Services
Applied AI
Retrieval, embeddings, and language-model features built into software people actually use.
We treat a model as one component in a larger system, not the whole product. The interesting work is usually around it: getting the right context to it, handling the cases where it is wrong, and measuring whether it genuinely helps before shipping.
We have built this from the inside out with inode, a knowledge base that retrieves by meaning, so we know where retrieval quality, latency, and cost actually bite.
What this involves
Retrieval and semantic search
Embeddings, vector search, and ranking that find the right thing even when the words do not match. We tune for precision on real queries, not benchmark scores.
Agents and pipelines
Multi-step flows that call tools, with clear boundaries so a wrong step fails safely instead of cascading, and a human stays in the loop where it matters.
Honest evaluation
We measure whether a feature helps before it ships, and we are upfront about what the model can and cannot do.