Industry
Software engineering for Agriculture
Agritech promises a lot and delivers unevenly, because farming is a thin-margin, seasonal, weather-governed business run in places with no signal. The value is in software that survives that reality: sensor data that still arrives from a field with no coverage, records that make traceability and subsidy claims defensible, and tools that pay back within a season a farmer can actually feel. We build for the field and the margin, not for the venture-funded dashboard that assumes broadband, spare cash and a farmer with time to spare.
Why the domain matters
Agriculture is a business the technology sector consistently underestimates, because it is governed by realities that most software quietly assumes away: thin and volatile margins, hard seasonal deadlines set by weather and biology rather than sprints, harsh outdoor environments, and rural locations where connectivity ranges from patchy to absent. A farm is not a warehouse with better views: it is a large, distributed, weather-exposed operation where a decision missed by a week because the data did not arrive can cost a whole crop, and where there is rarely spare cash or spare time to babysit software that does not immediately earn its keep. Agritech that ignores any of that: that assumes broadband, a farmer with hours to configure a dashboard, or a margin that can absorb a subscription with a vague payback, dies quietly in the second season when the free trial ends and nothing measurable changed.
The sector has a shape worth naming, because software lands very differently across it. Arable and horticultural growers care about precision farming, inputs, yield, and the timing of operations against weather and soil. Livestock farmers care about animal records, health, movement and welfare, and the traceability that follows an animal through its life. Increasingly the pressure comes from the other end of the chain: retailers and processors demand farm-to-shelf traceability and evidence of standards, so a grower’s software has to produce records a buyer downstream will accept. And across all of it sits the state (DEFRA and the Rural Payments Agency and their evolving schemes), whose subsidy and stewardship payments are material to farm income and whose reporting requirements reach directly into what the software must capture and evidence. The genuinely hard parts are not the agronomy models everyone demos; they are getting reliable data out of a field with no signal, and building something with a payback a thin-margin farm can actually feel.
We are a senior-led team and we operate what we build, so we start from the field and the farm’s margin rather than from the precision-agriculture pitch. The recurring failure mode in agritech is the elegant platform that assumes connectivity it will not have, generates insight the farmer has no time or money to act on, or produces records that do not quite match what a retailer or the RPA will actually accept. We would rather build the unglamorous, robust thing: telemetry that buffers and syncs from a dead-signal field, records structured so a traceability audit or a subsidy claim just works, a tool whose payback a farmer can point to within a season, than the venture-funded dashboard that wins a grant and then goes unused. The hard part here is almost never the model; it is the connectivity, the environment, the seasonality and the margin, and we build for those first.
The challenges in agriculture
Rural connectivity you cannot assume is there
Large parts of a farm have poor mobile coverage and no fixed broadband, and the field where the sensor sits or the decision is made is often the worst of all. Software and hardware that assume a live connection simply fail where the work happens. Store-and-forward telemetry, offline-first apps, and honest choices about connectivity technology (cellular, low-power wide-area, local gateways), are not enhancements here; they are the difference between data that arrives and a system that silently goes dark exactly when the field needs it.
Thin margins that demand real, felt payback
Farming runs on tight, volatile margins with little spare cash, so software has to earn its keep in a way the farmer can actually feel (a saved input cost, a subsidy secured, a loss avoided), within a season, not a vague efficiency promised over years. Agritech that adds a subscription without a payback the farm can point to is abandoned when the trial ends. We treat honest, near-term return as a design constraint, because a thin-margin business will not, and should not, carry software that does not pay.
Seasonality and hard biological deadlines
A farm’s calendar is set by weather and biology, not by roadmaps: drilling, spraying, lambing and harvest each have windows that do not move and cannot be missed. Software has to be reliable and ready exactly when the season demands it and tolerate long quiet periods in between, and a failure at harvest is not a bug to fix next sprint: it is a lost window. We build for those immovable peaks and the intense, time-critical bursts of use around them, because the season does not wait for a patch.
Harsh environments for hardware and people
Sensors and devices live in mud, dust, damp, temperature extremes and the reach of animals and machinery, and the people using apps are outdoors in weather with dirty hands. Consumer assumptions about hardware lifespan, power and handling do not hold. Ruggedisation, power budgets for kit that may be nowhere near mains, and interfaces usable outdoors with gloves are core engineering concerns, not afterthoughts, because equipment that fails in the field takes the data and the trust with it.
