Backend
Python
A programming language for AI features, data processing and automation: the engine behind document reading, reports and smart search.
Used for
- AI features
- Document reading
- Reports and analytics
- Task automation
Agriculture
Software development for AgriTech companies
Product engineering for AgriTech startups: advisory platforms, satellite and IoT crop monitoring, agri marketplaces and farmer credit products.
At a glance
The short version
- What we build
- How it works
- Field agent or farmer
- Satellites and IoT sensors
- Data pipeline
- Farmer app
- 2 more
- Runs on
- Android
- Web app
- Admin
- PWA
- Built with
- and 1 more tool
- Who it's for
AgriTech companies combine agronomy with data, marketplaces and finance. We engineer the platforms behind them, from imagery pipelines to regulated credit journeys.
- Cost depends on
- Data pipelines
- Model development
- Regulatory scope
Flutter and the related logo are trademarks of Google LLC. We are not endorsed by or affiliated with Google LLC.
How to start
Tell us what you need in your own words. You talk to the developers who would build it and get a written, line-item estimate.
Get a project estimateEngineering products for agriculture at scale
AgriTech startups build advisory apps, input and output marketplaces, farm-data platforms and credit products for farmers. Their engineering problems are distinctive: satellite imagery and sensor readings to process, AI models that must work in varied field conditions, many languages, and financial products governed by RBI rules. We act as the product engineering team for these companies, from first MVP to multi-season data pipelines.
Challenges
Engineering challenges in AgriTech
Noisy field data
Sensor gaps, cloudy satellite passes and inconsistent farmer inputs.
Models that travel badly
Crop disease models trained in one region perform worse in another.
Two-sided markets
Balancing buyers and farmers, grading and logistics in one marketplace.
Regulated credit
Farmer loans through bank or NBFC partners must follow digital lending rules.
Solutions
Products AgriTech teams build with us
Across 3 areas
Multi-vendor marketplaces with seller onboarding, commission rules, split payouts, per-seller invoicing and catalogue moderation.
Multi-tenant SaaS products with AI at their core, built with tenant isolation, usage metering, model routing, evaluation and subscription billing.
Product, content and offer recommendations built from your behavioural data, with business rules, cold-start handling and A/B testing.
Secure fintech apps and back offices for payments, lending, investments and insurance, integrated with licensed banking, KYC and data partners.
Custom loan origination and loan management software covering applications, underwriting, disbursal, EMIs, collections and reporting.
Assistants that book, schedule, look up records and raise requests by voice or text, with confirmation before every action.
AgriTech platform capabilities
Satellite crop monitoring
Vegetation indices such as NDVI from Sentinel-2 or commercial imagery, mapped to farm boundaries.
IoT data ingestion
Soil moisture and weather station readings over MQTT, with alerts and dashboards.
Photo-based crop diagnosis
Likely issues shown with confidence scores, escalated to agronomists when unsure.
ONDC and marketplace integration
FPO produce listed on ONDC through a seller app, alongside your own buyer network.
Farmer credit journeys
KYC through licensed providers, Account Aggregator consent and lender-side servicing.
How it works
How a crop advisory platform turns field data into advice
- Field agent or farmer
Step 1: Farm mapped and enrolled
The farm boundary is drawn on a map or walked with GPS, and the crop, variety and sowing date are recorded against it.
- Satellites and IoT sensors
Step 2: Imagery and sensor data arrive
Each satellite pass adds vegetation indices for the plot, while soil moisture and weather readings stream in from field devices over MQTT.
- Data pipeline
Step 3: Pipeline flags a problem
Unusual drops in crop health, dry soil or weather risk trigger alerts, with cloudy passes and sensor gaps handled rather than treated as zero.
- Farmer app
Step 4: Farmer receives advice
Alerts and advice arrive in the farmer’s language, and a photo of an affected leaf returns a likely diagnosis with a confidence level.
- Agronomist
Step 5: Agronomist handles uncertain cases
Low-confidence or high-risk cases go to an agronomist, and their answers become labelled data that improves the model next season.
- Marketplace or lending partner
Step 6: Inputs, credit or buyers
The farmer can order recommended inputs, apply for credit with a regulated lender, or list produce for buyers from the same app.
Then it starts again at step 1: Farm mapped and enrolled
Platforms
Platforms for AgriTech products
- Mobile
Android app
Apps for Android phones and tablets, tested on budget and mid-range devices and published on Google Play or privately.
- Web
Web application
Browser-based applications with logins, roles and workflows, such as customer portals, SaaS products and internal tools.
- Back office
Admin dashboard
Back-office panels for operations, support and finance teams: orders, users, content, reports and permissions.
- Web
Progressive web app
Installable web apps with offline support and notifications, useful when an app-store listing isn't essential or phones have little storage.
Technology
AgriTech technology stack
Python for imagery and models, a Flutter farmer app and data pipelines built on AWS that grow season by season.
Mobile
Flutter
Build Android and iOS apps from one shared codebase, so you launch on both platforms faster with a consistent experience.
