What we build
- LLM-powered chat, search, summarization, and copilots
- Retrieval-Augmented Generation (RAG) over your data
- Agentic workflows with tool use and human-in-the-loop
- Computer vision: detection, OCR, image generation pipelines
- Custom model fine-tuning, evaluations, and red-teaming
- ML pipelines: training, serving, monitoring, drift detection
- Vector databases, embeddings, semantic search
- Cost optimization: model routing, caching, batching
Stack we use
How we de-risk AI projects
Frame
We start with the user problem and the success metric — not the model.
Prototype
A working demo on real data within 2 weeks. Cheap to throw away if it's wrong.
Evaluate
Quantitative evals and red-team tests so you know what "good" means.
Scale
Productionize with caching, observability, fallbacks, and cost controls.
Frequently asked
Will my data leave our environment?
It doesn't have to. We can deploy to your cloud (AWS Bedrock, Azure OpenAI, GCP Vertex) or self-hosted models so your data never touches a third-party API.
Do we need to fine-tune a model?
Usually not. Most teams overestimate the need for fine-tuning. Smart prompting, RAG, and well-designed tool use solve 80% of cases at a fraction of the cost. We'll tell you if you're in the 20%.
How do you measure if the AI is actually working?
Custom eval sets, golden datasets, A/B tests against the existing flow, and ongoing telemetry. "Vibes-based" AI development is how projects fail in production.