Capabilities
What we actually build.
Each page below describes the work rather than the outcome, because by the time somebody is reading this they have usually had enough of the outcome version.
LLM integration
Putting a language model behind a real feature, with the evaluation, guardrails and cost controls that stop it embarrassing you in production.
Retrieval and semantic search
Search over your own documents that returns the right passage and cites where it came from, rather than a confident paragraph you cannot verify.
Agent and workflow pipelines
Multi step processes where a model calls real tools against real systems, built so a failure halfway through does not leave your data in pieces.
Model deployment and inference
Running models on your own infrastructure when data residency, unit cost or latency make a hosted API the wrong answer.
Data engineering for AI
The unglamorous work that decides whether anything above will function. Pipelines, quality, lineage and the schema nobody wrote down.
Tell us what you are trying to build.
Technical detail welcome. The more concrete the problem, the more useful the first reply.