Luniby Farm: An AI-Ready Livestock & Farm Management Platform

Luniby Farm is a digital livestock and farm management platform, designed and built by Sovata, that replaces the notebooks, whiteboards and scattered spreadsheets farms still rely on with a single, cloud-synced source of truth. It centralises livestock profiles, breeding history, health and vaccination records, weight and production data, and day-to-day operational workflows into one clear, mobile-friendly experience — cutting paperwork, improving traceability, and deliberately laying the data foundation for AI-powered agriculture.

Agriculture is one of the least digitised major industries, and it is not for lack of data. A working farm generates an enormous amount of it every day — which animal, which paddock, which treatment, which weight, which offspring — but most of that data lives in a notebook in a coat pocket, a whiteboard in the shed, or a spreadsheet that only one person understands. When the data is trapped like that, it cannot be trusted, traced or acted on, and the farmer carries the whole operation in their head.

Luniby Farm was built to change that, starting with the least glamorous and most valuable move in any digital transformation: making the record reliable. Sovata designed and delivered a platform that captures livestock and farm data once, keeps it in one cloud-synced place, and makes it instantly retrievable on the device a farmer actually has with them. The strategic outcome is not just tidier records. It is a digitised operational backbone — accurate, traceable, and structured — that turns an analogue farm into an organisation ready to adopt AI-powered decision-making rather than one still hunting for last season’s numbers.

Luniby Farm exists because the tools most farms use to manage their most valuable assets are not really tools at all. Livestock represent significant capital, careful breeding decisions and strict health and traceability obligations, yet the records behind them are frequently paper-based, easily lost and impossible to search. The consequence is not just inconvenience; it is lost history, poor traceability and decisions made on memory rather than evidence. Luniby Farm replaces that with a purpose-built system designed around how farms actually work.

It serves the full range of livestock operations — dairy, beef, sheep and goat farms, commercial livestock businesses, and smaller lifestyle blocks — and the people who run them, from hands-on farm owners to professional farm managers overseeing large herds. That breadth is intentional. The core needs are remarkably consistent across farm types: know your animals, track their health and breeding, record what happens, and be able to prove it. Building around those shared fundamentals lets Luniby Farm serve a wide market without fragmenting into one-off products.

The market opportunity is a structural one. AgTech adoption is accelerating, driven by rising traceability requirements, tighter margins that reward efficiency, and a generational shift toward farmers comfortable running their operation from a phone. The business value Luniby Farm creates is the foundation that everything else depends on: accurate, centralised, real-time data. That is the prerequisite for compliance, for efficiency, and — increasingly — for the AI-driven insights that will define the next decade of farming.

The core challenge is that many farms still run on disconnected, analogue tools, and every downstream problem flows from that single fact.

Framed correctly, this is a digital transformation opportunity, not merely a software gap. The absence of a reliable digital record is what blocks everything a modern farm wants next: better traceability, real operational visibility, and the ability to use AI. You cannot forecast production, predict disease risk or automate a workflow on top of a notebook. Luniby Farm was built to remove that blocker first.

Discovery started from a hard constraint that shapes everything about good farm software, and worked outward from it in four moves.

1. 01

   Design for the paddock, not the desk

   The user is often standing in a paddock, in poor weather, wearing gloves, with a spare thirty seconds and patchy connectivity. Any design that assumes an office, a keyboard and a stable connection has already failed — so fast, forgiving, mobile and offline-tolerant capture became the defining requirement.

2. 02

   Understand owners and managers

   Farm owners of smaller operations need something they can pick up with no training, because they are the whole team. Managers of larger herds need the same fast capture plus multiple staff, role-based access and reporting. We made capture effortless for everyone, then layered management capability on top.

3. 03

   Learn from the AgTech landscape

   Livestock software that is powerful but complex stalls on farms because daily capture is too slow — if recording an event is a chore, it does not get recorded and the data decays. The opportunity was to win on the fundamentals: effortless capture, instant retrieval, reliable sync, minimal training.

4. 04

   Map the AI opportunities

   We mapped where AI would create value — disease-risk prediction, breeding recommendations, production forecasting — and structured the data model from day one to support them, because the value of agricultural AI is entirely determined by the quality of the record beneath it.

Luniby Farm’s design follows from a few deliberate strategic commitments, each made to serve the reality of the farm rather than the convenience of the software.

Digitise the record first

Before any intelligence, the platform’s job is to make the core record reliable, centralised and complete. Everything else is built on that — a trustworthy single source of truth for the whole farm.

Capture must be effortless

The platform lives or dies on how fast and forgiving daily data entry is, so speed at the point of capture outranks almost every other consideration. The payoff is records that actually get kept, so the data stays complete.

Mobile and offline-friendly

The work happens in the field, not the office, so a mobile-first, offline-tolerant experience is the baseline, not a companion app. The tool works where and when the farmer does.

Serve many farm types from one core

Dairy, beef, sheep, goat and lifestyle farms share the same fundamentals, so a common core with role-based access serves them all — one platform, a broad market, no fragmentation.

AI-ready by data design

A clean, structured, well-labelled data model is the real prerequisite for agricultural AI, so we engineered for it from the start. Predictive farming can be added later without re-collecting data.

The design philosophy for Luniby Farm can be summed up in one sentence: assume the hardest realistic conditions and design for those. If the platform is fast and clear for a farmer in the rain with one free hand, it is effortless for a manager at a desk. That principle drove every interface decision.

