The full ML stack, engineered for production
Most AI projects never reach production. We close the gap between a clever prototype and a system you can rely on.
What we build
LLM applications
Assistants, copilots and document intelligence built on frontier models and tuned to your domain, with evaluation harnesses so quality is measured, not assumed.
RAG systems
Retrieval pipelines that ground answers in your own data: canonical segmentation, embedding and reranking, and LLM-as-judge validation against source passages.
AI agents
Autonomous agents that reason, plan and act across your tools, pausing for human approval on the steps that matter, with every decision logged and auditable.
Workflow automation
Agentic pipelines that absorb repetitive operational work end to end and integrate with the systems you already run.
Computer vision
Detection, tracking, pose estimation and longitudinal phenotyping from standard video, with no specialist hardware required.
Document intelligence
NLP and OCR pipelines for identification, segmentation, entity recognition and PII redaction across regulated, high-volume document flows.
MLOps and deployment
Reproducible training, evaluation and serving: in the cloud, in your region, or fully on-prem, with monitoring built in.
Strategy and advisory
Honest guidance on where AI delivers, where it doesn’t, and the fastest safe path from idea to production value.
Reliable, auditable and safe by default
A demo is easy. A system you can trust is not. Every capability above ships with the same engineering baseline.
- Evaluation harnesses, so quality is measured rather than assumed
- Answers grounded in your data, with citations back to the source
- Human approval on the steps that carry weight
- Every decision logged and auditable
- Deployed in the cloud, in your region, or fully on-prem
- Monitoring and retraining after launch
Not sure where AI fits your stack?
We will help you find the highest-value, lowest-risk place to start.