Services
AI & LLM Systems
Retrieval, agents, and the data layer underneath them.
Most teams reach for an LLM API and discover the hard part is everything around it: getting the right context in front of the model, proving retrieval quality did not regress, and keeping cost and latency inside a budget.
That work is infrastructure, and infrastructure is what we do. Our open source includes embedded database engines with vector, full-text, and graph indexes — so when a retrieval layer misbehaves, we debug it rather than swap providers and hope.
What this covers
- →RAG pipelines — ingestion, chunking, embedding, retrieval, reranking
- →Evaluation harnesses that catch retrieval regressions before users do
- →Agent systems and tool orchestration
- →Vector search on pgvector, Qdrant, and Cloudflare Vectorize
- →LLM cost and latency optimization, caching, and rate-limit management
Stacks we use for this
- Python / FastAPI
- TypeScript
- Go
- Rust
Common questions
How long does a production RAG system take to build?
A focused pipeline with evaluation coverage is typically eight to fourteen weeks, based on our own delivered projects rather than published industry data, depending on how many source systems feed it and how clean that data is. Ingestion and data quality consume more of that schedule than the retrieval layer does.
Do we need to fine-tune a model?
Usually not. Most retrieval quality problems are chunking, ranking, and evaluation problems, and fine-tuning is an expensive way to work around them. We establish measurement first and only recommend training when the numbers justify it.
Have something hard to build?
Tell us what you are working on. If we are not the right people for it, we will say so and point you somewhere better.
Book a technical call