Traceability that a downstream buyer will actually accept
Retailers and processors increasingly demand evidence of provenance, standards and an unbroken chain from farm to shelf, and a grower’s records only have value if the buyer downstream accepts them. The hard part is producing records structured and complete enough to satisfy a specific customer’s assurance scheme or an auditor, not merely capturing data that looks tidy on the farm. Traceability that does not match what the chain requires is effort spent for nothing when the audit or the buyer’s check comes.
Subsidy and scheme reporting that shapes the data model
DEFRA and RPA schemes (and their ongoing shift toward environmental land management and stewardship), are material to farm income, and their reporting requirements reach directly into what has to be recorded, mapped and evidenced. These are not a report bolted on at year end; the field boundaries, land use, activities and evidence a claim depends on have to be captured accurately as the year runs. Software that treats scheme reporting as an afterthought produces claims that are painful to assemble and easy to get wrong, against payments the farm relies on.
What we build for agriculture
The systems this sector most often needs, built by engineers who understand the domain, not just the code.
Precision farming and agronomy tools
Software that turns field, soil, weather and input data into better-timed, better-targeted operations, variable-rate inputs, timing against conditions, and yield tracking. We build these honestly around what a farmer can act on with the kit and the margin they have, because precision agriculture only pays when the insight leads to a felt saving or a protected yield, not when it produces a beautiful map the farm has no means or reason to use.
IoT sensors and telemetry
Sensor networks and telemetry for soil, weather, water, stores and equipment, engineered for the field, which means store-and-forward buffering, sensible connectivity choices, power budgets for kit far from mains, and ruggedisation against mud, damp and animals. We treat getting reliable data out of a no-signal field as the core problem, because the elegant analytics on top are worthless if the readings silently stop arriving the week they matter.
Farm management systems
Whole-farm systems that pull fields, inputs, operations, labour, machinery and costs into one place a farmer can actually run the business from. We build for the reality of seasonal, time-pressured use and thin margins, reliable at the peaks, undemanding in the troughs, and focused on the records and figures that drive real decisions and real payments, rather than a comprehensive platform that needs constant feeding the farm has no time to give.
Livestock and crop records
Records for animal health, movement, welfare and breeding, or for crop protection, inputs and field history, structured so they double as the evidence traceability, assurance schemes and subsidy claims require. We build these to be quick to capture in the yard or the field and complete enough to stand up to an audit, because in livestock and cropping the record is both an operational tool and the proof the wider chain and the RPA will demand.
Traceability and supply-chain compliance
Farm-to-retailer traceability and the compliance evidence downstream buyers and assurance schemes require, provenance, standards, and an unbroken chain from field or animal to shelf. We build records structured to satisfy the specific schemes and customers your produce actually goes to, because traceability only has value if the buyer or auditor downstream accepts it, and matching their requirements is the real engineering, not capturing data that merely looks tidy on the farm.
Subsidy and scheme reporting (DEFRA/RPA)
Tools that make subsidy and stewardship reporting defensible, capturing field boundaries, land use, activities and evidence accurately through the year so a claim assembles cleanly and stands up. We design the data model around what the schemes actually require, including their shift toward environmental land management, because these payments are material to farm income and a claim built on data captured as an afterthought is painful to assemble and easy to get wrong.
Where we help
Telemetry that still arrives from a field with no signal
A grower wants soil-moisture, weather and store readings they can trust to time irrigation, spraying or drying. The real work is not the dashboard. It is getting reliable data out of a field with no coverage: store-and-forward buffering, the right low-power connectivity, power budgets for kit nowhere near mains, and ruggedisation against mud and animals. Get that right and the readings arrive the week they matter; get it wrong and the sensors go silently dark exactly when the season depends on them, which is the usual fate of agritech hardware.
A farm management system a busy farmer will actually keep up
A mixed farm wants fields, inputs, operations, labour, machinery and costs in one place instead of scattered notebooks and spreadsheets. We build for seasonal, time-pressured reality: quick to capture in the yard, reliable at the harvest peak, undemanding in the quiet months, and focus on the records and figures that drive real decisions and real payments. The value is a system the farmer keeps up because it earns its keep, not a comprehensive platform that needs feeding the farm has no time to give and quietly abandons.
Livestock records that double as traceability and claim evidence
A livestock farmer needs animal health, movement, welfare and breeding recorded for their own management, and structured so the same records satisfy movement reporting, an assurance scheme and a subsidy claim without re-keying. We build capture that is fast in the yard and complete enough to stand up to an audit, because in livestock the record is both the operational tool and the proof the wider chain and the RPA demand. Done well it turns a compliance burden into a by-product of good record-keeping rather than a separate chore.
