Grounding Strategy & Architecture Selection
We assess your data and question types and choose the right pattern - advanced RAG, GraphRAG, agentic retrieval, long-context, or a blend - instead of forcing one approach.
RAG & Knowledge Retrieval Systems
We build retrieval-augmented generation systems - from hybrid search to GraphRAG and agentic retrieval - that ground language models in your data so they answer from reality, not guesswork.
How We Build Production-Grade RAG
Most RAG demos break in production. We engineer every stage - ingestion, chunking, hybrid search, reranking, evaluation, and integration - and pick the right architecture (advanced RAG, GraphRAG, or agentic retrieval) for your data and questions, so the system is accurate, observable, and affordable at scale.
Data Ingestion & Smart Chunking
We turn your documents, databases, and systems into a clean, structured knowledge base with semantic, structure-aware chunking and rich metadata.
Hybrid Search & Reranking
We combine vector and keyword search and add cross-encoder reranking so the model receives the truly relevant context - catching both meaning and exact terms.
GraphRAG & Agentic Retrieval
For multi-hop and research-grade questions, we build knowledge graphs and agentic retrieval loops that connect facts across your whole corpus.
Evaluation, Guardrails & Observability
We ship automated evals, grounding guardrails, and dashboards for accuracy, latency, and cost per query - so the system stays trustworthy after every change.
Integration & Cost Optimization
We integrate retrieval into your real products and control spend with caching, routing, and right-sized models - production value, not a demo.
Grounded AI, Shipped
Retrieval and RAG systems we've built into production for our clients.
