AI Engineering · Retrieval-Augmented Generation (RAG) Retrieval, engineered.
A model only knows what it has seen. Retrieval-augmented generation (RAG) teaches it the rest of your business — in real time, on the right data, with the right governance. We engineer retrieval systems that survive production: indexed against ground truth, evaluated continuously, and accountable for every answer and the source behind it.
Why it matters
RAG is a discipline, not a feature.
Most RAG demos work on a hundred documents and break on a hundred thousand. Our work begins where the demo ends.
- 01Chunking discipline — how content is split decides what can ever be retrieved
- 02Hybrid retrieval — the right answer is rarely in one channel
- 03Citation fidelity and freshness windows — answers accountable to their source and the moment it came from
- 04An evaluation harness that catches drift before your users do
What we engineer
Retrieval patterns that hold at scale.
Hybrid retrieval
Vector, keyword and structured retrieval, scored together. The right answer is rarely in one channel.
Agentic RAG
The agent decides what to retrieve, when, and how to use it — with multi-hop retrieval, query rewriting and self-critique built in.
GraphRAG
For domains where relationships matter more than text, with knowledge graphs as first-class context.
Citation-grounded answers
Every claim traceable to a source document, paragraph and timestamp. An audit trail by design.
RAG evaluation
Faithfulness, answer relevance, context precision and context recall — scored on every change.
The stack we use
Model-agnostic. Deployment-flexible.
We build on what you already pay for, or on what best fits your constraints.
Vector stores
- Pinecone
- Milvus
- Weaviate
- Qdrant
- pgvector
- OpenSearch
Hybrid search and reranking
- BM25 + dense
- Cohere reranker
- BGE-reranker
Knowledge graphs
- Neo4j
- Neptune
- Memgraph
Frontier models
- Claude
- GPT
- Gemini
Open-weight models
- Qwen
- Kimi
- DeepSeek
- Llama
Embeddings
- OpenAI
- Cohere
- BGE
- NV-Embed
- Custom domain-tuned
Orchestration
- LangChain
- LlamaIndex
- Custom orchestrators
- MCP-native tool integration
- Caching, streaming and fallbacks
Evaluation
- Ragas
- Custom scorer frameworks
- LLM-as-a-Judge
- Continuous evaluation tied to deployment
The question we hear most
RAG or fine-tuning: when to use each.
Most enterprise systems need both.
The knowledge changes
- Use fine-tuning when…
- The response style needs to change
Sources need to be cited
- Use fine-tuning when…
- The response format needs to change
Answers must be grounded in your data
- Use fine-tuning when…
- —
Where it fits
The engineering discipline behind most of what we ship.
RAG powers conversational analytics, knowledge assistants, customer-support agents, code search, compliance review and internal copilots that must answer with citations. It drives semantic-layer-aware retrieval in AI-Powered BI, grounds every action our Future of Work co-workers take, ships through the GenAI Delivery Factory, and is scored by Agent Evaluations — retrieval and generation, separately and together.
Related guides
Keep reading.
AI Engineering · 6 min
RAG vs Fine-Tuning
When to use retrieval augmented generation versus fine-tuning for enterprise LLM applications.
Read the guide
Data Engineering · 8 min
Vector Database for the Enterprise.
Pinecone, Milvus, Weaviate, Qdrant, pgvector, OpenSearch — the trade-offs that actually matter for production RAG in India.
Read the guide
Data Engineering · 9 min
Text-to-SQL Needs a Semantic Layer.
Why naive natural-language-to-SQL fails on enterprise data — and how the semantic layer makes conversational analytics actually trustworthy.
Read the guideNext step
We are an intent away.
Bring the corpus your demo broke on. We’ll show you how hybrid retrieval, citation-grounded answers and continuous evaluation hold up on your data.