Services
Three practice areas, written for the operator, not the dashboard.
Below is what we actually do. Each area starts with a real conversation — the audit call — and ends with documentation a successor team can read three years from now.
Pillar 1
AI for Agriculture
The core practice. Premium crops, narrow margins, hard data. We design and ship the AI components that move a decision — soil-moisture readings that change the day's irrigation, computer vision that catches an early disease cluster, predictive models that move a harvest by 48 hours.
What we include
-
Soil & microclimate intelligence
Multi-source sensor networks, calibrated for the site, with anomaly detection that doesn't cry wolf.
-
Computer vision for crop health
Smartphone-grade or drone-grade image pipelines that flag disease, pest, and stress patterns before scouting catches them.
-
Predictive harvest models
Phenology-aware ML trained on the actual plot, not a generic regional curve. Improves with each season.
-
Yield and revenue forecasting
Probabilistic models that tell you the spread, not a point estimate the bank will hate.
-
Acoustic and behavioural monitoring
For apiaries, livestock, and high-value protected sites. Real-time alerting on conditions that matter.
-
Regulatory and quality traceability
AI-assisted document workflows for DOP, IGP, organic certifications, and bilateral export protocols.
Applied examples
- A boutique vineyard estate moving from gut-feel pruning to a calibrated model trained on three seasons of micro-station data.
- A truffière association building a shared anomaly-detection layer across member sites without exposing each producer's yield curve.
- A premium honey cooperative replacing manual hive checks with acoustic sensors, saving 60 percent of inspection time during foraging peak.
Pillar 2
AI for Food-Service & Filiera
The second pillar covers everything that happens after the field: transformation, traceability, hospitality, food R&D. We work with processors, distributors, hospitality groups, and food-tech startups that have outgrown spreadsheets but can't afford a dedicated ML team.
What we include
-
Supply-chain traceability
From plot to plate, with auditable AI-generated documentation and exception alerts on stalls in the chain.
-
Quality control automation
Computer vision for visual grading; sensor fusion for aroma, colour, and texture profiles.
-
Demand forecasting for hospitality
Per-cover prediction that survives Saturday-night reality, not just the spreadsheet average.
-
Menu engineering with constraint solvers
Profitability, seasonality, and sourcing constraints solved together rather than in isolation.
-
Food R&D acceleration
AI-supported formulation and sensory profile search for new products and ingredient substitutions.
Applied examples
- A regional olive oil bottler standardising provenance documentation across eleven mills using a shared OCR + classification pipeline.
- A fine-dining group consolidating bookings, no-show patterns, and ingredient ordering into a single weekly decision dashboard.
Pillar 3
AI Process Automation
Cross-sector practice for operations work that doesn't fit Pillar 1 or 2 but where the same methodology applies. Mostly document-heavy or multilingual workflows that exhaust a small operations team.
What we include
-
Bilingual / trilingual document workflows
EN / IT / ES / FR pipelines that respect legal and commercial nuance, not raw machine translation.
-
Inbound triage and routing
AI-assisted classification of incoming inquiries, contracts, and tickets, with conservative confidence thresholds.
-
Knowledge base and retrieval systems
Internal search that works when nobody can remember which folder the right contract is in.