG & Private Knowledge Search Engine
Private and secure Retrieval-Augmented Generation (RAG) engines to audit, query, and extract operational intelligence across technical documents, contracts, and knowledge bases.
⏱ Estimated Timeline: 3 - 5 Weeks
Included Deliverables & Scope
- ✓ Ingestion Pipeline & Intelligent Chunking: Concurrent parsing across heterogeneous document formats, schemas, and source code with semantic embeddings
- ✓ Hybrid Vector Storage: Dual-index configuration combining dense semantic vectors and sparse lexical search (BM25 + Cosine Similarity)
- ✓ Zero-Data-Leakage Architecture: Self-hosted local model deployments or enterprise API connectors governed by strict zero-retention policies
- ✓ Re-ranking Layer & Strict Attribution: Cross-encoder relevance filters and re-ranking to guarantee hallucination-free answers linked to primary sources
- ✓ Role-Based Access Control (RBAC): Document-level access isolation aligned with existing enterprise authorization hierarchies and audit policies
Retrieval-Augmented Generation Vector Search Hybrid Search Information Retrieval Enterprise Knowledge Base RBAC Security Semantic Caching
Build Enterprise RAG → Overview
Enterprise operational knowledge is frequently trapped in siloed repositories and legacy document stores. Deploying an enterprise-grade RAG engine demands far more than naive document-to-LLM pipelines; it requires advanced semantic chunking, low-latency hybrid retrieval, and deterministic access governance to guarantee employees only retrieve records permitted by their operational role.
What We Deliver
- High-Precision Semantic Retrieval: Sub-second location of internal compliance policies, contract clauses, and complex technical system specifications.
- Verifiable Source Attribution: Every synthesized answer is explicitly grounded with direct citations, line extracts, and links to source documents.
- Privilege & Tenant Isolation: Native enforcement of enterprise permission boundaries paired with tamper-evident query audit logging.
- Latency & Cost Optimization: Distributed semantic caching layers that accelerate frequent queries and reduce inference API spend.