NOV 2025
RBI requires banks to assess and manage climate risk.
Scenario analysis and stress testing move from nice-to-have to expected. Agriculture is the most climate-exposed book.²⁴
VarshaOrbiSat · Earth observation × Simulation · India
Satellites show what happened. We show what happens next.
OrbiSat fuses multispectral and thermal satellite data with ground data — weather, census, health, credit — and runs what-if simulations. Banks see their crop-loan risk before the monsoon fails. Cities see where to save lives before the heatwave hits.
2 Products · 1 simulation engine · Built for India
PASS 02 — THE PROBLEM
B08 NIR
Everyone has a map. Nobody has a what-if.
India now has more satellite data than ever. But today’s tools describe the past: this field is stressed, this ward is hot. The people who carry the risk — a bank with ₹10 lakh crore in farm loans¹, a city with a heatwave coming — need to know what happens if: if rain falls 30% short, if the heatwave lasts five days, if we put the water points here instead of there.
PASS 03 — THE INSIGHT
B10 TIR
The hottest place is not the most vulnerable place.
In summer, an empty field can be the hottest pixel on a satellite image. But people don’t die in empty fields — they die in crowded wards with tin roofs, outdoor workers, the elderly, and no water nearby. A heat map says where it’s hot. We model who gets hurt. The same is true for farms: a rainfall number says how much rain fell. We model whose crop fails and whose loan breaks.
Layer
Same city. Same day. Different answer.
Illustrative simulation on synthetic data — not a forecast.
PASS 04 — WHAT WE DO
B03 GREEN
Sense. Fuse. Simulate. Decide.
01
Sense.
Multispectral, SAR and thermal imagery: Sentinel-1/2, Landsat 8/9, MODIS. Commercial high-res when needed.
02
Fuse.
Ground truth layered on top: IMD weather, census and population, health records, soil, elevation, loan books, ward boundaries.
03
Simulate.
Our scenario engine asks what if: rainfall ±X%, a 5-day heatwave, a budget of 40 water points — and computes the outcome.
04
Decide.
Outputs in the buyer’s language: ₹ at risk for a bank; lives protected per rupee for a city. Delivered by dashboard, API and audit-ready report.
One engine. Two markets. Every new dataset makes both products smarter.
PASS 05 — WHY NOW
B11 SWIR
2026 changed the rules — and the money.
JUN 2026
RBI KCC Directions let banks finance satellite crop monitoring.
A new, legitimate budget line for exactly what we sell.⁵
VarshaAUG 2026
Heatwaves become a notified national disaster.
States can now draw on ₹2.04 lakh crore of disaster funds (2026–31) for heat — cooling shelters, water points, early warning.⁶
ChhayaJUN 2026
NHRC calls for ward-level heat vulnerability maps.
Using GIS, remote sensing, AI and social vulnerability indicators — our exact stack.⁸
ChhayaProduct 01 · For banks & insurers
Varsha
Climate-scenario simulation for crop loan and crop insurance books.
“If rainfall drops 30%, how much crop is lost — and how much of our loan book breaks?”
- Village-level loss
- ₹ exposure at risk
- RBI stress-test ready
Product 02 · For governments & cities
Chhaya
Ward-level heat vulnerability and intervention planning.
“Where should we place water points and shade shelters to protect the most people?”
- 6-factor vulnerability index
- Placement optimiser
- Lives-per-rupee
PASS 07 — WHY WE WIN
B12 SWIR-2
Others map. We simulate.
| Capability | Monitoring tools (e.g. agri-sat platforms) | Global cat models | Free heat dashboards | OrbiSat |
|---|---|---|---|---|
| Satellite monitoring | ● | ◐ | ● | ● |
| Fuses ground data (census, health, credit) | ◐ | ◐ | ◐ | ● |
| Interactive what-if scenarios | ○ | ◐ | ○ | ● |
| Village / ward-level output | ● | ○ | ● | ● |
| Answers in ₹ or lives, not indices | ○ | ◐ | ○ | ● |
| Recommends where to act (optimisation) | ○ | ○ | ◐ | ● |
| Built for Indian regulation and schemes | ◐ | ○ | ◐ | ● |
- The fusion is the moat. Imagery is a commodity. Fusing it with census, health, credit and scheme data, and validating against what actually happened, is not.
- Outcome language. We don’t sell NDVI. We sell ₹ at risk and lives protected.
- One engine, two markets. Every model improvement and every dataset is reused across both products.
- Validated against reality. Every model is back-tested against real claims, defaults and heatstroke records — something most indices never do.
PASS 08 — MARKET
B02 BLUE
A market being forced into existence.
India geospatial analytics: ~$1.81B (2026) → $3.55B (2031)⁹
Climate-risk analytics (global): ~$2–3B, growing ~17–19% a year¹⁰
Our beachhead: India’s rural, co-operative and agri lenders, crop insurers, and cities with Heat Action Plans
Market reports vary widely; we use conservative figures.
PASS 09 — BUSINESS MODEL & GO-TO-MARKET
B05 RED-EDGE
How we make money.
Varsha
SaaS subscription per loan or policy portfolio, priced per account per season, plus a per-scenario fee for regulatory stress-test reports.
First customers: Regional rural banks, co-operative banks, agri-NBFCs and crop insurers.
Chhaya
Annual city licence plus a per-season operations package (heat-season daily risk and placement plans). Funded through state disaster funds (SDRF) and Heat Action Plan budgets.
First customers: Cities with existing Heat Action Plans; state disaster management authorities.
Pilot (1 district / 1 city)
Paid pilot
State-level rollout
API for partners
PASS 10 — THE ASK
ALL BANDS
Help us put a what-if engine in orbit.
We’re raising to take Varsha and Chhaya from first pilots — one district, one city — to paid deployments with banks and state governments.