Putting AI into production, safely
From a workflow that works in a demo to one your team can rely on. We start from the work itself, measure it before launch, and stay accountable for how it runs.
How we put AI into production
Start from the workflow
Not from the model. We map the work as it happens today, where time goes, and where a mistake would hurt.
Measure it with evals
An evaluation set built from real examples of the work, run before launch and after every change.
Guardrails and human review
Limits on what the system can see and do, and a person in the loop wherever the stakes are high.
Own the run
Monitoring, cost and quality tracked after launch, with someone accountable for the next change.
Ways we work on AI
AI workflows
AI added to the work you already do, across engineering, sales, product and operations, with evaluation and monitoring built in.
Explore serviceAI-native teams
Products designed around AI, and the engineering practices, tooling and guardrails that let your own team build with it.
Explore serviceSystems integration & FDE
A forward-deployed engineer inside your team to integrate AI into your systems and ship it, working to your priorities.
Explore serviceAI readiness & workflow sprint
A fixed-scope sprint to find the workflows worth automating first, with a working pilot and a rollout plan.
View packageSecurity for AI features
AI features bring their own risks: instructions hidden in the content they read, sensitive data showing up in answers, output passed straight into other systems, and agents with more access than they need. We cover them as part of our application security work.
LLM application security review
Reviewed against the OWASP Top 10 for LLM applications: prompt injection, sensitive information disclosure, improper output handling, excessive agency and more.
AI usage policy
Which AI tools your team may use, what data may go into them, and which outputs need a person to check them.
Model and vendor risk review
How each AI provider handles your data, and your fallback if a model changes or is withdrawn.
For the rules that apply to AI systems, see AI regulation and data protection readiness.
Common questions about AI in production
Where should a business start with AI?
With one workflow that is repetitive, well understood and easy to check, such as drafting, sorting or extracting information. Pick something where a mistake is caught before it reaches a customer, prove it works, then move to the next.
How do you test an AI system before launch?
We build an evaluation set from real examples of the work, agree what a good result looks like, and measure the system against it before launch and after every change. If it doesn't meet the bar, it doesn't ship.
Do we need our own AI model?
Rarely at the start. Most business workflows can run on an existing model plus your own data, instructions and checks. We recommend training or fine-tuning only when the evaluations show it is needed.
How is our data protected?
We agree up front what data the system may see, keep it to the minimum the workflow needs, and check how each AI provider stores and uses it. Access, logging and retention follow the data protection laws that apply to you.
Who runs the system after launch?
Either your team, with the monitoring, documentation and runbook we hand over, or us, under a Run engagement. Either way someone owns the cost, the quality and the next change.
Start with one workflow
Tell us which piece of work you'd like AI to help with. We'll tell you whether it's a good fit, what it would take, and how we'd know it's working.