What we build
Most RAG demos work on a hundred documents and break on a hundred thousand. Our work begins where demos end — chunking discipline, hybrid retrieval, citation fidelity, freshness windows, and the evaluation harness that catches drift before users do.
- Hybrid retrieval — vector + keyword + structured, scored together. The right answer is rarely in one channel.
- Agentic RAG — the agent decides what to retrieve, when, and how to use it. Multi-hop, query rewriting, self-critique built in.
- GraphRAG — for domains where relationships matter more than text. Knowledge graphs as first-class context.
- Citation-grounded answers — every claim traceable to a source document, paragraph, and timestamp. Audit trail by design.
- RAG evaluation — faithfulness, answer relevance, context precision, context recall — scored on every change.
The stack we use
Model-agnostic, deployment-flexible. We build on what you already pay for, or what fits your constraints best.
Retrieval
- Vector stores: Pinecone, Milvus, Weaviate, Qdrant, pgvector, OpenSearch
- Hybrid search with BM25 + dense + reranking (Cohere, BGE-reranker)
- Knowledge graphs: Neo4j, Neptune, Memgraph
Generation
- Frontier: Claude, GPT, Gemini
- Open-weight: Qwen, Kimi, DeepSeek, Llama
- Embeddings: OpenAI, Cohere, BGE, NV-Embed, custom domain-tuned
Orchestration
- LangChain, LlamaIndex, custom orchestrators
- MCP-native tool integration
- Caching, streaming, fallbacks built in
Evaluation
- Ragas, custom scorer frameworks, LLM-as-Judge
- Faithfulness, answer relevance, context precision/recall
- Continuous evaluation tied to deployment
RAG vs fine-tuning — the question we hear most
Use RAG when the knowledge changes, when sources need to be cited, or when answers must be grounded in your data. Use fine-tuning when the style or format of the response is what needs to change. Most enterprise systems need both. Read the full decision guide →
Where it fits
RAG is the engineering discipline behind most of the AI applications we ship — conversational analytics, knowledge assistants, customer-support agents, code search, compliance review, and the kind of internal copilots that need to answer with citations.
- AI-Powered BI — conversational analytics over your live data, with semantic-layer-aware retrieval.
- Future of Work — AI coworkers that ground every action in retrieved context.
- GenAI Delivery Factory — the SDLC we ship RAG systems through.
- Agent Evaluations — how we score the retrieval and the generation, separately and together.