Mayur Patel
Mar 27, 2026
6 min read
Last updated Mar 27, 2026

Univia built a voice-first AgriTech platform now used by 5,000+ farmers, many with limited digital literacy, without compromising usability or system performance. Instead of forcing users to adapt to technology, the system was designed to work the way farmers already operate: voice-led, localised, and simple. The result is a platform that delivers real-time agricultural advisory, market data, and support in a format that doesn’t require training or technical understanding.
This case study breaks down how Linearloop helped design and deliver a system that balances accessibility, scale, and performance under real-world constraints such as low connectivity, high usage variability, and a non-technical user base. Go through the full case study to understand how the architecture, design decisions, and trade-offs came together to make this work.

Univia is an AgriTech platform founded by Ravi Varmora, built to address structural gaps in Indian agriculture filled with fragmented supply chains, unreliable inputs, and limited access to timely advisory. The platform focuses on delivering real, actionable agricultural support across the farming lifecycle, from soil-level insights to market access.
At its core, Univia operates with a farmer-first approach. The goal is not just digitisation, but accessibility, making critical information like weather forecasts, mandi prices, and crop guidance available in a format farmers can actually use. It positions itself as a practical support system.
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The North Star was to build a full-stack agricultural ecosystem that supports farmers across the entire lifecycle, beginning from soil analysis, to crop planning, real-time advisory, and market access. The goal was to create a system where farmers could get accurate, timely guidance and act on it without friction.
Accessibility was central to this. Every layer, from advisory to data delivery, had to work in real-time and in formats farmers could actually use. That meant simplifying interactions, localising content, and ensuring the system could deliver insights like weather updates, mandi prices, and crop calendars without requiring technical effort from the user.
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The system had to work within real-world constraints that directly impacted architecture, design, and execution decisions:

Success was not defined by traffic, installs, or surface-level engagement. It came down to whether farmers could use the platform without assistance and consistently rely on it for day-to-day decisions. If a user with no technical background could open the app, ask a question, and get a usable answer without friction, the system was working.
The second layer of success was seamless interaction between farmers and experts. Voice queries had to move through the system quickly, get resolved efficiently, and return meaningful responses without delays or breakdowns. Adoption by farmers and repeat usage became the only signals that mattered.
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The system had to handle multiple high-risk engineering challenges simultaneously, where failure in any layer would directly impact usability and trust for non-technical users:
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The stack was selected to balance performance, scalability, and SEO while supporting real-time interactions and low-bandwidth usage.
| Layer | Technology | Purpose |
| Frontend | Next.js | Server-side rendering for fast load times and SEO optimisation |
| Backend | PHP (Laravel) | Core application logic and admin dashboard management |
| Backend (APIs) | Node.js | Scalable API layer for handling concurrent requests and real-time operations |
| Database | MongoDB | Flexible storage for unstructured data like voice notes and user queries |
| Mobile | Android Native | Lightweight, responsive mobile experience tailored for farmer usage conditions |
The system was designed around how farmers actually interact with information, not how typical apps are structured. Every architectural decision prioritised accessibility, low friction, and reliability under real-world constraints like poor connectivity and low digital literacy. Instead of layering features, the focus was on building a system that could translate complex agricultural workflows into simple, usable interactions while still maintaining backend robustness and scalability.
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The team followed an Agile workflow structured around Kanban to maintain continuous progress without blocking development cycles. Work was broken down into small, actionable tasks, allowing features like voice query handling, dashboard workflows, and localisation layers to be developed and tested independently. This ensured steady delivery without overloading any part of the system.
Iteration was key to reducing risk. Features were released in controlled phases, validated quickly, and refined based on performance and usability. This approach helped balance speed with stability, especially under a fixed timeline and evolving requirements around user behaviour and system load.
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The system relied on external data sources and communication layers to deliver real-time, actionable insights to farmers. These integrations ensured that critical information like weather conditions and market prices was always up to date and accessible within the platform.
| Integration Type | Source | Purpose |
| Weather data | Third-party weather APIs | Deliver real-time weather forecasts to support crop planning and decision-making |
| Market data | Government / mandi APIs | Provide live mandi prices to help farmers make informed selling decisions |
| Communication systems | Email services | Enable notifications, alerts, and system-level communication with users |
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The platform achieved measurable adoption with 20,000+ downloads and 5,000+ active farmers using it for daily decision-making. More importantly, the system handled increasing volumes of voice queries and real-time data without downtime, validating the backend architecture under real-world conditions. SEO improvements also strengthened discoverability, ensuring the platform could scale its reach alongside usage.
The system was built with scalability in mind, allowing future expansion across data sources, advisory layers, and integrations. The architecture supports additional APIs, deeper personalisation, and extended workflows without requiring a rebuild. This ensures the platform can evolve as usage grows while maintaining the same level of accessibility and performance.
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Univia proves that building for real-world users requires more than clean interfaces and modern stacks. It requires aligning system design with how people actually think, speak, and operate. By prioritising voice, localisation, and simplicity, the platform removed friction at every layer and delivered a system farmers could trust and use without assistance.
If you’re building products where usability, scale, and real-world constraints collide, this is where most systems break. Linearloop helps you design and engineer systems that work in production. Reach out to Linearloop if you’re looking to build something that actually gets used.
Mayur Patel, Head of Delivery at Linearloop, drives seamless project execution with a strong focus on quality, collaboration, and client outcomes. With deep experience in delivery management and operational excellence, he ensures every engagement runs smoothly and creates lasting value for customers.