Industry

Digital, software and AI for agriculture and agritech

A sector where subsidy compliance, buyer requirements and new supply chain regulation all demand data that farms have never systematically collected.

Discuss your situation All services

Agriculture is being asked for data from three directions simultaneously. Subsidy schemes require evidence of practice, buyers impose their own assurance standards, and new supply chain regulation requires proof of where a commodity was grown — and none of it is data most farm businesses have collected in a usable form.

For agritech companies selling into that market, the constraint is different: a customer base with limited connectivity, seasonal availability and a well-earned scepticism about software that was designed by people who have never stood in a field.

Why this sector is moving now

The EU deforestation regulation requires operators placing certain commodities on the market to demonstrate the land they came from was not deforested, with geolocation data down to the plot. That is a supply chain data obligation of a kind agriculture has not previously faced.

Subsidy frameworks increasingly tie payment to demonstrable practice rather than to area alone, which means the evidence becomes the payment condition and record-keeping becomes financially material.

Connectivity in the field remains genuinely poor in much of Europe, which is not an inconvenience but an architectural constraint. Software that assumes a connection is software that will not be used where the work happens.

The pressures behind it

Supply chain traceability: Geolocation-level evidence of origin required by new deforestation rules.
Subsidy evidence: Payment tied to demonstrable practice rather than area, making records financially material.
Buyer assurance standards: Retailer and processor requirements that exceed statutory obligations.
Field connectivity: An architectural constraint rather than an inconvenience.
Seasonal availability: Customers unreachable for months at a time, which shapes both sales and support.
Margin volatility: Input and commodity prices moving faster than planning cycles.

Where the work usually starts

The systems and channels a agricultural businesses typically needs joined up. Most engagements begin at one spoke and widen only if it earns it.

Usually field record capture that works offline, because everything else — subsidy evidence, buyer assurance, traceability — depends on data being recorded where and when the work happens rather than reconstructed in an office afterwards.

Traceability and reporting assembly follow. For agritech companies, the equivalent starting point is usually making the product usable without connectivity rather than adding features.

Marketing and brand for agricultural businesses

Brand Strategy & Development: Producer positioning is increasingly about provenance and practice rather than commodity, and buyers pay for evidence rather than claims. For agritech, positioning against scepticism means demonstrating field credibility before capability.
Brand Management: For producers selling direct, consistency across packaging, market stalls and online. For agritech, consistency between what the marketing promises and what works without a signal, which is where credibility in this sector is won and lost.
Social Media Strategy: Farming has a genuinely engaged social community, and producers who show the work build direct customer relationships that command premium. For agritech, that same community is where product credibility is established or destroyed.
Social Media Management: Content is captured during work rather than produced separately, which suits the sector well. Seasonality means cadence varies enormously through the year and that should be planned rather than fought.
Content Creation & Creative Production: Showing production honestly is the strongest asset a producer has, particularly against provenance claims competitors cannot evidence. For agritech, field footage of the product working is worth more than any specification sheet.
Digital Marketing: For direct-selling producers, local and seasonal with sharp peaks. For agritech, long agricultural buying cycles tied to season and capital planning, which makes anything measured monthly misleading.
Paid Advertising: Narrow and specific for agritech — equipment, input and software searches with clear intent. For producers selling direct, local and seasonal with limited volume.
Search Engine Optimisation: Practical content on practice, inputs and problems ranks and reaches farmers researching decisions. It is one of the sectors where genuinely useful technical content still faces relatively little competition.
Email, SMS & WhatsApp Marketing: SMS works in agriculture where email frequently does not, because a phone is what is to hand. Seasonal timing matters more than frequency, and messages arriving during harvest are ignored regardless of content.
Lead Generation & Prospecting: For agritech, agricultural registers and holding data support targeted prospecting in several markets. For producers, buyer and processor relationships are a finite known list.

