A leading product engineering company, creating adaptive software solutions to improve operations, providing businesses with expert development services from across domain.
A leading product engineering company, creating adaptive software solutions to improve operations, providing businesses with expert development services from across domain.
AgriTech / IoT
A full-stack pond-to-port aquaculture ecosystem — AI monitoring, disease prediction, supply chain linkage, and export readiness — designed and delivered by Creuto from scratch.
6,000+
Farmers Registered8,000+
Acres Monitored150+
Villages Covered₹25Cr
Institutional Funding RaisedClient
Aquapulse
Industry
Aquaculture / AgriTech
Services
Mobile App Development
AI & IoT Integration
Web Dashboard Development
Backend Architecture
Cloud Deployment
Aquapulse is an end-to-end aquaculture intelligence platform built for the Indian shrimp and fish farming industry. It combines AI-driven pond monitoring with pre-harvest advisory, post-harvest processing support, supply chain coordination, and global export linkage — all accessible from a single platform by farmers, field agents, and operations teams.
The platform is live and operational across Odisha, Andhra Pradesh, and West Bengal, currently serving 6,000+ registered farmers across 150+ villages with active aquaculture ponds covering more than 8,000 acres.
Creuto designed and delivered the complete platform end to end:
A farmer-facing mobile app for pond management, advisory, and market linkage
A field agent and extension worker dashboard for remote monitoring and intervention
An operations and supply chain panel for harvest coordination, cold chain logistics, and export management
A super admin dashboard for executive oversight, analytics, and farmer network management
Creuto acted as a full product partner — shaping the feature set, designing the experience for low-digital-literacy users, architecting the AI layer, and deploying everything to production at agricultural scale.
Farmers lacked tools, data, and market access
Smallholder shrimp and fish farmers across coastal India operated almost entirely without data, digital tools, or institutional support. Their only inputs came from local input dealers and informal peer networks — both with commercial interests that did not always align with the farmer's.
→
No real-time monitoring of dissolved oxygen, salinity, pH, or temperature
→
No way to predict and prevent disease outbreaks before stock losses
→
No optimised feeding schedules balancing cost against yield
→
No direct connection to buyers, processors, or exporters
The result was chronic underperformance: yield losses of 20–35% from preventable disease, feed wastage inflating operating costs by 15–25%, and farmers realising a fraction of international market value.
Post-harvest losses and export inefficiencies compounded the problem
Even when farmers achieved a good harvest, the value chain downstream was broken. Cold chain infrastructure was patchy, grading was inconsistent, and the export documentation and compliance burden was entirely opaque to farmers selling to local middlemen.
→
Cold chain handling losses ran to 10–20% of volume in some corridors
→
Quality rejections at export stage cost time and eroded buyer relationships
→
No leverage to negotiate with processors or exporters directly
→
No visibility into international price benchmarks at point of sale
Operations were manual and non-digital
On the aggregator and operations side, the challenge was equally severe. Coordinating harvest timing across hundreds of farms, managing cold chain logistics, tracking quality through processing, and meeting export compliance — all of it ran on spreadsheets, WhatsApp groups, and phone calls.
→
No single system of record across the farmer network
→
No visibility into pond-level data for forecasting supply
→
Field agents covering 200+ scattered farmers could not visit each pond often enough
→
Extension services existed in theory but rarely reached the farmers who needed them
01
IoT sensors at each pond continuously capture dissolved oxygen, pH, salinity, temperature, and turbidity. Data is transmitted via MQTT to AWS IoT Core, processed in real time, and surfaced to the farmer through the mobile app and field agents through the web dashboard.
Each parameter is compared against crop-stage benchmarks. Anomalies trigger instant alerts. A farmer whose dissolved oxygen drops below the safe threshold at 2 AM gets a push notification telling them exactly what has happened and what action to take — before stock mortality occurs.
02
A machine learning model trained on thousands of crop cycles identifies disease risk patterns 5–10 days before visible symptoms appear. When a pond crosses a risk threshold, the system generates a structured alert specifying the suspected pathogen, the confidence level, the recommended diagnostic steps, and the treatment protocols to follow.
Field agents are notified simultaneously and can coordinate a physical visit. Early detection at this stage of the crop cycle means the difference between a few thousand rupees of intervention and a total stock loss costing lakhs.
03
Feed is the single largest variable cost in shrimp and fish farming — typically 60–70% of operating expenses. The optimisation module integrates pond biomass estimates, water quality parameters, stocking density, weather conditions, and species-specific feed conversion ratios to generate a daily feeding schedule pushed to the farmer's phone each morning.
Farmers using the feed optimisation module consistently report feed cost reductions of 12–18% per crop cycle. For a 3–5 acre farmer, that translates to ₹50,000–₹1,20,000 in annual savings.
04
Farmers without IoT-equipped ponds can log observations manually — feeding response, shrimp behaviour, water colour, mortality counts — and receive structured guidance based on those inputs combined with local weather and historical pattern data. Field agents view all ponds in their assigned geography from a single dashboard, enabling one agent to support 150–200 farms instead of the 30–40 visit-based extension models permit.
Advisory content is delivered in Odia, Telugu, and Bengali in addition to English, designed for farmers reading on basic Android devices over 2G connections.
