Most failed AI projects are not model failures. The model was fine; the data feeding it was late, incomplete, or quietly wrong, and nobody noticed until the forecast embarrassed someone in a meeting.
Because we build the platform layer as well, the AI we ship stands on pipelines we can vouch for — and lands in production with monitoring, not in a notebook with a screenshot.
Predictive and generative work
Demand forecasting, churn and propensity models, anomaly detection, and LLM applications over your own governed knowledge — assistants that answer from your documents, classification and extraction over messy inputs, and retrieval that respects your existing access controls.
Intelligent automation
The best automation candidates are the tasks your team does every week and dreads: assembling the same report, reconciling two systems, moving and validating files, chasing status. We map those workflows, automate them with orchestrated pipelines in Airflow or Temporal, and add AI-driven steps only where judgment is genuinely needed — classification, extraction, summarization — rather than everywhere it would sound impressive.
What you get
- Use-case selection driven by measurable impact, with the weak ideas killed early
- Models and automations deployed to production with monitoring and alerting
- AI steps used where they earn their cost, and plain code where they don’t
- Documentation and handover, so your team owns what we built
Once these are live, AI Harness Development keeps them from regressing, and AI Adoption Measurement proves what they returned.
Ready to talk it through?
Tell us where your data hurts — we'll tell you honestly whether we can help.
Contact Us