Seed businesses plan production 1–2 seasons before demand is known, depend on grower networks and weather, and sell into brutal kharif/rabi windows. Agri-Intelligence is the AI platform built around exactly that: end-to-end visibility, automated forecasting, crop-lifecycle risk management and prescriptive planning — in one place.
A one-minute look inside the platform — Executive Control Tower, LCAPI digital twin of every acre, demand planning, grower scorecards and the AI copilot. Book a demo for the full 6-minute walkthrough on your own crops and regions.
The complete 6-minute tour covers every module — LCAPI digital twin, forecast engine, grower scorecards, carryover-risk inventory, risk intelligence and the AI copilot — on real seed data. We’ll walk you through it live and answer your questions.
Three structural realities make seed planning harder than any FMCG or retail supply chain — and none of them fit a spreadsheet.
You commit acreage and grower contracts one to two seasons before the market tells you what it wants. A wrong bet becomes carryover stock with a germination clock ticking on it.
Output depends on growers, crop stages and monsoon behaviour. Planning that ignores the crop calendar isn't planning — it's hoping.
A season's revenue lands in a few weeks. A stockout in week 2 of kharif is revenue you never recover; excess in week 6 is inventory you carry for a year.
This is the full module library — what each module does, the problem it kills, and who lives in it daily. Every module speaks to the others: the forecast knows the crop calendar, inventory knows the germination clock, distribution knows the demand plan.
See demand before the season does — and run the company on one number.
One screen for the whole season: projected revenue, profit and margin, working capital, forecast accuracy (MAPE), service level and on-time delivery — with drill-down from enterprise to crop, hybrid, district, dealer, grower, warehouse and individual seed lot, across nine analysis views from Demand & Market to Risk & Weather.
Leadership reviews built from five departments' conflicting decks. The control tower is the single source of truth — the MD's question and the planner's answer come from the same live screen.
MD/CEO, business heads, planning leadership
The season plan workspace: demand by crop, hybrid, region and pack size for kharif, rabi and summer windows.
Sales says 12,000 units, supply says 8,000, finance budgets 10,000 — and the season starts with three plans. Demand Planning forces one consensus number with planner overrides and a full audit trail of who changed what and why.
Demand planners, sales heads, business heads
Automated ML forecasting that learns each hybrid's seasonal pattern and scores itself with MAPE and bias every cycle.
Last-year-plus-10% forecasting is why carryover exists. The engine races statistical and ML models per SKU, picks the winner on holdout accuracy, and shows you exactly how much better it is than the manual number.
Demand planners, analytics teams
A library of demand drivers — rainfall, sowing progress, MSP/price moves, dealer schemes, competitor launches — attachable to any forecast.
Seed demand doesn't move on history alone; it moves on monsoon and market signals. Drivers make the forecast explainable: 'demand up 14% because sowing advanced 2 weeks in Vidarbha' beats a black box.
Planners, regional sales managers
A monthly demand–supply–finance consensus rhythm with the numbers pre-assembled: gaps, scenarios and decisions on one screen.
Most S&OP meetings argue about whose spreadsheet is right. This module ends that: everyone reviews the same live gap analysis and leaves with recorded decisions, not action-item amnesia.
Leadership, planning heads, finance
Side-by-side what-if simulation for any season variable: a delayed monsoon, a grower shortfall, a hybrid suddenly taking off. One workspace, run from Plan, reused for risk contingency in Protect.
Committing crores to acreage on one guess is gambling. Simulate three monsoon scenarios before contracting growers, and you know your downside before you sign it.
Planning heads, leadership
Production planning that understands acres, growers and crop stages — not machine hours.
Your grower network as a managed asset: contracts, acreage, expected vs actual output, and performance history per grower and region.
Grower information living in field officers' phones is a business risk. A grower register shows who reliably delivers, who over-promises, and where next season's acreage should go.
Production heads, field officers
Sowing-to-harvest timelines per crop and region that drive every downstream commitment.
Promising October supply from a crop that harvests in November isn't a plan. When the calendar is the backbone, processing, packing and dispatch dates become physically possible by construction.
Production planning, supply planning
"Module 0" of the platform: every acre as a production asset. A digital-twin registry of GPS-mapped farmer fields — village, district, agro-climatic zone, rainfall zone — with eight workspaces: Digital Twin, Land Resources, Soil Intelligence, Climate Intelligence, Crop Planning, Crop Allocation Optimizer, Acre Optimization AI and Scenarios.
Supply planning that starts at the warehouse is already too late — the season is decided on the acre. LCAPI puts land, soil and climate intelligence under every downstream plan, so acreage allocation and yield expectations are optimised before a single grower contract is signed.
Production heads, agronomy teams, planning leadership
Live visibility of processing, conditioning and packing: where every lot is, and what's blocking it.
'Where is lot 4172?' should not take three phone calls. Lot-level status ends the daily archaeology and surfaces bottlenecks while they can still be fixed.
Plant managers, production planners
Plant, processing and packing capacity checked against the seasonal plan — before the season, not during it.
