Systems that run in production, not demos.
AI & Machine Learning
Retrieval pipelines, document intelligence, agentic workflows, and model integration built to survive contact with real data and real users — with evaluation, cost controls, and failure handling designed in from the start.
The problem this solves
The pilot impressed everyone. Then it met production data, and nobody could say whether the output was right, what it cost, or what happens when the model changes.
What you get out of it
- Working systems with measurable accuracy, not proofs of concept
- Evaluation harnesses so quality regressions surface before users find them
- Cost and latency budgets that hold as volume grows
- Graceful degradation when a provider fails or a model is retired
What we actually hand over
- 01Architecture and model selection with an explicit cost model
- 02Production pipeline with logging, evaluation, and guardrails
- 03Human-review workflow where the stakes require one
- 04Runbook and handover, or ongoing operation under retainer
Scope, fees, and timelines are set in a Statement of Work under our Master Services Agreement. We do not guarantee ranking, traffic, or citation outcomes — anyone who does is guessing on your behalf. We do commit to a stated methodology, a baseline captured before the work, and re-measurement on the same instrument.