I ran numbers, not opinions. Designed systems across the group and, beyond my original brief, walked a 5 day plant audit at Hero Steels because the opportunity to find something was there. The internship extended on the CFO's own recommendation.

The single number this chapter turns on

5 days

Plant audit at Hero Steels. Near zero adoption of the SAP PM maintenance module, driven by interface complexity, was a primary driver of unplanned downtime. Recommendations adopted by the CEO.

The decision node

Month 9, brief complete

Same nine month window. One branch was the job. The other was the choice.

Systems I shipped in 9 months

Fixed Asset Management System

Designed and deployed across group entities. IT Act aligned calculations, QR verification, role based access, lifecycle dashboards.

Predictive maintenance design

Downtime tracking and maintenance forecasting design for Hero Steels, built with the CEO's office.

VAPT and cyber resilience

Delivered with the IT function. Vulnerability posture, remediation plan, ongoing hardening.

Serendipity Arts, funding pipeline

Built a funding pipeline and lead management system. Range extended past manufacturing.

The rooms I earned

Five cities, one thread

Ludhiana

Hero Steels plant. Audit finding on SAP PM interface adoption, presented to the CEO. Recommendations adopted.

The 5 day audit, Hero Steels

Wide floor observation narrowing to 1 named finding in 5 days.

Day 1 of 5

Day 1
Walked the shop floor with the maintenance leads. Watched how the SAP PM module was actually used, not how it was documented.
5 day plant audit
Day 2
Pulled adoption data across shifts. Near zero. The interface complexity was the story, not operator discipline.
Near zero SAP PM adoption
Day 3
Mapped the module gap directly to unplanned downtime categories. The correlation was clean.
Primary driver, unplanned downtime
Day 4
Drafted an interface simplification plan the maintenance team could adopt without a new system.
Zero new software
Day 5
Presented directly to Hero Steels CEO Jitendra Sharma. Recommendations adopted.
Adopted by the CEO

"The module was fine. The interface people had to actually use was not."

Path to acceptance

Floor→CEO Jitendra Sharma→Recommendations adopted→Group CFO acknowledged

The proof band, operator to founder

9 months

Hero Enterprise Group CFO's Office. Systems shipped across the group; 5 day audit adopted by the CEO.

Rs 3.6 to 7 Cr

Recurring EBITDA, the validated subset of the Rs 12 Cr+ identified, validated by their CFO.

Rs 12 Cr+

Zedral pilot, gross annualised recovery identified, zero new capex.

Hero Enterprise: Nine months in the Group CFO's Office. 5 day audit at Hero Steels, SAP PM interface finding adopted.
Zedral pilot: One integrated steel facility, 180,000 MTPA. 65 datasets, 1,336 delay entries, 73% traced to setup and changeover, not mechanical failure. Phase 1 in execution.

02, Consultant

Same three moves, every time.

Categorise the problem with data. Structure the fix before you price it. Spend last, and prove it in weeks.

The number I walked away on

Rs 25 to 35 Cr

Minimum viable capital threshold for Zestofy. Below it, walking away was the disciplined answer. I did.

Venture building, PE secondary

34-page capital strategy

Co-founded a venture targeting India's private equity secondary market. Executed a structured strategic pause after capital-viability analysis. Closed on evidence, not emotion.

The three moves

Diagnose

Where capacity actually goes. Data, not opinion. Every entry labelled, every label priced.

Redesign

Structure the fix before you price it. Capex last, not first. Choreography, then tooling, then hardware.

Monitor

Continuously, so efficiency never goes dark again. Six leading signals, updated daily.

03, Builder

The diagnostic had to survive contact with a plant manager.

Input is whatever the plant already has. DCS/PLC, MES, ERP, logs and Excel. Zero new hardware. 4 sources land in one Zedral data lake. On top of it sit 6 intelligence modules, Shopfloor Digitization, OEE and Line Performance, Yield, Quality, Preventive Maintenance, Energy and ESG, each surfacing signals a shift supervisor can read in under a minute, updated daily.

Four sources, one layer

Zero new hardware

PLC and SCADA

ERP and SAP

Operator tablet, four taps

CCTV via computer vision

Zedral data layer, one truthful record

Shopfloor Digitization
Foundation
OEE and Line Performance
Yield Intelligence
Quality Intelligence
Preventive Maintenance
Energy and ESG

Paper logs move to a four tap tablet. Everything lands in one record. Shopfloor Digitization is the foundation, the other five read from it.

Captured vs acted on

Before

Delay logs, DCS tags, registers and ERP rows exist. They sit in four places and are read at month end, if at all.

After

The same data lands in one lake and comes back as a ranked, rupee valued signal a shift supervisor reads in under a minute.

63%

of manufacturers say they actively analyse or use less than half of the manufacturing data they collect.

Source: Manufacturing Leadership Council, Data Mastery and Analytics survey, 2026

We read machines nobody else will touch

Controllers from the 2000s. Machines still running Windows XP. Chinese PLCs with no documentation and no vendor support. We identify the parameters ourselves and write our own drivers, in Python and Rust, with no integrator and no hardware partner. In a mid-market plant, "we could not read that machine" is the single reason digitisation projects die. We have not yet met a machine we could not make speak.

