Overview
The difference between an AI product that impresses in a demo and one that earns enterprise customers is data. We design and implement the data infrastructure — ingestion, processing, vector stores, and retrieval pipelines — that makes your AI accurate, fast, and genuinely useful at scale.
Why this matters
AI quality is a data problem, not a model problem. The gap between GPT-4-with-your-data and the out-of-the-box model is often the difference between a demo that wins deals and a pilot that stalls. Proper RAG, evaluation frameworks, and embedding strategy is the moat.
How we run it
Data Audit
What data do you have, what do you need, what can you legally use? We map sources, licensing, quality, and freshness.
Retrieval Architecture
Chunking strategy, embedding model selection (OpenAI, Cohere, open-source), vector store (Pinecone, Milvus, Weaviate), and reranking pipelines.
Evaluation Harness
We build an evaluation dataset from real queries. No more 'it feels better' — we measure precision, recall, and citation accuracy.
Production Pipeline
Real-time and batch ingestion, freshness monitoring, cost tracking, and a rollback path when embeddings drift.
What you get
- Data audit — what you have, what you need, and what you can use
- Retrieval-Augmented Generation (RAG) pipeline design and build
- Vector database selection and optimization
- Embedding model selection and fine-tuning strategy
- Real-time and batch data ingestion pipelines
- Data quality monitoring and refresh cadence
Our technology choice
We're vendor-neutral on vector DBs and embedding models — we pick based on your data residency, scale, and cost constraints. LangChain and LangGraph for agent orchestration where multi-step reasoning matters. Straight retrieval + prompting for everything else.