The right choice when
- You have several AI ideas and need to know which is worth building first.
- A vendor or an internal team has proposed something and you want a second opinion.
- You are unsure whether to buy a tool or build your own.
AI consulting
A short, practical assessment of where AI would help your product or operation and where it would not. It is done by the engineers who would build it, and it ends in a written recommendation you can act on with us or with anyone else.
We look at the task, the people and the data as they are.
Feasibility, risk, rough cost and whether a ready-made tool would do.
Go, wait or do not build, with a scoped first step if it is a go.
“Do not build” is a valid outcome.
01 / Is this the one
Consulting is for the stage before a build. If you already know what you want, skip it.
02 / What the work covers
The output is a short written report. It includes the ideas we advise against and why.
Conversations with the people who do the work, and a look at the data and systems involved.
Each idea rated for value, feasibility and risk, including the ones we would drop.
Where an existing product would do the job, we say so and describe the kind of tool to look for.
For the strongest case: scope, success measure, data needed, a cost range and what would make us stop.
03 / Limits and risks
Some problems are better solved with ordinary software or a change of process. If so, the report says that.
Whether a model performs well enough on your data is only proven by testing it on your data.
Costs and timelines at this stage are indicative. A fixed quote needs an agreed scope.
04 / Built and operated by NSDBytes
Our own AI product. India market news as short briefings on Telegram and Web Chat, each story linked to the listed company, commodity, index or currency it concerns. It shows that the people advising you build and operate an AI product themselves.
https://
Add this address to Claude, Cursor or any MCP client.
search_stories · One read-only toolListed in
A client project. FocusPulse is one product in one field, and we have no published case study for a consulting engagement.
05 / What to do next
The first step is a free scoping call. Bring the AI ideas on your list, however rough. We agree which workflows to look at and send a written estimate.
A little clarity
A short written report: the use cases we looked at, which to pursue and which to drop, the data and systems each one needs, the main risks and a plan for a first pilot. It is yours to use with us or with another team.
No. The recommendation is written so that any competent team could act on it. If we think a ready-made product would serve you better than a custom build, the report says so.
No. Finding out what state the data is in is part of the work. Poor or scattered data is a common reason an AI idea is not ready, and it is better to learn that before a build than during one.
We have no published client case study for a consulting engagement, and we would rather say so. The advice comes from engineers who build and operate FocusPulse, our own AI product, and who build software for sectors such as hospitality and agriculture.
It depends on how many workflows are in scope. The price is agreed after a free scoping call. If you already have a build in mind, the project cost estimator gives a range for it without a call.
Start wherever you are
We’ll help you sort the ones worth building from the ones worth dropping, and say so plainly.