Discovering in September that packing capacity can't meet October dispatches is a preventable disaster. Capacity gaps show up months early, when adding a shift still solves them.
Operations heads, plant managers
Inventory and distribution built for products with a germination clock.
Lot-level stock across plants and depots with age, germination validity and carryover risk scored continuously.
In this business, inventory doesn't just cost money — it expires. The carryover radar flags at-risk lots months before the write-off, while liquidation options still exist.
Supply planners, finance, sales
Depot- and dealer-level allocation matched to regional demand signals.
The oldest seed-industry pain: stock sitting in the wrong state during the sowing window. Demand-matched allocation stops shipping to where the stock already is.
Distribution, regional sales
Dispatch and route planning tuned for the season's crunch weeks.
A season's revenue moves in about six weeks of dispatches. Planned routing and pre-positioning replace the annual freight-and-firefighting festival.
Logistics, dispatch teams
Packing material and input procurement synced to the production plan.
No pouch, no product — lines have stopped for want of a printed packet. Procurement triggered by the actual plan means materials arrive when packing does.
Procurement, plant stores
Seed supply chains carry weather, biological and market risk — watch all three.
Crop-lifecycle risk management: weather anomalies, production-shortfall signals and demand-shift alerts, early enough to act.
Every seed company gets surprised by the same risks every year. Early-warning signals convert 'we found out too late' into 'we saw it in week two and re-planned'.
Leadership, planning, production
The data backbone plus an AI copilot that answers in seconds what took a week of Excel.
One clean master of crops, hybrids, lots, pack sizes and regions.
Five spreadsheets with five spellings of the same hybrid is why reports never reconcile. One master ends the reconciliation industry inside your company.
Everyone, via IT/analytics owners
Lab-grade seed quality tracking alongside stock: germination rate, purity percentage, moisture content and seed treatment status per lot.
A lot can be numerically "in stock" and commercially unsellable — below germination threshold, over moisture limit, untreated. This module catches that gap before a dealer does.
Quality teams, seed testing labs, inventory managers
Ask the platform plain-language questions — 'which hybrids risk carryover in Maharashtra?' — answered from your own data.
Insight shouldn't queue behind whoever knows the spreadsheet. The copilot gives every manager direct answers, grounded in live data, with the source shown.
Every decision-maker
Digital twin of every field — soil, climate and agro-zone — so allocation is optimised before grower contracts are signed.
AutoML forecasting with confidence bands and causal drivers replaces last-year-plus-10% guessing.
Lot-level germination validity flags at-risk stock months before it becomes a write-off.
Scorecards, isolation verification and risk scoring replace field-officer WhatsApp groups.
Weather, market, regulatory and biological risks with financial exposure and mitigation actions.
Ask the AI copilot in plain language instead of waiting a week for a spreadsheet.
| Decision | Spreadsheets + ERP | Mathnal Agri-Intelligence |
|---|---|---|
| Season demand plan | Last year + gut feel, argued in meetings | ML forecast + drivers + planner judgment, one number |
| Grower & production visibility | Phone calls and field WhatsApp groups | Live grower register tied to the crop calendar |
| Carryover risk | Discovered at year-end stock count | Flagged lot-by-lot with germination clock |
| Season-crunch dispatch | Firefighting in the sowing window | Pre-positioned allocation & transport plan |
| "What if" questions | A week of Excel per scenario | Scenario planner + AI copilot, minutes |
A live demo takes 30 minutes. A pilot on your own data — one crop or product line — proves the value in weeks, before you commit to a rollout. Pricing is scoped per deployment: modules, users and data footprint.
Also relevant: our agri-industry solutions playbook → · case studies with quantified ROI → · free supply chain diagnostic →
Every engagement follows our integrated DAPP → RESO methodology: from understanding your business problem through domain analysis, data engineering, algorithms and prediction — to risk-adjusted optimisation, simulation and executable decisions. A closed learning loop ensures continuous improvement.
Start from the supply chain business problem — not the technology. Understand the decision, its frequency, constraints. Engineer data into decision-ready features. Select algorithms because of the problem, not fashion. Generate predictions with uncertainty, then prescribe candidate recommendations.
Take DAPP's candidate recommendation and stress-test it. Is it the risk-adjusted optimum? How does it perform under realistic variability and disruption scenarios? Who decides, what threshold, what human oversight? Then execute, measure outcome, and feed learning back to domain.
Every execution outcome feeds back into the domain layer. Models retrain, risk scores recalibrate, decision thresholds adapt. This is not a one-time project — it's a continuously improving decision system.
How our delivery maps to the framework
Data engineering, algorithm selection, model training — the DAPP backbone.
DAPP PhaseForecasting engines, inventory optimisers, risk monitors — productised DAPP outputs.
DAPP → RESODomain analysis, risk assessment, decision design — the full DAPP → RESO arc.
Full LifecycleOptimisation, simulation, dashboards, training — enabling your team to operate the loop.
RESO + Learning