Data entry and retrieval

Data entry is engineered to be quick and low-friction, because a slow form is a form that stays blank. Recording a health event, a weight or a breeding record is designed to take seconds, with sensible defaults and minimal typing. Retrieval is treated as equally important: an animal’s full profile — its identification, breeding history, health and vaccination record, and weights — is a fast search away, so a farmer can answer a question about any animal on the spot rather than trekking back to the shed.

Dashboards, navigation and information architecture

The farm dashboard gives an at-a-glance operational picture, surfacing what needs attention rather than burying it in menus. The information architecture follows the farm’s own structure — animals have profiles; profiles accumulate events like health treatments, vaccinations, weights and breeding; activity logs record what happened and when. Because the model mirrors how farmers already think about their stock, navigation is intuitive and the platform needs almost no training. Role-based access lets a manager give staff exactly the right level of access without exposing the whole operation.

Mobile, offline and accessibility

A mobile-friendly, offline-tolerant experience is central, not optional: connectivity on a farm is unreliable, so the design anticipates capture now and sync later, with cloud synchronisation keeping every device consistent once a connection returns. Accessibility was treated as practical robustness — large, legible type and generous touch targets for use with gloves in bright sun or low light, clear focus states, and interactions that forgive the odd mis-tap. The visual hierarchy keeps each screen focused on the one task at hand, which is what makes the platform usable in the few seconds a busy farmer can spare.

Luniby Farm brings the whole operation into one connected system, organised around the way farms actually run.

Luniby Farm delivers clear value today simply by digitising and centralising the farm record. But the reason we engineered the data model so carefully is that agriculture is one of the most promising frontiers for applied AI — and the opportunities we mapped during discovery become achievable precisely because the foundation is clean.

Because animals, health events, breeding and weights live in a clean, well-labelled relational model, AI becomes an additive layer rather than a rebuild. The opportunities run from disease-risk prediction and breeding recommendations to production forecasting, a natural-language herd assistant, smart reminders and workflow automation, and — further out — voice capture and computer-vision animal identification. The Future opportunities below set these out in full.

This is Sovata’s core discipline applied to our own product. We would sequence these the way we advise any organisation to: start with the use case that combines the highest value with the cleanest available data — smart reminders and natural-language record search are the natural first steps — prove it against real farm workflows, keep the farmer in control of consequential decisions, and expand from there. Predictive livestock health and computer-vision identification are the destination; a reliable digital record is how you earn the right to build them.

1. 01

   Discovery & framing

   Studied real field conditions and the two user roles, learned from the AgTech landscape, and mapped the AI opportunities — before any code — so the whole build served the farm rather than the software.

2. 02

   Data model & product design

   Designed the data model as the core asset — standardised animal identification, structured event types and clean relationships — then designed fast, forgiving capture and instant retrieval around how farmers already think.

3. 03

   Build on a scalable stack

   Built the platform on Next.js, React and TypeScript with Supabase, PostgreSQL and cloud sync, tuned for a capture experience fast enough that farmers keep using it and a data layer clean enough for AI.

4. 04

   Ship & synchronise

   Delivered a working, cloud-synced, role-based platform with offline-tolerant capture — a real system of record, not a proof of concept.

The technology serves two goals at once: a capture experience fast enough that farmers keep using it, and a data layer clean enough that AI can be built on it later. A well-structured relational model is not a technical nicety here — it is the single most important asset the platform creates.

Luniby Farm demonstrates the full arc of Sovata’s capability — from AI and digital-transformation strategy through product and interface design to full software delivery and AI-ready architecture.

• AI Strategy
• Digital Transformation
• Product Strategy
• UX Research
• UX/UI Design
• Interaction Design
• Dashboard Design
• Information Architecture
• Software Design
• Technical Architecture
• AgTech Product Design

Luniby Farm’s outcomes are described as delivered capability — what now exists and what it makes possible — rather than metrics we cannot independently verify. That is consistent with how Sovata reports results everywhere.

Luniby Farm matters because it addresses the single biggest barrier to progress in agriculture: the absence of a reliable digital record. The business impact is compounding. Accurate, centralised data reduces administration, strengthens traceability and gives managers real operational visibility — and each of those is worth money on a working farm. The competitive advantage, both for the farms that adopt it and for Luniby Farm as a product, is being ready for what comes next: an operation with clean data can adopt AI-driven forecasting and automation, while one still on paper cannot.

The customer impact is felt daily — less time on paperwork, faster answers about any animal, and a farm whose knowledge no longer lives in one person’s head. The operational efficiency comes from capturing data once and using it everywhere, instead of recording, re-recording and reconciling. And the return on the investment grows over time, because the value of a livestock record compounds as its history deepens and as AI turns that history into foresight. This is digital transformation in its truest sense: not a new app, but a new foundation.

The clearest lesson is that in field-based software, adoption is won or lost at the point of capture. A powerful platform that is slow to enter data into will quietly fail, because incomplete data is untrustworthy data. Optimising ruthlessly for fast, forgiving, mobile, offline-tolerant entry is not a UX nicety in agriculture — it is the difference between a product that becomes the farm’s system of record and one that gets abandoned within a fortnight.

The second lesson is that the most valuable thing an AgTech platform builds is not its interface but its data model. Getting animal identification, event structure and relationships right from the start is what makes every future AI capability possible — and getting them wrong is a debt that no amount of later modelling can fully repay. The third is the discipline we bring to every engagement: deliver real value now by digitising the record, and earn the right to add AI by making that record clean. Foundations first, intelligence second.

The roadmap layers intelligence and connectivity onto the same reliable foundation, moving the platform from record-keeping to decision-support.