Practical AI and machine learning, pointed at real outcomes.
AI and machine learning, including the newer generative tools, aimed at automation and faster decisions. We build the things that pay off, not science projects.
What it is
The question with AI is not whether it is impressive, it is whether it ships, holds up in production, and pays for itself. We start from a real outcome, build on your own data with clear guardrails, keep a person in the loop where it matters, and measure the result. The unglamorous data and security groundwork is what makes the difference between a demo and a system you can trust.
Outcomes
- Work that used to take hours done in minutes
- Decisions backed by data, available when they are needed
- AI in production, governed and measurable, not a demo
The sub-services in the AI practice.
Use-case discovery
Find the few opportunities worth doing, and a realistic roadmap to do them.
Generative AI and assistants
Retrieval and assistants built on your own data, with citations and guardrails.
Machine learning
Models and the pipelines to train, serve, and monitor them in production.
Process automation
Take slow, manual, error-prone work off people's plates, safely.
MLOps
The operational backbone: evaluation, deployment, monitoring, and rollback.
Governance and safety
Controls, evaluation, and human review so AI stays accountable.
How we deliver.
- 01
Pick outcomes worth the effort, and rule out the ones that are not.
- 02
Build on your data with guardrails and a human in the loop.
- 03
Measure accuracy and value, not vibes.
- 04
Operate and improve, with monitoring and clear rollback.
Fluent across your stack.
We pick the right tool for the job and stay fluent across the ones below. If your stack is not listed, we have almost certainly worked with something close.
The full ecosystemAI in the real world.
Turning support tickets into answers, and cutting resolution time 41%
Halcyon's support team was drowning in tickets. We built a practical AI assistant on their own data, governed and measurable.
A governed clinical data platform that passed audit with zero critical findings
Meridian wanted to unify clinical and operational data for new patient-facing features, without putting protected health information at risk.
About the AI practice.
Is our data safe with generative AI?
How do you stop AI from making things up?
What if we are not sure AI is worth it?
Stronger together than apart.
Let us take AI off your plate.
One team, one bill, one point of accountability across cloud, data, AI, security, and software. Tell us where you are.