RAG Implementation Services
Custom AI Chatbots & Knowledge Bases on Your Data
RAG (Retrieval-Augmented Generation) grounds your LLM on your own data (PDFs, databases, documentation, knowledge bases) so it answers from your information, not from the internet.
Brillnex Systems builds production RAG pipelines with hybrid search, re-ranking, access controls, and observability. As of 2026, RAG is the standard approach for enterprise AI knowledge systems, and we have implemented it across SaaS products in the US, UK, and Australia.
TL;DR
RAG = your data + a vector database + an LLM. Instead of a model that only knows its training data, you get one that answers from your documents, database, or knowledge base. Brillnex builds the full pipeline: ingestion, embedding, vector store, hybrid retrieval, re-ranking, and generation, built for production reliability. Offshore rates, 2–16 week delivery.
What RAG Implementation Includes
Full-stack RAG pipeline delivery, from document ingestion to production observability:
- Document ingestion pipeline: PDF, DOCX, web URLs, databases, APIs
- Chunking strategy design: semantic, recursive, or fixed chunking
- Embedding model selection and setup: OpenAI, Cohere, or open-source
- Vector database configuration: Pinecone, pgvector, Weaviate, or Qdrant
- Hybrid search: vector similarity + BM25 keyword search combined
- Re-ranking layer for precision improvement (Cohere Rerank, cross-encoders)
- Query transformation: HyDE, multi-query, and step-back prompting
- Access control: users only retrieve documents they are authorised to see
- RAG evaluation pipeline: measuring retrieval accuracy and answer faithfulness
- Production observability: tracing retrievals, monitoring latency, logging queries
Why Choose Brillnex for RAG?
Hybrid search by default
We build hybrid retrieval (vector + keyword) as standard, at no extra cost. Pure vector search misses exact-match queries on product names, IDs, and technical terms. Hybrid search is more accurate for real enterprise data.
Evaluated, not just built
We set up RAG evaluation frameworks (RAGAS, TruLens) so you can measure retrieval accuracy and answer faithfulness instead of assuming the system works.
Offshore pricing
A RAG system that would cost $30k–$50k at a US AI agency costs $15k–$30k with Brillnex. Same senior engineers, same quality, at offshore rates.
Frequently Asked Questions
What is RAG (Retrieval-Augmented Generation)?
RAG is a technique that connects a large language model to an external knowledge base. Instead of relying solely on the model's training data, RAG retrieves relevant documents from your own data source at query time and feeds them into the LLM as context. The result is an LLM that answers questions based on your specific, current data rather than general internet training.
What is the difference between RAG and fine-tuning?
Fine-tuning modifies the model's weights by training it on your data. RAG retrieves your data at inference time and injects it as context; the model itself is unchanged. For most enterprise use cases, RAG is preferable: it is easier to update, more auditable, and significantly cheaper.
Which vector databases do you work with?
We work with Pinecone, pgvector (PostgreSQL), Weaviate, Qdrant, and Chroma. For most production deployments we recommend pgvector if you are already on PostgreSQL, or Pinecone for very large knowledge bases requiring managed scaling.
What is hybrid search in RAG?
Hybrid search combines semantic vector search (finding documents by meaning) with keyword full-text search (finding documents by exact terms). This produces significantly more accurate retrieval, especially for enterprise data with structured terminology like product names, IDs, or technical terms.
How much does a RAG implementation cost?
A RAG implementation at Brillnex costs $15,000–$30,000 depending on scope: a basic pipeline at the lower end, an enterprise system with hybrid search, re-ranking, and access controls at the higher end. The equivalent runs $30,000–$50,000+ at a US agency. Offshore pricing makes it 40–60% less, and we scope every engagement with fixed pricing before we start.
How long does RAG implementation take?
A basic RAG pipeline takes 2–4 weeks. An enterprise RAG system with multiple data sources, hybrid search, re-ranking, access controls, and observability takes 8–16 weeks. We scope every engagement before quoting a timeline.
Can you build a custom AI chatbot trained on our own data?
Yes. A custom AI chatbot is one of the most common things we build with RAG: the chatbot retrieves answers from your documents, help centre, database or website instead of guessing, cites its sources, and only shows each user content they are allowed to see. For EU deployments we build in the AI disclosure that the EU AI Act requires for chatbots from August 2026. If you want a ready-made chatbot for your website rather than a custom build, ConGreeto, the AI chatbot product we built and operate, is the faster route.
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Ready to build your RAG system?
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