05
The platform connects farmers directly to processors, exporters, and cold chain operators — bypassing middlemen. Harvest coordination, quality grading, cold chain tracking, and export documentation are all managed through the operations dashboard, giving farmers visibility into international benchmarks and direct buyer relationships for the first time.
06
The Aquapulse operations team manages the entire farmer network from a single panel — tracking pond health alerts, harvest readiness across regions, field agent activity, supply chain status, and platform-wide analytics for executive reporting and investor metrics.
Creuto understood the unique challenge of building for low-digital-literacy users on low-bandwidth connections. They didn't just build software — they built a platform that actually works in the field, for farmers who had never used a smartphone app before. The disease prediction alone has changed how our extension network operates.
Founding Team, Aquapulse
Building an AI platform for agriculture requires deep product thinking, not just engineering execution. Here is what makes Creuto the right partner.
Aquapulse serves farmers on basic Android devices over 2G connections in regional languages. We designed every screen for low-literacy users in the field — not for demo environments.
The disease prediction and feed optimisation models are built into the product's data pipeline from day one. We architected, trained, and integrated the AI — not bolted it on as a third-party widget.
Mobile apps, field agent dashboards, operations panels, IoT integrations, supply chain tooling, and cloud infrastructure — delivered as one coherent platform under one contract.
After discovery, Creuto provides a detailed scope document and a fixed-cost proposal. No vague estimates, no change order surprises — just predictable delivery.
Every technology chosen for low-bandwidth resilience, real-time IoT data ingestion, and the ability to scale across thousands of remote farmer endpoints.
From field research with farmers to production deployment across three states — a structured build process designed for complex, multi-stakeholder agricultural products.
Structured workshops with farmers, field agents, and the Aquapulse team to understand operational workflows, user literacy levels, connectivity constraints, and export compliance requirements. Defined success metrics, AI use cases, and integration scope before design began.
User flows mapped for each role — farmer, field agent, operations team, and super admin. Mobile-first prototypes designed for low-literacy users with voice guidance and illustrated protocols. Validated with field agents on actual devices before development began.
Backend API, IoT data ingestion pipeline, real-time alert engine, and database architecture. React Native mobile app and web dashboard developed in parallel using shared component libraries and typed API contracts.
Disease prediction and feed optimisation models trained on historical crop data and integrated progressively. Validated against real-world performance before going live. Models continuously retrained as new crop data enters the system.
Functional, performance, and security testing across all modules. Edge case testing across low-bandwidth scenarios, multiple device types, and regional language rendering. Data encryption, access control, and audit logging implemented for sensitive farmer data.
Phased rollout across Odisha, Andhra Pradesh, and West Bengal with on-ground agent training. AWS deployment with CI/CD pipelines. Creuto provides ongoing support, model retraining, and feature iteration post-launch.
We work in two-week agile sprints, with field validation at every milestone. Our team flies to actual farms during discovery and rollout — not just because it is good practice, but because building software that runs on a 2G connection in a Telugu-speaking village requires you to be there.
Aquapulse rolled out across three states and 6,000+ farmers — without a single failed crop cycle attributable to platform issues.
Everything you need to know about building a product like this with Creuto.
Yes. The pond monitoring, advisory, and supply chain modules are parameterised for crop type. Extending to additional species or land-based agriculture requires retraining the prediction models on the relevant crop data and adjusting the advisory content library — the underlying architecture remains the same.
The platform is designed with field agents as an intermediary layer. Agents monitor all ponds in their geography from a web dashboard and can push guidance via SMS or voice call for farmers without smartphone access. IoT-equipped ponds report automatically regardless of farmer device capability.
The model is trained on historical data from thousands of crop cycles across the geographies Aquapulse operates in. It identifies high-risk conditions 5–10 days before visible symptoms. Accuracy improves continuously as new seasonal data enters the system. False positives trigger advisory rather than alarm, preserving farmer trust.
A platform of this scope — farmer mobile app, field agent dashboard, operations panel, super admin, AI prediction engine, IoT integration, and cloud infrastructure — typically costs between $60,000 and $150,000 USD depending on AI model complexity, sensor hardware partner choice, and rollout geography. Creuto provides a fixed-cost proposal after discovery.
Yes. As part of our post-launch support engagement, we manage model retraining cycles, monitor prediction accuracy, and update advisory content. The AI layer is treated as a live product component, not a one-time build.
Whether you are working with 100 farmers or 100,000 — Creuto builds AI-powered, low-bandwidth-resilient platforms that actually work in the field, for the users who need them most.
AI prediction. IoT integration. Multi-language. Field-tested. One team. Fixed price.
Book a free consultation11th Floor, O-Hub, Chandaka Industrial Estate, Infocity, Bhubaneswar, Odisha 751024
Level 4, 11 York Street Sydney Startup Hub Sydney, NSW – 2000
30 N. Đinh Nghệ, Phước Mỹ Sơn Trà, Đà Nẵng / Da Nang City – 550000
Level 25, AIDP Business Tower, Dubai Marina, United Arab Emirates
50 Beauchamp Street, Wellington, WGN 5028, New Zealand
© 2026 Creuto All Rights Reserved