RAG Development Services
CookMyTech is an enterprise RAG development company. We build high-accuracy Retrieval-Augmented Generation (RAG) systems, vector database architectures, and knowledge search engines that connect LLMs directly to your private enterprise data with sub-100ms retrieval and verifiable citations.
LLMs Know the Web, But Not Your Company
Base foundational models (like GPT-4o or Claude 3.5 Sonnet) lack access to your internal documentation, customer purchase histories, proprietary technical specs, and internal compliance handbooks.
When prompted on private business domain knowledge, standard AI tools hallucinate convincing but incorrect answers, breach data confidentiality, or return outdated information that damages customer trust.
Production Hybrid RAG with Strict Grounding
We design enterprise RAG architectures that index your knowledge into high-performance vector databases. Before any response is generated, our search pipeline dynamically retrieves the most relevant chunks, reranks them for precision, and forces the model to cite exact source sentences.
- ✔ Hybrid retrieval: Dense semantic vectors + Sparse BM25 keyword matching
- ✔ Cross-encoder reranking to elevate true context relevance
- ✔ Document-level ACLs ensuring strict tenant and role isolation
- ✔ Sub-100ms vector query latencies with semantic caching
Who Our RAG Development Services Are Built For
Enterprise Knowledge & Operations
Organizations looking to empower customer success, legal, support, or engineering teams with an internal AI knowledge assistant that answers complex queries directly from internal wikis, contracts, and ticket histories.
AI SaaS Founders & Startups
Founders engineering document search, legal tech analysis, medical record querying, or finance intelligence SaaS products requiring multi-tenant vector isolation and high gross margins.
Customer Support & E-Commerce
Brands seeking 24/7 automated resolution bots that consult live product manuals, return policies, and stock catalogs to resolve customer inquiries with 0% hallucinations.
Supported Vector Databases & RAG Technologies
Our RAG Engineering Process
Data Audit & Ingestion Architecture
We inventory your document sources (PDFs, Notion, SQL databases, Google Drive) and establish real-time or batch parsing pipelines with automated metadata extraction.
Chunking & Embedding Optimization
We test domain-tailored chunking strategies (semantic chunking, sliding windows, hierarchical parent-child trees) paired with high-performance embedding models (text-embedding-3, Voyage AI, BGE).
Hybrid Search & Reranking Integration
We implement dense vector search alongside sparse BM25 keyword matching, running cross-encoder reranking algorithms to ensure top-K retrieved contexts are strictly relevant.
Citation Enforcement & Hallucination Guardrails
We design system prompts and post-generation verifiers that enforce strict grounding: if the retrieved context does not answer the question, the system gracefully declines rather than hallucinating.
Production Deployment & Semantic Caching
We deploy high-availability API endpoints with Redis semantic caching, OpenTelemetry tracing, retrieval benchmark evaluation suites (RAGAS), and monitoring.
Project Deliverables
We hand over clean, production-verified software with zero third-party platform lock-in.
- ✔Production-ready RAG application repository with typed TypeScript or Python backend
- ✔Automated document ingestion, cleaning, and vector indexing microservices
- ✔Hybrid search (Dense Vector + BM25 Sparse) pipeline with Cohere or BGE reranking
- ✔Sub-100ms vector retrieval integration with Pinecone, Qdrant, or pgvector
- ✔Role-based access control (RBAC) ensuring granular document-level permission isolation
- ✔Semantic caching layer (Redis) to drastically reduce repeat token expenditures
- ✔Comprehensive automated evaluation suite using RAGAS for faithfullness and context precision
Security, Permissions & Cost Controls
A production RAG system must protect confidentiality and control infrastructure expenditure:
- • Granular Tenant Isolation: Namespace partitioning in vector databases to ensure customers never access another organization's vectors.
- • Document-Level Access Control (ACL): Dynamic metadata filters passed directly during similarity searches matching the querying user's auth token.
- • Semantic Caching: Storing recent query embeddings in Redis to serve identical questions at sub-10ms with 0 LLM token cost.
- • Verification Benchmarks: Automated continuous regression evaluations using synthetic test sets to track hallucination rates over time.
Complementary Services & Deep Dives
Frequently Asked Questions
What is Retrieval-Augmented Generation (RAG) and why do enterprises need it?
RAG is an architectural pattern that grounds Large Language Models with private enterprise knowledge bases (PDFs, SQL databases, internal wikis, CRM data) before generating an answer. Instead of training or fine-tuning an expensive model, RAG fetches the exact relevant document chunks in real time, injecting them into the context window so the model delivers factual, up-to-date responses with verifiable citations.
When should a business use RAG instead of model fine-tuning?
RAG is optimal when your proprietary data updates frequently (daily or hourly), when you require exact document citations, or when data access permissions must be enforced per user. Fine-tuning is better suited for teaching a model a specialized linguistic tone, niche domain vocabulary, or formatting style—not for static knowledge recall.
Which vector databases and search algorithms do you implement?
We implement managed and self-hosted vector databases based on latency, security, and scale requirements, including Pinecone, Qdrant, pgvector (PostgreSQL), Weaviate, and Milvus. We combine dense semantic embeddings with sparse BM25 lexical keyword matching (Hybrid Search) plus Cohere/BGE rerankers for top-tier retrieval accuracy.
How do you guarantee data privacy and compliance in RAG systems?
We engineer end-to-end encryption for document ingestion pipelines, enforce document-level Access Control Lists (ACLs) so users only retrieve records they are authorized to view, and configure enterprise LLM gateways that forbid data retention for third-party model training.
What factors influence the cost and latency of a production RAG pipeline?
Key cost and latency drivers include document volume and ingestion frequency, vector database sizing, embedding model compute, semantic caching coverage (which reduces repetitive LLM token costs by up to 60%), and reranking complexity. Typical search response latencies average under 100ms before streaming.
Build Production RAG Systems With CookMyTech
Talk directly with senior AI engineers to scope your vector pipeline, model selection, and document ingestion timeline.