Custom Knowledge Bases & Enterprise RAG
High-accuracy vector retrieval pipelines querying proprietary enterprise documentation with sub-300ms speed.
Fixed milestone scope • No hourly surprises • Direct GitHub delivery
For massive document collections, strict SOC2/HIPAA compliance, real-time sync, and re-ranking pipelines.
Why Typical Approaches Fail & How We Engineer It
Real engineering discernment: diagnosing root failure modes and engineering resilient solutions instead of generic AI wrappers.
Enterprises possess thousands of pages in PDFs, Google Drive files, contracts, and Notion docs. Keyword search fails because it misses semantic meaning, while standard ChatGPT models know nothing about internal company policies or private schemas.
We construct a production RAG system: (1) Automated document ingestion that parses PDFs, Word docs, and Markdown, (2) Semantic chunking with overlap, (3) Dual indexing combining PostgreSQL full-text search (`tsvector`) with pgvector cosine similarity, and (4) Strict source citation with page references.
Technical Architecture Blueprint
[Document Ingestion (PDF / Docs / Notion)]
│
▼
[Semantic Chunker & Metadata Tagger] ──▶ [Role-Based Tenant Guard]
│
▼
[PostgreSQL Database (Neon Serverless)]
│
├─▶ Full-Text Search (tsvector / BM25) ──┐
│ ├──▶ [Hybrid Re-Ranker] ──▶ Top-K Context ──▶ Grounded LLM
└─▶ Semantic Search (pgvector 3072d) ────┘Exact Deliverables & Scope
Clear scope boundaries eliminate surprises. Here is exactly what is delivered in a standard sprint versus what is handled under custom scope.
- Document parsing pipeline for PDFs, Markdown, and text files
- Hybrid PostgreSQL pgvector search with metadata filtering
- Verified citation injection in generated responses
- Fast streaming search API and sample frontend widget
- 14-day warranty
- •Automated continuous sync with Google Drive, Confluence, or Notion APIs
- •OCR parsing for complex scanned PDF tables and invoices
- •Cross-encoder re-ranking for ultra-high precision legal/medical workflows
Technologies Implemented in This Service
How This Sprint Is Executed
Discovery & Code Audit
Understand your product goals, inspect your existing codebase or requirements, and define a clear milestone roadmap with fixed deliverables.
Rapid Prototype & Validation
Build and validate a working prototype or targeted fix to test assumptions, AI response quality, and API contracts early with your team.
Implementation & Hardening
Refine business logic, implement caching and error handling, optimize database queries, and test edge cases thoroughly with regression tests.
Production Handover & Support
Deploy to production, integrate with your existing stack, and provide complete documentation, tests, and code walkthroughs.
Frequently Asked Questions About This Sprint
Why use PostgreSQL pgvector instead of Pinecone or Milvus?
PostgreSQL with pgvector allows your relational data and vector embeddings to live in the same ACID-compliant database. You eliminate the cost, complexity, and security risk of managing a separate third-party vector database service.
Can users verify where the AI found its answers?
Yes. Every generated answer includes exact Markdown source links and citations referencing the specific document, section, and page number retrieved from the context.
Ready to Build Custom Knowledge Bases & Enterprise RAG?
Tell me about your tech stack and target timeline. I review requirements and provide a clear milestone scope within 24–48 hours.
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