Nobody should sign a six-figure AI project on the strength of a demo. The right first step is a feasibility study: a short, fixed-scope, fixed-fee engagement that answers one question with data. Is the signature of the problem you want to solve actually present in the signal you can afford to measure, in your environment? Here is exactly what ours looks like, so you can compare it with what anyone else offers.
What goes in
- Your problem statement, refined together into a measurable question. "Reduce scrap" becomes "detect condition X on machine Y at least Z minutes before it causes a reject."
- Existing data if you have it: recordings, PLC logs, inspection results, old pilot data. Stalled projects often hide a usable dataset.
- Or a short measurement campaign: one to three days on site with a portable multi-modal unit (audio, ultrasound, vibration, vision, environmental) capturing known-good and known-bad conditions.
What we do with it
- Signal inspection and time-frequency analysis to see whether the phenomenon is visible at all, and in which channel.
- Quick baselines: simple, transparent methods first. If a threshold on a spectral band already works, we will say so.
- A first learned model, evaluated honestly with held-out data, to estimate what a full project could reach.
- An assessment of the practical path: sensor placement, edge hardware class, integration points, data governance constraints.
What comes out
A written report your engineers and your management can both read. It contains the evidence (plots, numbers, what worked and what did not), a clear recommendation with a go / no-go, and, if go, a scoped proposal for the proof of concept with milestones, duration and the metrics we would commit to. If the answer is no-go, you have spent days, not months, and you know why.
A feasibility study that always says "go" is a sales tool. Ours says no-go when the physics says no-go.
Duration, cost and logistics
Two to ten days of engineering time, fixed scope, fixed fee, quoted after a free 30 to 45 minute discovery call. Prices are not on the website because the scope varies; the quote is transparent and itemised. For Indian plants, analysis happens remotely, the measurement campaign on site if needed, and calls sit in the 12:30 to 16:30 IST window. NDAs are standard; anonymised or synthetic samples are fine for the first conversation.
If you have a stalled project, a stubborn defect, or a vendor solution that did not meet the goal, this is the cheapest way to find out what is actually possible.
Key takeaways
- A feasibility study answers one question with data: is the signature present in a signal you can afford to measure?
- Inputs: a measurable problem statement plus existing data or a short measurement campaign.
- Outputs: a readable report, an honest go / no-go and, if go, a scoped PoC proposal.
- Two to ten days, fixed scope and fee, quoted after a free discovery call.
- The right first step for stalled projects and stubborn defects.
If any of this matches a problem on your line, the fastest way to find out what is possible is a free discovery call followed, where it makes sense, by a feasibility study of two to ten days.