Service
Artificial Intelligence
Learn · Reason · Assist
We build AI systems that hold up in production: retrieval-grounded assistants, machine-learning models, and evaluation harnesses that make quality measurable.
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The problem
Demos are easy. Production AI is engineering.
A model that impresses in a demo fails quietly in production: wrong answers with confidence, no evaluation loop, no grounding in your data, no controls. Production AI is a systems discipline, and that is how we treat it.
What we do
Capabilities
LLM applications
Assistants and copilots grounded in your data with retrieval, citations, and guardrails.
Machine learning
Forecasting, classification, and anomaly detection models with honest validation.
AI evaluation
Test sets, scoring harnesses, and regression gates so model quality is measured, not assumed.
AI strategy
Selecting the problems where AI earns its cost, and the architecture that fits your constraints.
How it works
The path from idea to operation
- 01
Frame
Define the task, the failure costs, and what measurable success means.
- 02
Ground
Connect models to your data with retrieval and structured context.
- 03
Evaluate
Build the evaluation harness before scaling usage.
- 04
Harden
Add guardrails, fallbacks, and monitoring for the failure modes that matter.
- 05
Iterate
Improve against the evaluation set with every model and prompt change.