Assistants and agents
Assistants that draft, answer and act inside the tools your teams already use, with a human in the loop where it matters.
WorkflowsApplied AI
Applied AI that earns its place.
We put applied AI to work in your operations: assistants, search over your own data and intelligent automation, integrated into your existing systems and measured against the way things worked before.
What we build
Use cases that survive the meeting
No demos that die after the presentation. Each of these goes into production, inside your systems, with someone accountable for the result.
Search over your own data
Ask questions of your documents, tickets and knowledge bases, and get answers that cite where they came from.
RAGIntelligent automation
Documents read, sorted and routed automatically, so people spend their time on the cases that need judgement.
OperationsEvaluation and guardrails
Test sets, quality metrics and monitoring that tell you whether the model is still doing its job next month.
QualityAI usage policy for your teams
Clear rules on which tools, which data and which decisions, so adoption is fast and safe at the same time.
GovernanceTraining and adoption
Hands-on sessions built around your own workflows, so the tools actually get used after we leave.
PeopleFrom idea to production
Measured before, measured after
- 1PickWe find the use cases where a model clearly beats the old way, and drop the rest.
- 2BaselineWe measure how the work is done today, so improvement is a number, not a feeling.
- 3IntegrateThe model goes into your existing systems and permissions, not a separate demo app.
- 4GovernEvaluation, monitoring and a usage policy keep it honest after launch.
Our rules
AI is a tool, not the product
- Your data stays yoursWe work inside your environment and your access rules.
- Every answer is traceableSources are cited, decisions are logged.
- A human stays accountableThe model proposes; your team decides where it matters.
- If it does not beat the baseline, it does not shipWe would rather tell you early.
Questions
Frequently asked questions
How do you choose which AI use cases to build?
We look for the work where a model clearly beats the current way of doing it, measure how that work is done today, and drop the use cases that do not pass. What ships is what beats the baseline.
Can AI work with our existing systems?
Yes. That is the point. The model goes into your existing tools, data and permissions rather than a separate demo app, so people use it where they already work.
What happens to our data?
Your data stays yours. We work inside your environment and your access rules, answers cite their sources and decisions are logged.
How do you know the AI is still working after launch?
With evaluation and monitoring: test sets, quality metrics and alerts that show whether the model is still doing its job next month, not only on launch day.
Do you help our teams adopt AI?
Yes. We write an AI usage policy with you, covering which tools, which data and which decisions, and run hands-on sessions built around your own workflows.
Have a process AI could improve?