AI for industries
AI that knows the work. Built on software we’ve shipped in your sector.
Agents, retrieval and integrations applied to the workflows of industries we already build for — starting with one task your team does every day.
Industry figures as of September 2026. The MCP server is part of FocusPulse, which NSDBytes builds and operates.
01 / Where AI fits
One sector, one workflow worth changing.
Each line pairs a place AI can help with the software we have shipped in that industry — or, where we have not yet, how we would approach it. Open an industry to see more.
Night-audit and front-desk agents that reconcile PMS data and flag exceptions for staff to approve.
Our work: GMS, live at 9 of 10 MKL hotelsRetrieval over approved clinical and operational documents, with sources shown and patient-data boundaries set first.
Our work: Shiv Jyoti Eye Hospital, BoltAssistants that answer partner-store and farmer questions from the product catalogue and agronomy content.
Our work: UNIVIA, 2,000+ partner storesReconciliation and exception review where the model proposes and a person approves — never unsupervised money movement.
How we approach itException agents that watch shipment and handoff events and escalate before a delay becomes a missed delivery.
Our work: UNIVIA’s three-app supply chainCourse and tutoring assistants grounded in the institution’s own material, with teachers able to review answers.
Our work: Multi-role learning platformDocument extraction and checking support for property records, with every field traceable to its source.
Our work: Property verification platformClaims-intake triage and document summaries that speed up review while decisions stay with adjusters.
How we approach it02 / How we start
Small enough to measure. Real enough to matter.
An industry AI project fails on context, not on the model. These four steps put the context first.
- 01
Pick one workflow
Choose a task your team repeats every day, with a clear owner and a result you can measure.
- 02
Map the data and the rules
Identify the systems, documents and permissions involved, and the sector rules — health, payment or personal data — that bound them.
- 03
Pilot with the people who do the work
Test on real cases, including the awkward ones, and measure accuracy, time saved and cost before widening use.
- 04
Run it with review built in
Ship monitoring, approval steps and documentation so the system stays trustworthy as models and processes change.
03 / Built and operated by NSDBytes
FocusPulse: the AI engineering behind this page.
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. The agent, retrieval and integration work you would get is the work we run ourselves.
- 500+
- articles read a day
- 30+
- business and markets publishers
- 4
- public MCP directories
https://
Add this address to Claude, Cursor or any MCP client.
- Tool
search_stories· One read-only tool- Covers
- India and US stories from the last 48 hours
- Access
- No account or key
Listed in
A little clarity
Before an industry AI project begins.
Which industries does NSDBytes build AI for?
The ones where we already build software: hospitality, healthcare, agritech, fintech, logistics, education, real estate and insurance. Knowing how a sector works decides where AI helps, so we start from domain experience rather than from a model.
Have you delivered AI projects in every one of these industries?
No, and we would rather say so. Our industry experience comes from products we have shipped, such as GMS for hotels and UNIVIA for agriculture. Our AI engineering is shown in FocusPulse, our own product, whose public MCP server is listed in the official MCP Registry. An AI project in your sector combines the two.
Can AI work with the industry software we already run?
Often, yes — through the system’s API, an MCP server or a purpose-built integration. GMS, for example, runs alongside the SkyTouch PMS rather than replacing it. Access, data quality and vendor terms are checked first.
How do you handle regulated data such as health or payment records?
The data boundaries are set during discovery, before any model sees real records. We are engineers, not compliance assessors or lawyers, so a regulated project also needs the specialists you appoint; we work alongside them.
What is a sensible first AI project?
One narrow, measurable workflow — for example exception review or document triage — piloted with the team that does it today. It shows whether AI earns its place before you invest in wider automation.
Start wherever you are
Bring the workflow in your sector that costs the most time.
We’ll help you judge whether AI fits it, what data it needs and what the smallest useful pilot looks like.