IT, software and AI for agricultural businesses

Website Design & Development: Has to work on a poor connection on a phone in a yard, which is a genuine performance constraint rather than a general principle. For direct-selling producers, ordering and collection information do most of the work.
CRM & Sales Systems: Long cycles tied to season and capital planning for agritech, and buyer relationship management with contract and delivery schedules for producers. Neither resembles a standard sales funnel.
Business Process Automation: Subsidy claim assembly, assurance scheme documentation, movement records and input purchasing. Claim assembly is directly financial because the evidence is the payment condition.
AI Automation Systems: Extraction from delivery notes, input records and assurance documentation. Imagery-based crop and livestock analysis exists and is specialist agronomy territory rather than something we would take on alone.
AI Knowledge Bases & RAG: Scheme rules, assurance standards and agronomic guidance, retrievable with citations. Subsidy rules in particular are complex, change annually and carry financial consequences for getting wrong.
AI Voice & Customer Communication: Genuinely useful in a sector where the customer has hands full and no signal for typing. Voice capture of field records is a better fit here than in most sectors.
Custom Software & Platforms: Justified where farm management software does not model an unusual enterprise mix or contract structure. Offline-first is a requirement rather than a feature, and it is where generic platforms fail.
Data Engineering & BI: Margin by enterprise and field, input cost per unit of output, and yield against variable inputs. Most farm businesses know the whole-farm result and cannot see which enterprise produced it.
Cloud, DevOps & Infrastructure: Offline capability and synchronisation are the defining requirements. Anything requiring connectivity at the point of work will be abandoned regardless of how good it is.
Systems Integration: Machinery telematics to records, records to subsidy claims, buyers to delivery data, everything to accounting. Machinery data formats are fragmented and vendor-specific, which is the main integration difficulty.
Digital Transformation Consulting: The audit usually finds records reconstructed in an office from memory and paper, which is both a compliance exposure and the reason enterprise-level margin is invisible.
Maintenance & Ongoing Support: Subsidy scheme rules and assurance standards change annually and the deadlines are absolute. Maintenance here is compliance work on an agricultural calendar.

What is specific to this sector

The EU deforestation regulation requires operators placing cattle, cocoa, coffee, palm oil, rubber, soya and wood on the EU market to demonstrate the commodity was not produced on land deforested after the cut-off date, supported by geolocation data for the plots concerned. This is a supply chain data obligation that reaches producers well outside the EU and requires plot-level records most supply chains have never held.

Subsidy schemes increasingly condition payment on evidence of practice rather than area alone, which makes field records financially material rather than administrative. An incomplete record is a reduced payment, and reconstruction after the fact is both unreliable and, in an inspection, unconvincing.

Agricultural data ownership is contested territory. Machinery telematics, agronomy platforms and buyer portals all collect data about a farm business, and who may use it for what is frequently unclear in the contracts. It is worth reading those terms before integrating a platform, because the answer is sometimes unwelcome.

Nutrient and plant protection product application is recorded under national implementations of the Nitrates Directive and plant protection rules, with records that must be retained and produced on inspection. These are field-level records tied to date, area and product, which is exactly the data that gets reconstructed from memory when capture does not work where the work happens.

Plant material moving within and into the EU requires plant passports and phytosanitary documentation, with traceability obligations on the operators handling it. For nurseries, propagators and anyone trading plant material this is a per-consignment documentation burden that sits alongside, and is usually kept separately from, the commercial records.

Not legal or regulatory advice. Sector rules described here are scoping context, current to our latest review. Confirm what applies to your business with a qualified adviser.

Questions

Does the deforestation regulation affect us?

If you place cattle, cocoa, coffee, palm oil, rubber, soya or wood on the EU market, or supply someone who does, very likely. It requires plot-level geolocation evidence, which is a data obligation most supply chains have not previously carried. Confirm your position with a specialist adviser.

Why does offline capability matter so much?

Because connectivity in the field is genuinely poor across much of Europe, and software that requires a signal at the point of work will not be used there. Records then get reconstructed in an office, which is where compliance exposure comes from.

Where is the return for a farm business?

Usually subsidy claim assembly and enterprise-level margin visibility. The first is directly financial because evidence conditions payment; the second changes what the business chooses to grow.

We are an agritech company — what is different?

Your customers have seasonal availability, poor connectivity and justified scepticism about software designed by people who have not used it in the field. Product credibility is established by working without a signal before it is established by features.

Who owns our farm data?

Frequently unclear, and worth reading the terms of any machinery, agronomy or buyer platform before integrating it. The answer in the contract is sometimes not the answer the farm assumed.

Can AI analyse crop imagery?

It exists and it is specialist agronomy work rather than general software engineering. We would bring in or recommend domain specialists rather than take that alone, and we would say so rather than improvise.

What does it cost?

Quoted per phase after a discovery call, and scheduled around the agricultural calendar. Delivery during harvest or drilling is not realistic and we would not propose it.

Do buyers ask for more than the regulator does?

Frequently, yes. Retailer and processor assurance standards commonly exceed statutory requirements and each buyer has its own scheme. The practical goal is capturing field data once in a form that can be reshaped for any of them, rather than maintaining a separate record for each scheme.

Why is machinery data hard to integrate?

Formats are fragmented and largely vendor-specific, and the useful data frequently sits behind a manufacturer platform rather than in an open standard. Integration is achievable but it is per-vendor work, and it is worth scoping honestly rather than assuming one connector covers the fleet.