Does this mean replacing our team with AI?

No. The model handles volume; a person handles the judgment calls. The goal is analyst time back, not fewer analysts making the calls that matter.

We tried an AI pilot before and it did not survive contact with real data. What is different here?

Most failed pilots skip the human-review layer and the audit trail, then lose trust the first time the model gets something wrong. We design for that case from the start, not after it happens.

RPA or a real API integration, which one do we need?

Depends on whether the systems involved expose a real API. Screen automation looks faster in a demo and breaks on the next vendor redesign; we will tell you honestly which one fits.

AI & automation

AI in production, with a human in the loop where it matters

Automation and AI built into the workflows you already run, with review where judgment matters and an audit trail on every decision.

Book a Two-Week Discovery

How we work

Bounded first workflow: one high-volume, well-defined step, measured before it widens
Human review where judgment or compliance matters, not blanket autonomy
Real API integration with your existing systems, not screen automation that breaks on the next redesign
An audit trail on every automated decision, for when someone has to explain it

Insights

Putting an LLM in production without giving it autonomy
Where the confidence threshold goes, what human review has to catch, and which decisions a language model should never be allowed to own in production.
Read
RPA or API integration: when screen automation becomes debt
Screen automation is fast to build and expensive to keep. A decision table for choosing between RPA and a real integration, and how to survive RPA.
Read
ETL or ELT for a mid-sized team, and how to tell which you need
ELT is the default advice and it is not always right. Where each pattern belongs, what the warehouse bill hides, and the constraint that decides it for you.
Read

Assessments

Frequently asked questions

Does this mean replacing our team with AI?: No. The model handles volume; a person handles the judgment calls. The goal is analyst time back, not fewer analysts making the calls that matter.
We tried an AI pilot before and it did not survive contact with real data. What is different here?: Most failed pilots skip the human-review layer and the audit trail, then lose trust the first time the model gets something wrong. We design for that case from the start, not after it happens.
RPA or a real API integration, which one do we need?: Depends on whether the systems involved expose a real API. Screen automation looks faster in a demo and breaks on the next vendor redesign; we will tell you honestly which one fits.

Buried in manual, repetitive work at volume?

Bring us the workflow. We will map where automation pays back fastest, and where a human still has to stay in the loop.