A subsidy claim that assembles cleanly instead of painfully
A farm relying on DEFRA and RPA payments wants the year’s field boundaries, land use, activities and evidence captured as it goes, so the claim assembles cleanly rather than being reconstructed under deadline. We design the data model around what the schemes actually require, including the move toward environmental land management, so the evidence a claim depends on already exists in the right shape. The payoff is a defensible claim against payments the farm relies on, instead of a stressful year-end scramble against data that was never captured with the claim in mind.
How we build for agriculture
We start from the field, the connectivity and the margin, not from the agronomy model. Before designing anything we want to understand where the signal actually is, how the season shapes the work, what the farm can genuinely afford in cash and time, and where a missed decision or a failed subsidy claim actually costs money. In agritech the constraint is almost never the cleverness of the analytics: it is whether reliable data reaches the decision and whether the tool pays back in a way a thin-margin farm can feel, so we design for those realities first and treat the modelling as secondary.
We are blunt about which agritech pays back and which does not, because the sector is full of software that quietly went unused. Removing real friction: telemetry that arrives from a dead-signal field, a subsidy claim that assembles cleanly, an input cost saved, records that turn traceability into a by-product, is worth building. A dashboard that generates insight the farmer has no time or money to act on, or a subscription with a payback that is vague and years away, usually is not, and will be dropped when the trial ends. We would rather tell you that early and build the thing that pays than let you fund the elegant platform that impresses a grant panel and then sits idle.
We build offline-first and environment-first as a matter of course. The connectivity is not there, the kit lives in mud and weather far from mains power, the people using it are outdoors with dirty hands, and the calendar is set by biology. So we design for store-and-forward data, sensible power and connectivity choices, ruggedisation, and reliability at immovable seasonal peaks, because a system that assumes broadband, mains power and a farmer with spare time is a system built for a farm that does not exist.
Because we operate what we build, the people designing a telemetry pipeline or a subsidy data model are the ones who hear about it when readings stop arriving from a field or a claim will not assemble before the deadline. That keeps us honest about the only things that matter in agritech (did the data reach the decision, and did the tool pay back), and stops us shipping something that demos well to an investor and then goes dark in the second season on the farm it was meant for.
Regulation and compliance
The regulatory landscape in agriculture is dominated by two forces that reach directly into the software: state support schemes and traceability. On the support side, DEFRA and the Rural Payments Agency administer subsidy and stewardship payments that are material to farm income, and their requirements (increasingly built around environmental land management and stewardship rather than area payments), dictate what has to be recorded, mapped and evidenced. Where our software supports a claim, its job is to capture field boundaries, land use, activities and evidence accurately as the year runs, because the compliance lives in the data trail and a claim built on afterthought data is painful to assemble and easy to get wrong against payments the farm relies on.
Traceability and animal-health rules form the other pillar. Livestock movement reporting, animal identification and welfare records, and the food-chain traceability that lets produce be tracked from farm toward the shelf are legal and commercial obligations at once. Retailers and processors layer their own assurance schemes on top, so a farm’s records have to satisfy both the statutory requirements and the specific customer’s scheme. We build records structured to meet what the chain actually demands, because traceability that does not match the buyer’s or the auditor’s requirements is effort spent for nothing when the check comes.
Alongside these sit environmental, plant-health and input-use rules: nutrient and pesticide record-keeping, environmental protections and the evidence that supports both compliance and stewardship claims, and data protection under UK GDPR for the personal data of workers and any land or business owners. We engineer for accurate, retained records and lawful handling of personal data from the start, because in a sector where records underpin both payments and market access, the record is frequently the thing that protects the farm’s income and its ability to sell.
We build systems that meet these obligations, but we are engineers, not your agricultural, environmental or legal advisers. The rules around DEFRA and RPA schemes, animal health and movement, traceability and environmental protection carry real weight (financial and legal), and sign-off rests with your own advisers and the accountable people on the farm. Our job is to build software that captures, structures and retains what those obligations require so a claim or an audit just works, and to work alongside the people accountable for them, not to replace their judgement.
Integration
The defining integration reality in agriculture is the field itself. Before any software integration matters, data has to get out of a large, weather-exposed, poorly-connected place, so the first integration problem is between sensors and equipment in a no-signal field and the systems that need their data, solved with store-and-forward buffering, sensible connectivity technology and power budgets for kit far from mains. We treat that as first-class engineering, because the cleverest downstream integration is worthless if the readings never leave the field they were taken in.
