Affordable
Shared, reusable twin models and taxonomies mean each new district, crop or programme costs a fraction of the first.
Agriculture andthe public sector.
AI is only as good as the quality, availability and context of the data behind it. We help governments, agribusinesses, insurers and lenders build a trusted, interoperable agriculture data ecosystem with the farmer at its centre.
Satellite and soilPlot signals, consented
Twyn KisanEach plot, modelled
Voice copilotAdvisory in local language
Extension officerReviews and approves
Farmer actsOutcome fed back
01 — Why agriculture needs a different approach
Four tests every solution must pass.
Indian farms are small and fragmented, with many crops, regions and languages, uneven digital maturity and very different abilities to pay. If a solution passes these tests, it becomes relevant to developing economies across Asia, Africa and beyond.
01
02
Accessible
Voice-first advisory in local languages, low-bandwidth apps with offline sync, and channels farmers already use.
03
Simple
One question, one clear answer. Officers approve; farmers act. No dashboards required at the farm gate.
04
Scalable
Sovereign, on-premises or government-cloud deployment that plugs into national digital public infrastructure.
02 — The data foundation
The one thing to get right.
Taking AI from pilots to population scale needs common standards, secure interoperability and strong governance. That is where we begin.
The problemWhat we doWhat changes
Farmer, land, crop and bank records do not match, so benefits are delayed or go to duplicate and ghost beneficiaries.
What we do
Farmer and land master data management unifies land records, identity and farmer registries, crop-sown records and bank seeding into one golden record, using fuzzy and phonetic matching across Indian scripts.
What changes
80–90% accuracy in document and subsidy verification; 30–50% faster subsidy delivery*.
Departments, agritech firms, FPOs, insurers and banks cannot share data reliably.
What we do
Agriculture data contracts and shared reference data, so departments, agritech firms, FPOs, insurers and banks read the same definitions.
What changes
Secure interoperability that lets new partners join without bespoke integration.
Farmers worry about who uses their data and why.
What we do
Consent is captured, recorded and honoured at the attribute level. The farmer's data is used for the farmer's benefit, and every use is logged.
What changes
Trust that keeps farmers participating.
03 — From data to decisions in the field
Twins that reach the farm gate.
Every advisory is grounded in approved agronomy and reviewed by extension officers and KVK scientists.
The problemWhat we doWhat changes
Advisories arrive late, in the wrong language, or are too generic to act on.
What we do
Twyn Kisan models each plot from satellite, weather and soil data. A vernacular voice copilot turns the twin into a specific, timely advisory, reviewed by extension officers and KVK scientists.
What changes
20–40% higher field-officer productivity; 30–50% faster advisory response*.
Canal rosters and reservoir releases do not match crop demand at the tail end.
What we do
A water twin links reservoir storage, canal-flow sensors and village-level crop demand to recommend release schedules for engineers to approve.
What changes
20–35% better water allocation and command-area management*.
Crop-damage assessments and insurance claims take months.
What we do
Plot twins combine satellite imagery, weather triggers and digital crop surveys to estimate yield loss, pre-fill claims and flag disputes for field verification.
What changes
25–40% better damage and drought assessment accuracy*; faster settlement.
Smallholders struggle to get formal credit because lenders cannot see their farm.
What we do
With the farmer's consent, plot history, crop records and repayment data form a lending twin that banks can use for Kisan Credit Card and input-finance decisions.
What changes
More farmers reach formal credit on fairer terms.
Leakage in fertiliser sales, procurement at mandis and public distribution.
What we do
Reconciliation agents continuously match point-of-sale quotas, weighbridge and bag-weight records, warehouse stock and payments, and route exceptions to auditors.
What changes
25–45% reduction in diversion and leakage*.
Export buyers and processors demand proof of origin and quality.
What we do
Traceability twins follow a lot from plot to cold chain to buyer, with residue, quarantine and quality records attached.
What changes
Premium market access backed by verifiable data.
* Indicative ranges drawn from Twyngentic solution blueprints and reference implementations. Every figure is confirmed for each client during discovery.
04 — What we measure
The farmer is the KPI.
Not how sophisticated the technology is or how many models we deploy — what changes for the farmer.
01
Better productivity
Yield per acre and per litre of water.
02
Lower input costs
Right fertiliser, seed and crop protection, at the right time and dose.
03
Efficient water and crop protection
Less waste, less runoff, fewer unnecessary sprays.
04
Lower risk
Earlier warnings, faster insurance, access to credit.
05
Greater sustainability
Soil health and resource use tracked over seasons.
06
Better income and prosperity
The outcome everything else must add up to.
05 — A staged path for departments
From quick wins to auditable autonomy.
Our four-tier model moves a department step by step.
Foundation and quick winsDocument intelligence for land deeds, licences and mandi bills; KYC automation; citizen service bots. 20–35% less manual data entry*.
Officer copilotsVernacular copilots for district and block officers and KVK scientists; draft file notes and RTI responses. 20–30% more daily inspections per officer*.
Enterprise intelligenceCross-departmental MDM, predictive yield models, satellite and GIS reconciliation, leakage detection. 25–30% lower administrative cost*.
Autonomous governanceDisaster relief triggered by satellite and weather telemetry, dynamic grain routing, closed-loop monitoring. 30–40% shorter disaster-to-disbursement cycles*.
06 — Departments we support
A blueprint for every bottleneck.
Each blueprint starts on a synthetic twin before it touches live records.
| Department | Typical bottleneck | Twyngentic blueprint |
|---|---|---|
| Agriculture and farmers' welfare | Scheme verification backlogs, duplicate subsidy claims | Automated land–identity–crop reconciliation; field-inspection copilot on mobile |
| Water resources and command area development | Inefficient canal rosters, late drought alerts | Water twin linking canal-flow sensors with tail-end crop demand |
| Food and civil supplies | Mandi procurement delays, inventory mismatches | Biometric token routing; automated bag-weight document intelligence; leakage reconciliation |
| Rural development and panchayati raj | Slow asset verification, fund-utilisation audits | Geo-tagged asset twins and audit agents across gram panchayats |
| Horticulture, animal husbandry and fisheries | Pest and disease alert latency, livestock insurance paperwork | Vernacular voice agent for pest diagnosis; livestock twins for tagging and insurance |
* Indicative ranges drawn from Twyngentic solution blueprints and reference implementations. Every figure is confirmed for each client during discovery.
Power to act