Annual production lost to downtime
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NAIQ (नायक)
Industrial AI · Raipur
The answers your factory needs
are already written down.
Nobody has read them yet.
Every plant in Raipur keeps production logs, maintenance sheets and downtime registers. Inside those pages are the breakdowns you could have seen coming and the costs you never had to pay. NAIQ reads them — carefully, personally, and with every estimate shown in ₹ before you commit to anything.
A page from any plant's downtime register
04 Jun — Pump-7 seal failure, line stopped6.5 hrs
11 Jun — Bearing seizure, mill stand 39 hrs
19 Jun — Furnace trip, restart + reheat11 hrs
Each one left a warning in the data — weeks earlier.—
Analyst-led · In person
NDA from day one
ROI shown in ₹ first
Hindi & English
सीधी बात · Straight talk
Before the pitch — here is exactly
where NAIQ stands today.
Most pitches start with the dream. We'd rather you know the facts first, so everything after this slide can be checked against them.
- We are early-stage. NAIQ is a young company based in Raipur, Chhattisgarh. We are not claiming scale we don't have.
- Delivery today is human-led. A qualified analyst — PhD in AI, NIT Raipur — personally examines your operational data and returns recommendations, forecasts and ROI estimates you can verify against your own numbers.
- The self-serve software platform is still in development. What you can buy today is analyst-led assessment and pilot work, plus a ₹2,999/month Industrial AI assistant.
- Our deeper research tracks — quantum methods, medical imaging, cognitive science — are research, not products. We keep them clearly separated from what we sell.
- We currently serve Raipur & Chhattisgarh only, and we visit plants in person.
- If the free assessment shows AI isn't worth it for you yet, we will tell you that. A wrong sale costs us more than no sale.
The problem
India's 3rd-largest steel state still runs
on memory and experience alone.
Chhattisgarh produces roughly 13% of India's steel. Around Raipur alone there are about 170 rolling mills and 90+ sponge-iron plants — and in most of them, maintenance and quality decisions are made the way they were made twenty years ago: by experience, after the breakdown.
Unplanned downtime that studies show is preventable30–50%
Energy share of operating cost (steel / sponge iron)30–40%
Extra machine life available with predictive care20–40%
The loss is invisible
Downtime, energy waste and quality drift never appear as one line in any account book — so they are never attacked as one problem.
The data already exists
Logs, registers and maintenance sheets are already being kept, every shift. The signals are recorded. They are simply never analysed.
Nobody serves this market
Metro AI vendors don't visit Tier-2 industrial clusters, don't price for MSMEs, and don't work in Hindi. The gap is structural, not temporary.
Market reality — sources named, conservative end assumed
The market is real. We quote it honestly
— and plan on the cautious end.
$0
India's AI-in-manufacturing market projected by 2030, growing at ~41% CAGR.
MarketsandMarkets, 20250–150 $B
Potential AI value in India's manufacturing MSMEs by 2035 — most of it still unclaimed.
PwC & ORF estimate0%
Of India's steel output comes from Chhattisgarh — the 3rd-largest producing state, centred on Raipur.
State industry dataWhat we do not claim
Market projections differ by source — India healthcare-AI forecasts for 2030, for example, range from $8.7B to $35B depending on who you read. We treat all such figures as direction, not destiny. Our plan is built bottom-up: plants in the Raipur cluster × verified savings per plant — not top-down from a TAM slide. The Chhattisgarh government signed an MoU with STPI (2026) and is drafting a state AI policy; that is tailwind, not a guarantee.
Why us · why here · why now
A defensible position no metro startup
will contest soon.
Local, physically present
We are from Raipur, we work in Raipur, and we walk the plant floor in person. Trust in this market is built face-to-face, in Hindi — not through a sales call from Bangalore.
A dense, underserved cluster
~170 rolling mills and 90+ sponge-iron plants within one region means short sales distances, referenceable neighbours, and compounding word-of-mouth — rare economics for B2B AI.
Research-grade founder, applied focus
Founder holds a PhD in AI (NIT Raipur) with peer-reviewed publications. The science is real — and it is pointed at pumps, furnaces and downtime registers, not at slideware.
Timing
Input costs are rising, quality norms are tightening, and the state is actively building AI policy support. The plants that adopt data-driven operations first will set the cost curve for the rest.
What we do
From your existing records to a verifiable
₹ number — in four steps.
No new sensors required to start. No platform lock-in. Each step ends with a number you can check against your own books, and you decide whether the next step is worth it.
Free
Discovery call
30 minutes to understand your plant, your pain points and your current records. No preparation needed.
Free
AI Readiness Scan
A 35-question audit of your data, infrastructure and operations — returned as a scorecard with a first ROI estimate in 7–15 days.
Pilot blueprint
A signed, costed proposal for the single highest-ROI use case — with hardware list, roadmap, and the maths shown before you pay.
Build & evolve
Pilot implementation, team training and ongoing model retraining, with a dedicated project manager beside you.
Available today
Analyst-led assessments and pilots · predictive-maintenance analysis · downtime & energy studies · the NAIQ Industrial AI assistant (Hindi/English, ₹2,999/month after 10 free messages).
In development — said plainly
The self-serve software platform that will make this intelligence continuous and affordable at scale. Until it ships, delivery is human-led and we say so on every page.
Interactive · see your own numbers
Don't take our word for it.
Move the sliders — it's your plant.
Recoverable at the conservative 30% end
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Assumes the published 30–50% preventable-downtime range (McKinsey); we display only the 30% floor. This is an illustration, not a promise — your actual number is estimated from your own records during the free Readiness Scan, and shown to you before any commitment. Results vary plant to plant and depend on data quality.
Who is asking for your trust
Resham Raj Shivwanshi
Founder · PhD in Artificial Intelligence, NIT Raipur
A decade in AI research — computer vision, medical imaging, deep learning — with peer-reviewed publications. Raipur is home. NAIQ exists to bring research-grade AI to the industrial belt that built this state, at prices its MSMEs can actually pay.
“I will not sell a plant an AI project its own data doesn't justify. The free assessment exists so that the maths — not the marketing — makes the decision.”
PhD, AI — NIT Raipur
Peer-reviewed publications
Hiring 5 founding roles
The slide most pitches leave out
What could go wrong — and what
we're doing about each one.
Plant data quality may be poor
Some registers are incomplete or inconsistent. Mitigation: the Readiness Scan scores data quality first, and we decline engagements the data can't support — publicly, as policy.
MSME sales cycles are slow
Trust takes months in this market. Mitigation: free entry steps, in-person visits, NDA from day one, and a dense local cluster where one good result travels fast.
We are services-led before we are software-led
Analyst-led delivery limits speed of scale. Mitigation: every engagement feeds the platform in development; services fund the build instead of dilutive capital.
Bigger players could arrive
If the market proves out, competition follows. Mitigation: local presence, Hindi-first delivery, MSME pricing and cluster relationships are slow, unglamorous moats — which is why they hold.
If any of these risks concern you, ask us about them directly. We would rather lose a deal on honesty than win one on silence.
The ask
One conversation. Thirty minutes.
Your data does the talking.
Bring last quarter's downtime register to a free discovery call. If the numbers say AI isn't worth it for your plant yet, we'll say so and you'll have lost thirty minutes. If they say otherwise, you'll see the ₹ figure before you spend a rupee.