Used for
- Mobile apps
- Delivery apps
- E-commerce apps
- Booking apps
Backend
NestJS
A structured way to build back ends on Node.js, so large business systems stay organised, testable and easy to hand over.
Used for
- Business app back ends
- SaaS platforms
- Marketplaces
- Admin panel back ends
Data
PostgreSQL
A reliable database for the records your business runs on: orders, payments, bookings and stock, kept accurate and easy to report on.
Used for
- Orders and customers
- Payments and ledgers
- Stock and inventory
- Bookings
Cloud & DevOps
AWS
Cloud hosting for your app, website and data, with data centres in India and room to grow when traffic rises.
Used for
- App hosting
- File and photo storage
- Backups
- Busy sale days
Connects your product to AI language models from several providers, so each task uses a suitable model and you can switch later.
Used for
- AI assistants
- Answers from documents
- Workflow automation
- Ticket triage
Payments & maps
Google Maps Platform
Adds maps, address search, live tracking and travel-time estimates to delivery, ride and field apps, so orders reach the right door.
Used for
- Live delivery tracking
- Address search
- Store locator
- Distance-based fees
Flutter and the related logo are trademarks of Google LLC. We are not endorsed by or affiliated with Google LLC.
Plain-English glossary
AgriTech terms, explained
- NDVI
- Normalised Difference Vegetation Index, a measure of plant greenness calculated from satellite images. A falling value on one plot can flag crop stress before it is visible from the field edge.
- Sentinel-2 imagery
- Free satellite images from the EU’s Copernicus programme, revisiting India every few days at 10-metre detail. They make crop monitoring affordable for medium and large plots.
- Account Aggregator
- An RBI-regulated framework through which a farmer can consent to share financial data, such as bank statements, with a lender. It speeds up credit checks without paper documents.
- ONDC
- Open Network for Digital Commerce: an open, government-promoted network where a seller listed through one app can be found by buyers using others. FPOs can join through a seller app instead of building a marketplace alone.
- Key Fact Statement
- A standard summary of a loan’s cost, fees and terms that the lender must show before the borrower signs. A farmer credit app has to display it clearly within the journey.
Product preview
Screens from an AgriTech product
Illustrative screens for a crop advisory app in the Nashik region.
Farmer app: photo diagnosis. Farmers get a likely diagnosis with its confidence level, and unclear cases go to an agronomist instead of being presented as fact. 
Agronomist console. Agronomists see farms flagged by satellite and sensor data on a map, and prioritise visits to plots where crop health is falling. 
Product team dashboard. Product teams track coverage, sensor health and escalations, which shows where the platform needs more field data.
Illustrative preview
Sample screens: names, prices and figures are examples, not client data.
Cost drivers
What drives AgriTech development cost
Data pipelines
Imagery and sensor streams across many farms need scalable storage and compute.
Model development
Custom ML needs labelled field data, often the slowest part.
Regulatory scope
Lending, payments or ONDC participation add compliance-driven features.
Estimates are written from your scope, with the effort and assumptions behind each line item.
How pricing worksServices
Services for this industry
The design and engineering services our in-house team combines on projects in this sector.
Mobile app development
Native and cross-platform Android and iOS apps, from first release through regular store updates, built by our in-house designers and engineers.
SaaS development
Multi-tenant SaaS products with subscription billing, team roles, onboarding and usage analytics, from first MVP to paying customers.
AI app development
AI features and AI-first apps built on model APIs, retrieval over your own data, workflow automation or custom ML, with evaluation and human review.
Android app development
Native Android apps in Kotlin and Jetpack Compose, designed for entry-level phones, patchy networks and the background limits of popular Android brands.
API development
Secure, documented REST and GraphQL APIs, plus integrations that connect your apps to payment, logistics, GST, messaging and business systems.
UI/UX design
Product design for web and mobile apps: research, user flows, wireframes, prototypes and design systems that engineers can build from.
FAQ
Frequently asked questions
Can we use free satellite imagery for crop monitoring?
For many use cases, yes. Sentinel-2 imagery from the EU's Copernicus programme is free, revisits India every few days and offers 10-metre resolution in key bands, which suits vegetation indices on medium and large plots. Very small plots, cloudy monsoon months and precise counts need commercial high-resolution imagery or drone data. We usually prototype with free imagery before paying for more.
How do RBI digital lending guidelines affect a farmer credit app?
If your app helps farmers borrow from a regulated lender, they shape the design: disbursal and repayment flow directly between the borrower's bank account and the lender, borrowers see a Key Fact Statement before signing, and data is collected only with explicit consent for a stated purpose. Your role as a lending service provider must be disclosed. Your compliance team confirms the interpretation.
Can AI diagnose crop diseases accurately from photos?
Accuracy depends on the crop, the disease, image quality and how closely users' photos match the training data. Public datasets are a start, but models usually need labelled photos from your own regions to perform well. We show a likely diagnosis with a confidence level, ask for a clearer photo when needed, and route low-confidence cases to an agronomist instead of presenting a guess as fact.
Next step
Building an AgriTech product?
Tell us what you are testing this season, and we will suggest an MVP and a data plan.