On the machinery and sensor side, farms run a mix of equipment, telematics and sensor brands that rarely share formats: tractors and implements, weather and soil sensors, weighing and store monitoring, and the data standards that partially connect them. We integrate carefully across that heterogeneity rather than assuming a single vendor’s ecosystem, because a real farm accumulates kit over years and decades and the software has to work with what is actually in the yard, not a tidy single-brand fleet.
Downstream, the supply chain and the state are the integrations that carry commercial and financial weight. Traceability has to feed retailer and processor assurance schemes and food-chain records in the shape those buyers accept; subsidy and stewardship reporting has to align with DEFRA and RPA requirements and their mapping of land and activity. We build these integrations around what the buyer or the scheme actually requires, because records that do not match the downstream format are effort wasted exactly when a claim or an audit depends on them.
Underpinning all of it is reconciliation across systems that were never designed to agree: sensor readings, machinery data, hand-entered records in the yard, mapping and land data, and financial figures, kept consistent as they cross a patchwork of kit and platforms with intermittent connectivity. That reconciliation, across an offline-prone environment and a heterogeneous mix of equipment and schemes, is the recurring engineering problem in agritech, and we treat it as core work rather than assuming a clean, connected, single-source farm that does not exist.
Security and data protection
Agriculture software holds data that is more sensitive and more valuable than it first appears: detailed operational records that are commercially confidential, the traceability and assurance evidence that underpins market access, the field and activity data behind subsidy claims worth real money, and the personal data of farm workers and business owners. In a thin-margin business where records underpin both income and the ability to sell, that combination deserves proper care, so we build with access controls scoped to genuine need, encryption in transit and at rest, and retention limited to what a lawful basis and the schemes actually require.
IoT and field devices widen the security surface in ways that need deliberate thought. Sensors and gateways deployed across a large, physically-accessible site, communicating over cellular or low-power networks, are exposed in ways an office system is not: to physical tampering as much as to network attack. We design device authentication, secure communication and sensible update paths for kit that may be remote and rarely visited, because a compromised or spoofed sensor feeds bad data into real decisions and a neglected device becomes a quiet way in.
The records that underpin subsidy claims and traceability deserve particular care for their integrity as much as their confidentiality. A claim worth real money, or the provenance evidence a retailer relies on, has to be trustworthy and defensible, which means protecting the records against undetected alteration and being able to show what was recorded and when. We treat the integrity and retention of that evidence as part of the deliverable, because it is exactly the data whose trustworthiness a payment or a market depends on.
Because we operate what we build, security here is not a report handed over at the end. We instrument for the access patterns and device anomalies that indicate a problem across a distributed, intermittently-connected estate, keep the audit trail a scheme claim, a traceability audit or a data-subject request would need, and treat the ability to reconstruct exactly what was recorded and by which device or person as part of the job, because in agriculture the record protects the farm’s income and market access, and it has to hold up when the claim or the audit comes.
What changes
Data that actually arrives from the field
Because we engineer telemetry for no-signal conditions with store-and-forward buffering and sensible connectivity and power choices, sensor readings reach the decision the week they matter instead of going silently dark, which is the usual failure of agritech hardware that assumed coverage the field never had.
Payback a thin-margin farm can actually feel
By treating near-term, felt return as a design constraint and being blunt about which agritech does not pay, the software delivers a saving, a secured subsidy or a loss avoided the farmer can point to within a season, rather than a subscription with a vague, years-away efficiency that gets dropped the moment the trial ends.
Records that turn compliance into a by-product
Because we structure livestock, crop and field records around what traceability schemes and DEFRA/RPA claims actually require, a subsidy claim assembles cleanly and an audit just works from data captured as the year runs, turning what is usually a stressful year-end scramble into a by-product of good record-keeping the farm was doing anyway.
What we build for agriculture
From a first platform to modernising what you already run. The disciplines this sector draws on most.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves, including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Building something for agriculture?
Tell us the problem and the constraints you are working under. A senior engineer will give you a straight view on what it would take, and say so plainly if we are not the right team for it.
Technologies we work in
Chosen per problem, not per fashion. A selection of the stack we most often reach for.
Why teams in agriculture choose us
We build for the field, not for assumed broadband
Most agritech fails because it assumes connectivity, mains power and a tidy single-brand farm that do not exist. We engineer offline-first telemetry, sensible connectivity and power budgets, and ruggedisation for kit in mud and weather, because the cleverest analytics are worthless if the data never leaves the no-signal field it was taken in.
