01
Farm evidence lives in different places
Lab reports, field notes, activities, crop history and observations are difficult to review as one agronomic picture.
The challenge
Great agronomy gets harder to scale.
As your client base grows, more time disappears into collecting evidence, writing reports and keeping recommendations consistent. The expertise is there. The workflow around it is often fragmented.
02
Recommendation writing takes time
Copying results, checking calculations and formatting farmer-ready advice can consume hours that could be spent interpreting and advising.
03
Consistency gets harder as workload grows
When every recommendation is assembled manually, structure and depth can vary between farms, seasons and advisors.
04
Your expertise is difficult to reuse
Target values, preferred methods and agronomic logic often stay in spreadsheets, documents or individual experience instead of becoming repeatable workflows.
05
Advice can become disconnected from action
A recommendation is more useful when the resulting field activities and follow-up remain connected to the farm record.
How SoilBeat works
One connected agronomic workflow.
SoilBeat connects the work around a recommendation — from the evidence you start with to what happens after the advice is delivered. Explore each step to see how the workflow comes together.
New evidence feeds the next decision — keeping the agronomic cycle connected.
01 · Measure
Bring the evidence together without rebuilding the farm picture.
SoilBeat brings reports, measurements, field observations and farm context into one structured record — so you spend less time preparing information and more time interpreting what it means.
01Import and structure the evidence
Prepare the evidence once. Keep using it throughout the agronomic workflow.
02 · Interpret
See more context. Reach better conclusions.
SoilBeat helps you move beyond isolated lab results by comparing trends, connecting measurements to field events and exploring the full farm record before you recommend.
01Compare trends, not just snapshots
03 · Recommend
Turn better interpretation into farmer-ready advice — faster.
SoilBeat helps you move from evidence and agronomic judgement to a clear recommendation without rebuilding the report from scratch. Review the draft, adjust it with your own expertise and deliver it in the format that works for your clients.
01Build the recommendation from the evidence
The point
Automate the repetitive work around the recommendation — not the agronomist’s judgement.
04 · Act
Turn recommendations into work that actually gets done.
A recommendation becomes more useful when the next action is already clear. SoilBeat connects published advice directly to the Activities module, giving farmers, agronomists and field technicians a simple way to move from recommendation to execution.
Publish the recommendation. Create the work.
When a recommendation is ready and published, its fertilisation and crop-protection activities appear on the farmer’s Activities screen — without having to create the tasks again.
Give the farmer a simple to-do list.
The farmer can see what needs to happen and, once the activity is completed, simply change its status to done.
Assign work to the right person.
Activities can also be assigned to field technicians, keeping the recommendation and the people carrying out the work connected.
Create activities by voice while you are in the field.
Use mobile voice input to capture and create activities while walking the field, reducing note-taking and follow-up administration for both agronomists and farmers.
For the farmer
A clear list of what needs doing, with less administration and a simple way to confirm when the work is complete.
For the agronomist
Less time translating recommendations into separate tasks, easier delegation and a clearer view of what happens after the advice is published.
Recommendation → action
Published recommendations create actionable work in the farm record, keeping advice connected to execution and giving the next agronomic decision better context.
→05 · Monitor
What happens next makes the next recommendation better.
Completed activities, field observations and new measurements become part of the farm record. So the next agronomic decision starts with more context about what was advised, what was done and what happened afterwards.
01ACTIVITY
Know what was actually carried out
Completed activities provide context on the fertilisation, crop protection and other work that has taken place after the recommendation.
02OBSERVE
Bring new observations back into the farm record
As the season develops, field observations, scouting information and new measurements add another layer of evidence.
03LEARN
Build the next decision on what happened before
The next interpretation can use the connected history instead of requiring the agronomist to reconstruct the story from separate notes, files and conversations.
Close the loop
Advice becomes action. Action creates new context. New context improves the next agronomic decision.
What you get back
Better advice. Less admin. More time for agronomy.
SoilBeat takes the repetitive work around a recommendation out of the way, so you can bring more context into your advice, keep your judgement in control and spend more time with growers.
Better recommendations
Bring structured measurements, farm history, observations and activities into the same workflow, so more of the relevant agronomic context is available when you interpret and advise.
More context at the point of judgement.
Less time managing data and reports
Import lab reports, structure the evidence, compare trends and start recommendations from connected farm data instead of copying values between PDFs, spreadsheets and reports.
Reduce repetitive preparation and report writing.
More time to work with growers
Keep recommendations, activities and follow-up connected, so less time disappears into chasing updates and reconstructing what happened after the advice was sent.
Spend more time on decisions and conversations.
Why SoilBeat, not generic AI
AI is only as useful as the agronomic context behind it.
Generic AI can help answer questions. SoilBeat is designed to work inside the agronomic workflow — with structured farm data, connected context, agronomic intelligence and the expertise your organisation chooses to add.
Explore SoilBeat agronomic intelligenceGeneric AI
A prompt goes in. An answer comes back.
Useful for general reasoning and drafting, but the model starts from the information you provide in that interaction.
01Prompt
02Model response
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Context often has to be uploaded or explained again.
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Farm history, activities and observations are not inherently connected.
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The answer is separate from the workflow that turns advice into action.
SoilBeat
The intelligence works on the farm record.
SoilBeat combines structured agronomic data with the connected farm context and recommendation workflow before AI is applied.
01Structured farm data
02Agronomic context
03Agronomic intelligence
04Recommendation
✓
Measurements stay connected to farms, fields, crops, dates, activities and observations.
✓
Interpretation can use the broader agronomic history instead of one isolated document.
✓
Recommendations can work with the fertilisers and crop-protection products the user specifies.
✓
Your proprietary AI agent and specialist recommendation modules can add organisation-specific knowledge on top.
The difference
SoilBeat is not simply an AI interface for agronomy. AI operates on a structured agronomic system that connects evidence, interpretation, recommendations, activities and follow-up.
Scale your expertise
Turn the way you advise into reusable knowledge.
Once the core advisory workflow is working, SoilBeat can help you capture what makes your advice yours — your methodology, specialist logic, products, priorities and way of working — and make it reusable across more recommendations.
Your methodology
Keep your logic, priorities, terminology and recommendation approach close to the workflow instead of leaving them in documents, spreadsheets or individual experience.
Your way of advising stays at the centre.
Your modules
Turn specialist methodology into reusable recommendation modules for recurring questions, calculations or areas of expertise.
Repeat specialist knowledge without rebuilding it.
Your brand
Deliver recommendations using your own language, structure and branding, so the grower experiences your advisory practice — not a generic AI output.
Your expertise. Your presentation. Your relationship.
Your commercial model
Where the model fits, proprietary agronomic knowledge can become a reusable knowledge asset that supports new services, differentiated advisory products or monetisation.
Turn know-how into something that can scale.
The goal is not to replace the agronomist. It is to make good agronomy easier to repeat, extend and build on.
See how expertise can be configured