RAG System Development
Zero-hallucination enterprise RAG architectures: Hybrid vector+keyword search (pgvector / Pinecone), semantic reranking (Cohere), source citations, and multimodal document ingestion.
What is RAG System Development?
Off-the-shelf RAG systems often produce hallucinations, retrieve irrelevant context chunks, or fail on tabular data and complex PDFs. Naive chunking and simple cosine similarity are insufficient for mission-critical enterprise knowledge retrieval.
We architect production-grade RAG systems using hybrid dense vector search (pgvector / Pinecone) combined with sparse BM25 keyword matching and Cohere semantic reranking. We extract tables, charts, and text with OCR, grounding every AI answer in verifiable source page citations.
Combines semantic vector similarity with exact keyword matching for 99.4% retrieval accuracy.
Uses Cohere Rerank to filter out irrelevant context chunks, cutting latency and token costs by 60%.
Every generated response includes clickable source page numbers, document names, and snippet highlights.
What's Included in Every Project
Full-cycle AI engineering deliverables designed for accuracy, reliability, and business impact.
Multimodal Document Ingestion Pipeline
Parses PDFs, DOCX, CSVs, Notion pages, and technical manuals with table and chart extraction.
Hybrid Vector Search Engine (pgvector / Pinecone)
Combined dense vector embeddings (OpenAI/Voyage) and BM25 sparse keyword indices.
Semantic Reranking & Context Compression
Cohere Rerank 3 pipeline ensuring only the most relevant passages are passed to the LLM.
Interactive Web UI with Source Highlighting
Modern Next.js search interface displaying answer streams alongside source page previews.
Automated Knowledge Base Re-Indexing Webhook
Real-time sync updating vector embeddings whenever documents are added or updated.
30-Day Post-Launch Warranty & Retrieval Benchmarks
Precision and recall benchmarking across 100+ domain queries to ensure zero hallucinations.
Our 4-Step AI Engineering Process
Benchmark-driven sprint delivery with continuous accuracy evaluations.
Knowledge Audit & Chunking Strategy
We analyze document formats, design semantic chunking boundaries, and select optimal embedding models.
Vector Ingestion & Hybrid Search Setup
We configure PostgreSQL with pgvector, set up BM25 full-text indexing, and implement Cohere reranking.
Prompt Guardrails & UI Build
We write strict system prompts requiring source citations and construct the responsive Next.js search UI.
Evaluation Benchmark & Production Deploy
We run automated RAG evaluation (Ragas / TruLens) to verify accuracy, deploy live, and hand over the code.
Technologies & Foundation Models
Frontier LLMs (GPT, Claude & Gemini), vector databases, and workflow automation frameworks.
Milestone-Based Investment Tiers
Fixed pricing with no hidden licensing fees. 100% IP ownership upon final milestone.
Complete RAG system for internal documentation, technical manuals, or customer knowledge bases.
- Ingestion for up to 5,000 Documents
- Hybrid Dense + Sparse Vector Search
- Cohere Semantic Reranking
- Clickable Source Page Citations
- Branded Next.js Search UI Widget
- 30-Day Post-Launch Warranty
- 100% Source Code Ownership
High-volume RAG platform handling tabular data, charts, role-based access control, and auto-sync.
- Ingestion for up to 50,000 Documents
- Multimodal Table & Chart Vision OCR
- Granular Role-Based Access Control (RBAC)
- Automated Google Drive / S3 Auto-Sync
- Conversation History & Query Analytics
- Priority 30-Day Support
- Full GitHub Repo Access
Private on-premise RAG deployment with local open-source LLMs, private VPC, and compliance SLAs.
- Self-Hosted Local LLM (Ollama / vLLM)
- Private On-Prem / VPC Deployment
- Dedicated Senior AI Architect
- SOC2 & HIPAA Compliance Auditing
- High-Throughput Clustered Vector DB
- 24/7 SLA Support Options
Custom Enterprise & Bespoke Project Scope
Have specialized requirements, existing legacy architecture, dedicated SLA agreements, or custom team workflows? We analyze your technical scope and deliver tailored milestone estimates within 24 hours.
Related AI Automation Case Studies
Measurable efficiency gains and cost reductions delivered for our clients.
Commercial Aviation Technical Manual RAG Assistant
Indexed 45,000 pages of aircraft engineering manuals into pgvector with exact diagram and page citations.
B2B Legal Retainer Contract Intelligence System
Built a hybrid search and reranking engine that retrieves exact liability and indemnification clauses instantly.
Frequently Asked Questions
Common questions about rag system development and our AI engineering methodology.
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Ready to implement your rag system development?
Describe your operational workflows and automation objectives. Receive a comprehensive feasibility review and fixed milestone quote within 24 hours.