Agriculture & Agribusiness

Data & AI solutions for more resilient, profitable agriculture

Modernise data foundations to support precision agriculture and traceability
Deploy AI models that improve yield, input efficiency, and asset uptime
Scale from pilots to secure, maintainable production systems

When our agriculture expertise is a strong fit

We’re most effective when there’s strategic intent to use data and AI to improve operational and commercial performance, not just run isolated pilots.

You have data, but it’s fragmented across systems and regions

Operational, agronomy, and commercial data exist but are siloed in legacy ERPs, spreadsheets, dealer systems, or OEM platforms.

You want to move beyond agritech pilots to scaled rollout

You’ve run PoCs or vendor pilots that showed promise, but lack the internal capacity to turn them into robust, maintainable products.

You manage complex, multi-party supply chains

You coordinate growers, cooperatives, processors, and logistics providers and need better visibility, traceability, and planning.

You operate mixed fleets and critical field assets

Your business depends on machinery uptime and you want predictive insights across OEMs, telematics systems, and service partners.

You see pressure on margins and sustainability targets

You’re looking for data-driven ways to reduce input waste, improve yields, and meet sustainability or traceability commitments.

You need flexible delivery capacity

You want a partner who can own fixed-scope projects end-to-end or augment your teams with specialised data, ML, and platform engineers.

Example use cases

Where we’re helping agriculture leaders today

Crop production

Field-level yield forecasting and input optimization

Combine historical yields, soil data, satellite imagery, and weather to forecast yields and recommend optimal seed, fertilizer, and irrigation plans at field level.

Equipment & machinery

Predictive maintenance for farm machinery fleets

Ingest telematics and sensor data from mixed OEM fleets to predict failures, reduce downtime, and optimize maintenance scheduling across seasons.

Agri supply chain

End-to-end supply chain visibility and quality tracking

Unify siloed ERP, logistics, and quality systems to track lots from field to processor, reducing waste, claims, and compliance risk.

Agri-inputs & trading

Demand forecasting for inputs and commodities

Use external signals, market data, and historical sales to forecast demand by region and product, improving production planning and inventory turns.

Agri-inputs & cooperatives

Farmer engagement and advisory platforms

Build digital advisory tools that surface tailored agronomy recommendations, product suggestions, and risk alerts to growers at scale.

How we work with agriculture clients

From fragmented data to production-grade tools in the field

We combine sector experience with pragmatic engineering to deliver data and AI solutions that work in real-world agricultural operations.

Discover value and constraints

Map your value chain, data landscape, and seasonal constraints to identify high-impact, feasible use cases across production, logistics, and commercial teams.

Design data and model foundations

Define the data model, integration patterns, and ML approach—accounting for variable data quality, connectivity gaps, and regulatory requirements.

Build and integrate solutions

Deliver fixed-scope solutions or stand up dedicated squads to build pipelines, models, and applications that integrate with your existing systems and equipment.

Pilot in the field and harden for scale

Prove value with targeted pilots in representative regions or crops, then harden for reliability, monitoring, and security before wider rollout.

Embed adoption and continuous improvement

Equip agronomists, ops teams, and partners with training, workflows, and feedback loops so models and tools stay accurate as conditions and practices evolve.

Business Outcomes

Clear roadmap of data and AI initiatives tied to yield, cost, and risk outcomes
Robust data pipelines that can handle heterogeneous, low-connectivity environments
Production systems that operators and agronomists actually use
Reduced waste and downtime across equipment, inputs, and logistics
Faster cycle time from pilot concepts to scaled deployment

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