We are honest about which agritech actually pays
Farming runs on thin, volatile margins with no room for software that does not earn its keep. We treat felt, near-term payback as a design constraint and will tell you plainly which tools deliver a saving or a secured subsidy and which are a dashboard the farm has no time or money to act on, rather than selling insight for its own sake.
We make traceability and subsidy reporting defensible
Traceability only has value if the buyer downstream accepts it, and a subsidy claim only pays if it stands up to the RPA. We structure records around what the schemes and assurance buyers actually require, so claims assemble cleanly and audits just work, turning compliance into a by-product of good record-keeping rather than a year-end scramble.
We operate what we build
The people who design a telemetry pipeline or a subsidy data model are the ones who hear when readings stop arriving or a claim will not assemble before the deadline. That keeps us honest about what actually matters in agritech (did the data reach the decision, and did the tool pay back), rather than shipping something that demos well and goes dark in the second season.
Common questions
Our farm has terrible connectivity, can sensor and telemetry software even work here?
Yes, but only if it is designed for that from the start, which most agritech is not. Poor or absent coverage is the normal condition on a farm, and the field where the sensor sits is often the worst of all, so anything that assumes a live connection fails where the work happens. We build store-and-forward telemetry that buffers readings and syncs when a connection is available, choose connectivity technology to fit the site (cellular, low-power wide-area networks, local gateways), and budget power for kit that may be nowhere near mains. Getting reliable data out of a no-signal field is the core engineering problem in agritech, not the dashboard on top, so we treat it as the first thing to solve rather than an afterthought that leaves the sensors going silently dark the week the season depends on them.
We run on thin margins, how do we know agritech will actually pay back?
You should be sceptical, and we build and advise accordingly. Farming runs on tight, volatile margins with little spare cash or time, so software has to earn its keep in a way you can actually feel (a saved input cost, a subsidy secured, a loss avoided), within a season, not a vague efficiency promised over years. We treat that near-term, felt payback as a design constraint, and we will be blunt when a proposed feature is really just a dashboard that generates insight you have no time or money to act on. We would rather build the narrower thing that clearly pays and can be pointed to than sell you a subscription that gets quietly dropped the moment the trial ends, because in this sector that is the honest difference between a tool that lasts and expensive shelfware.
How do you handle DEFRA and RPA subsidy and scheme reporting?
As a design input that shapes the whole data model, not a report bolted on at year end. DEFRA and RPA payments are material to farm income, and their requirements (increasingly built around environmental land management and stewardship rather than area payments), dictate what has to be recorded, mapped and evidenced. We design the software to capture field boundaries, land use, activities and evidence accurately as the year runs, so a claim assembles cleanly and stands up rather than being reconstructed under deadline from data that was never captured with the claim in mind. To be clear about the boundary: we are engineers, not your agricultural or scheme advisers, so sign-off on eligibility and claims rests with your own advisers and the accountable people on the farm, and we build the software so their claim just works.
Can you build traceability that our retailer or processor will actually accept?
That is exactly the right way to frame it, because traceability only has value if the buyer downstream accepts it. Retailers and processors demand evidence of provenance, standards and an unbroken chain from farm toward the shelf, and they each layer their own assurance schemes on top of the statutory movement and food-chain rules. The hard part is not capturing data that looks tidy on the farm: it is producing records structured and complete enough to satisfy the specific schemes and customers your produce actually goes to, and the statutory requirements underneath them. We build records to match what your chain requires, ideally so the same livestock or crop records that run the farm double as the traceability and assurance evidence, turning a compliance burden into a by-product of good record-keeping rather than a separate chore that fails the audit.
Is precision farming worth it for us, or is it overhyped?
Both, depending on the farm, and we will tell you straight rather than sell the hype. Precision farming genuinely pays when the insight leads to a felt saving or a protected yield you have the kit and the margin to act on: variable-rate inputs where the variation is real, timing operations against reliable soil and weather data, tracking yield to inform next season. It does not pay when it produces a beautiful map the farm has no means or reason to use, or when the data collection costs more in cash and time than the decisions it improves are worth. We start from what you can actually act on with the equipment and margin you have, and we would rather scope you into the parts of precision agriculture that pay back than fund a comprehensive platform that generates insight nobody has the time or resources to use, which is how a lot of well-funded agritech ends up unused.
Building for agriculture?
Tell us what the system has to do and what it cannot get wrong. A senior engineer reads it, and if agriculture is not a domain we know well enough to be useful in, we will say so rather than learn it on your budget.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.