RAG Solutions & Development
Your knowledge base is a goldmine.
Most teams cannot access it accurately.
A RAG system that hallucinates, retrieves the wrong documents, or times out under load is worse than no AI at all. We build RAG pipelines that retrieve accurately, cite truthfully, and operate reliably in production — across your PDFs, databases, APIs, and knowledge systems.
Discuss Your RAG Project →End-to-End RAG Pipeline Development
RAG failures are almost always architectural. The most common mistakes: poor chunking strategies that split context at the wrong boundaries, embedding models mismatched to the domain, and retrieval pipelines that prioritise similarity score over actual relevance. We have seen these failures in production. We design to avoid them from the start.
Our RAG engagements begin with an audit of your source documents and query patterns. We design the ingestion pipeline, select the right chunking strategy for your content type, choose or fine-tune an embedding model, and set up the vector database with proper indexing for your scale requirements.
Retrieval optimisation is where significant quality gains happen after the initial build. We implement hybrid search (combining dense and sparse retrieval), query rewriting, and reranking to push accuracy from acceptable to excellent. We measure everything with RAGAS and custom evaluation datasets.
We build RAG systems for use cases including internal knowledge bases, customer support automation, document Q&A, compliance and legal research tools, and technical documentation search — across regulated and unregulated industries.
What We Deliver
Every component of a production RAG system.
Document Ingestion Pipelines
Automated ingestion from PDFs, Word files, databases, APIs, and web sources. Handles format conversion, deduplication, and version tracking at scale.
Chunking Strategy Design
Fixed-size, semantic, and document-aware chunking strategies tailored to your content type. Chunking decisions directly impact retrieval quality — we test and tune for your data.
Embedding Model Selection
Evaluation and selection of the right embedding model (OpenAI, Cohere, local Sentence Transformers) for your domain, language, and latency requirements.
Vector Database Setup
Deployment and configuration of Pinecone, Weaviate, ChromaDB, pgvector, or Qdrant — with index design, filtering strategies, and scaling plans.
Retrieval Optimisation
Hybrid search (dense + sparse), MMR for diversity, query rewriting, and reranking with Cohere or cross-encoders to improve recall and precision.
RAG Evaluation & Quality Assurance
Systematic evaluation using RAGAS and custom metrics — faithfulness, answer relevancy, context precision. Continuous monitoring for quality regression in production.
Technologies We Work With
We select the stack that fits your scale, budget, and data residency requirements.
Common Questions
What is Retrieval Augmented Generation and when do we need it?
RAG connects an LLM to your own knowledge base so it can answer questions using your documents, not just its training data. You need RAG when your use case requires up-to-date information, proprietary data, or accurate citations — and when hallucination from a base LLM is a business risk.
How does RAG differ from fine-tuning for knowledge injection?
Fine-tuning bakes knowledge into model weights — it is expensive, hard to update, and can degrade general capabilities. RAG retrieves knowledge at inference time from a live index — it is cheaper to update, traceable, and auditable. For most enterprise knowledge base use cases, RAG is the right choice. We help you decide which approach fits your requirements.
What accuracy can we expect from a RAG system?
A well-built RAG system on clean, well-structured documents typically achieves 80–90% answer accuracy on in-domain questions as measured by RAGAS. Accuracy depends heavily on chunking strategy, retrieval quality, and document quality. We build evaluation frameworks so you can measure this continuously — not just at launch.
Can RAG work with our internal documents in multiple formats?
Yes. We build ingestion pipelines that handle PDFs, Word documents, Excel sheets, web pages, databases, and API sources. Format conversion, layout preservation, and table extraction are handled as part of the pipeline design.
How do you handle multi-language or multi-domain document collections?
We select embedding models that support your language requirements (multilingual-e5, Cohere multilingual, etc.) and can segment indices by domain when needed. Cross-lingual retrieval and domain routing are solvable problems with the right architecture.
Ready to build a RAG system that actually works?
Tell us about your documents and use case. We will design the architecture together.
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