Controller era coverage

1990s

2000s

2010s

Modern

Typical platforms

Zedral

We write our own drivers

Typical platforms read modern controllers only. Zedral reads every generation still running on the floor.

zedral, diagnostic pipeline

OEE the layer reads on the client facility

Availability 71%

Performance 63%

Quality 94%

Availability × Performance × Quality. Every point recovered flows to the client's EBITDA. The layer makes it legible daily.

Built with Sreejan Datta, Co-Founder and COO, who runs operations and product delivery.

Where this goes

Preventive today. Predictive next. Prescriptive after that.

Once one truthful record exists, agents run the plant: a capacity agent reads machine state, order book and live production and plans each machine for each shift; a planning agent sequences the floor; an inventory agent flags shortages; then dispatch, then quality. The agents propose, the human decides. That is a plant that is AI native, not digitised.

The maturity ladder

Live
Digitise
Live
Manage
We are here
Building
Predict
Next
Prescribe, agents

Preventive today. Predictive next. Prescriptive after that.

The agent constellation

The agents propose, the human decides. The core is the asset, the agents are swappable.

The agents are real product. The data underneath is what makes them possible.

Any model can be improved or replaced. The data cannot.

The compounding curve

Signed rights to train, and to retain after deployment.

05, Founder

Then I built a company on one finding I could not put down.

Not a market research decision. A conviction call. I had already watched plant leadership act on a diagnostic once. Doing it a second time, on purpose, at scale, was the whole point.

Identified and CFO-validated, per year

Rs 3.6 to 7 Cr

Recurring EBITDA impact, validated line by line by the Group CFO's office. It is the validated subset of Rs 12 Cr+ gross annualised recovery identified across the pilot.

Downtime decomposition, Hero Steels audit

73%

Setup and changeover

27%

Mechanical failure

1,336 delay entries, categorised one by one.

From identified to validated

Gross annualised recovery identifiedRs 12 Cr+
Less one-time and non-recurring items[to fill]
Less overlap between findings[to fill]
Less CFO prudence haircut[to fill]
Validated recurring EBITDA, per yearRs 3.6 to 7 Cr

Rs 12 Cr+ is the gross annualised recovery identified across the pilot. Rs 3.6 to 7 Cr is the recurring EBITDA subset the Group CFO's office validated line by line. Dashed steps are placeholders until the breakdown is published.

Where things stand, November 2026: [to fill]

Why I started

I built a company on one finding I could not put down. Not because market research demanded it, because I had already seen plant leadership act on it once.

Who I built it with

Sreejan Datta, Co-Founder and COO, because the diagnostic had to survive contact with a plant manager on day 1. Ayush Pal, Co-Founder, completes the founding team.

Why clients let us in

Two industrial groups have put us on their shop floors. Three plants are live, six more are in discussion, and both groups have signed over the right to train our models on their plant data and to keep that right after the deployment ends. That last clause is the company.

Why I keep shipping

Same E-Cell habit that ran Sharda Launchpad and now runs the NFSU E-Cell. Ship before you polish. Prove before you pitch.

How an engagement starts

Phase 1: [to fill]
Length: [to fill]
What the plant provides: [to fill]
What the plant gets: [to fill]

Founder timeline, four ventures, overlapping

2021 to 2026

2021

2022

2023

2024

2025

2026

Hix Media

Founder and Strategy Consultant

Zestofy

Co-Founder

Hero Enterprise

Finance and Enterprise Transformation Associate

Zedral

Co-Founder and CEOnow

Campus and press

Sharda E-Cell President

NFSU Corporate Outreach

NFSU E-Cell President

Inc42 feature

Four ventures, three campus roles, one feature, all overlapping. The picture that reads clean on a single line is not the picture that actually happened.

Inc42, May 2026

5 Manufacturing Startups to Watch

Credentials

Inc42, May 2026

5 Manufacturing Startups to Watch

Group CFO letter

Recommendation, Hero Enterprise

CEO letter

Recommendation, Hero Steels

President, E-Cell

National Forensic Sciences University

President, E-Cell

Sharda University, prior term

Harvard CS50

Computer science fundamentals

Work the floor like an operator. Structure problems like a consultant. Commit like a founder.

I answer my own inbox.

I am looking for the next plant: [sector, to fill], [tonnage, to fill], with [data sources already in place, to fill]. If that is yours, write to me. I read every message myself.

Plant operatorsIndustrial AI buildersPeople who back this work

jasnoorsinghkhalsa@gmail.com zedral.com LinkedIn

Operator, then consultant,then founder.

Clients

Goodluck India Limited

Hero Steels

Scale ladder

Nine months in Hero Enterprise's Group CFO's Office.

Systems I shipped in 9 months

The 5 day audit, Hero Steels

Same three moves, every time.

The diagnostic had to survive contact with a plant manager.

Four sources, one layer

Captured vs acted on

We read machines nobody else will touch

Controller era coverage

Preventive today. Predictive next. Prescriptive after that.

The maturity ladder

The agent constellation

The compounding curve

Then I built a company on one finding I could not put down.

Downtime decomposition, Hero Steels audit

From identified to validated

Why I started

Who I built it with

Why clients let us in

Why I keep shipping

How an engagement starts

Credentials

I answer my own inbox.

Operator, then consultant,
then founder.