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.
Artificial intelligence consulting for businesses.
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.
Many 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.
What do you deliver in a 4-week proof of concept?
A concrete use case executed on the client's real data, with a target metric measured before and after, a written report with the result and a clear decision: continue with deployment, adjust scope, or close. The PoC has a deliverable and success criteria agreed in writing before we start.
What's the difference between generative AI and classic automation?
Classic automation executes rules a person defined. Generative AI produces text, images or decisions from learned patterns. In mid-sized companies most repetitive processes are better solved with classic automation or small purpose-built models, not with generative AI. Generative AI adds value when there is unstructured natural language and enough volume.
Can I run the models on my own infrastructure?
It depends on the model and the use case. Open small or mid-size models can be deployed on your own infrastructure or a private cloud. Large proprietary models are accessed only by API. Decided case by case: cost, latency, data sensitivity and update control all weigh in.
How much does a proof of concept cost?
A PoC scoped to 4 weeks starts from a range closed in writing before signing, depending on data access and the target metric. Cost agreed before we start, no scope expansion mid-project.
What happens if the PoC fails?
You still get the written report with evidence of why it didn't work (data quality, volume size, model fit, biases, etc.) and a clear recommendation. The PoC has value whether it succeeds or fails: 'it doesn't work yet' is a valid result and is billed as such.