LLM-powered features and AI-native products built on data pipelines that hold up in production, not just in a demo.
We pick the right model for the job, and fine-tune or prompt-engineer it around your actual data and use case.
Ingestion, cleaning, and vector storage built to handle real traffic, not a one-off notebook.
Retrieval-augmented generation and tool-calling wired into your product so answers stay grounded in your data.
Structured evals and safety guardrails so the AI feature behaves predictably at scale.
Caching, batching, and model routing tuned so AI features stay fast and affordable as usage grows.
Visibility into how people actually use the AI feature, so you know what to improve next.