Fits
When yes.
- Company with volumetric internal data above one million records.
- A qualified repetitive process consuming senior people's hours.
- Leadership with the judgment to say "no, if it doesn't work" after the PoC.
AI consulting applied to the business.
Use cases identified in the business's real data. A measurable 4-week proof of concept before committing to a platform or a large budget.
Most AI projects in mid-sized companies fail because they start with the tech instead of the case. A model gets hired, tested on a screen and abandoned because internal data is dirty, there's no business integration, and no "before" was measured. Useful consulting identifies the case, measures the baseline and validates impact before committing to a platform or a large budget.
Data and process diagnosis.
Inventory of internal data, quality and volume, repetitive processes consuming senior people's hours, and real model fit — before touching anything.
Scoped proof of concept with a target metric.
4 weeks, one use case, one target metric measured before and after, written deliverable. Success criteria closed before we start.
Integration with internal systems.
The model connects to the ERP, CRM, ops software or the database where the real case lives. It doesn't stop at a demo screen.
Deployment with team training and post-launch monitoring.
Phased deployment, training for the team that uses it daily, and output quality control for the first months. No abandoned models.
Doesn't fit
When no.
- Generative AI is wanted as an internal or marketing demo.
- Data is not digitized or lives on paper and loose sheets.
- The goal is brand visibility, not real operational impact.