Industry: Agriculture & AgriTech
AgriTech in production: IoT for crops, supply chain traceability and AI for yields.
- Crop & machinery monitoring (IoT, telemetry, MQTT)
- Farm-to-fork traceability — audit-ready in hours, not weeks
- AI: yield prediction & disease detection (TensorFlow, satellite data)
- Full code ownership. Your repository, your infrastructure.
iot-dashboard.log
[SENSOR] Pole-7 wilgotność gleby: 34% — próg nawadniania: 30%
[GPS] Kombajn #3 pozycja: 51.1°N, 17.0°E — pokrycie pola 67%
[AI] Predykcja plonu zaktualizowana: +12% vs. zeszły sezon (confidence 94%)
[TRACE] Partia #2024-0847 — pochodzenie zweryfikowane, kod QR wygenerowany
[ALERT] Ryzyko szkodników podwyższone w sektorze NW — inspekcja w 48h
[AUDIT] Kontrola zgodności łańcucha dostaw — 0 flag recall
No obligations. NDA on request.
< 200ms MTTR 100% Traceability ROI in 6 months
THE COST OF INACTION
When data lives in spreadsheets, decisions come too late.
- No telemetry = reactive decisions — you learn about drought from wilted crops
- Manual traceability = audit risk — a single recall costs more than a full system
- Excel yield planning = 15-20% error margin — losses invisible until harvest
- No fleet tracking = idle machines, wasted fuel, unmeasurable field time
EU food recall costs average €10M per incident. 80% are traceable to manual data gaps.
Supply chain without traceability — where errors hide
IS THIS FOR YOU?
When this is the right solution
- Makes sense when:
-
- You manage 50+ ha or multiple production sites
- You export to the EU and need compliance-ready traceability
- You have sensor data (or want to deploy sensors) and need a central platform
- Decisions today are based on experience, not live data
- You have a clear process owner (COO, Farm Manager, CTO)
- Doesn't make sense when:
-
- You're looking for an "app for everything" without a defined process
- Budget under 20,000 PLN — a pilot needs a minimum scope to prove value
- No internal champion — software without an owner becomes shelfware
- You need a ready SaaS, not a custom platform
WHAT WE DELIVER
Three implementation paths: CUSTOM DEV, NEURAL, LAUNCH
Precision Farming & IoT Monitoring
Dashboards for real-time crop monitoring. MQTT / AWS IoT integration. Fleet tracking, irrigation automation, traceability system farm-to-fork.
MTTR < 200ms
- Precision Farming & IoT Monitoring
- Precision Farming Platform: centralized IoT-driven system for real-time crop monitoring, automated irrigation, and supply chain traceability in agriculture.
AI for Agricultural Data
Yield prediction models (TensorFlow). Disease and pest detection from drone/satellite imagery. Soil analysis optimization and variable-rate application maps.
94% prediction accuracy
- AI for Agricultural Data
- Agricultural AI: machine learning models trained on field data to predict crop yields, detect plant diseases, and optimize input application rates.
AgriTech MVP in 4-6 weeks
Hypothesis validation before full investment. Working prototype with real field data. Pilot on one area, measurable results, go/no-go decision based on evidence.
4-6 weeks to pilot
- AgriTech MVP in 4-6 weeks
- AgriTech MVP Development: rapid prototyping of agricultural technology solutions with field validation to prove ROI before scaling.
CASE STUDIES
Case studies from implementations: from data to decisions
IoT Monitoring
Crop monitoring IoT: alerts instead of field inspections
- Problem
- Grain producer (800 ha) lost 12% of yield annually due to late drought and pest detection.
- Solution
- Deployed 120 soil sensors + weather stations with central IoT dashboard and real-time alert system.
- Result
-
- Yield loss reduced from 12% to 3%
- MTTR for irrigation issues: 4h → 20min
Traceability
Farm-to-fork traceability: audit in minutes
- Problem
- Dairy processor failed 2 EU compliance audits due to manual batch tracking in spreadsheets.
- Solution
- Built end-to-end traceability platform with QR codes, batch genealogy, and automated compliance reports.
- Result
-
- Audit preparation: 3 weeks → 2 hours
- 0 compliance failures since deployment
AI Analytics
Yield prediction AI: decisions based on data, not intuition
- Problem
- AgriTech startup needed to validate yield prediction model with real field data from 3 growing seasons.
- Solution
- Trained TensorFlow model on satellite + sensor data. Built prediction dashboard with variable-rate application maps.
- Result
-
- Prediction accuracy: 94% (vs. 68% manual)
- Input cost reduction: 22%
IMPLEMENTATION PROCESS
Implementation process: pilot → rollout → maintenance
Each step has a clear deliverable. You know what you're getting — before we write code.
1
Operational Discovery (1-2 weeks)
Process mapping, data audit, sensor readiness assessment. You get an architecture document and risk map.
2
Data Architecture (1 week)
Data pipeline design, IoT protocol selection (MQTT/AMQP), integration map. Schema for traceability and sensor streams.
3
Pilot (1 area) (2-6 weeks)
Working system on one field/production line. Real data, real alerts, measurable results. Go/no-go decision based on evidence.
4
Rollout (scaling) (6-12 weeks)
Expanding to remaining areas, integration with ERP/WMS, user training. Architecture tested under production load.
5
Maintenance (SLA) (Ongoing)
Monitoring, incident response, seasonal updates, data model optimization. Defined SLA with response time commitments.
Data Readiness Checklist
- IoT sensor data available via API or exportable
- Historical yield data (min. 2 seasons)
- Defined production process (field → storage → dispatch)
- Internet connectivity on site (Wi-Fi/LTE/LoRaWAN)
- Internal data owner / process champion assigned
- Budget approved for pilot scope (min. 4 weeks)
HARD PROOF
Quality proof: security, code ownership, auditability
Every project is delivered with a private repository, documented code review process, and full data audit trail.
traceability-audit.log
[BATCH] Partia #2024-0847 | pszenica ozima | pole: Zielonka-7
[TRACE] Zbiór: 2024-08-12 14:32 UTC | kombajn: JD-S780 #3
[STORE] Magazyn: Silos-B | temp: 14°C | wilg: 12.4% | waga: 24.7t
[QC] Kontrola jakości: mykotoksyny < 0.5 ppb ✓ | klasa A
[SHIP] Wysyłka: TIR PL-7823K | cel: Młyn Kraków | ETA: 6h
[VERIFY] Łańcuch zweryfikowany: 5/5 punktów kontrolnych | hash: a7f3…e21b
[RECALL-TEST] Symulacja recall: partia zlokalizowana w 4 min 22s
[AUDIT-PASS] Zgodność EU 178/2002 — PASS | raport wygenerowany
- Client repository with full commit history
- Code review on every merge — zero cowboy commits
- Data audit trail — who changed what, when, why
- Infrastructure-as-code (Terraform / Pulumi)
PACKAGES
Implementation packages for AgriTech
Discovery
Process mapping before code
1-2 weeks
- Process audit & data mapping
- Sensor readiness assessment
- Architecture document
- Integration map (ERP/WMS/IoT)
- Go / No-Go recommendation
EMPFOHLEN
Pilot
Proof on one area
2-6 weeks
- Everything in Discovery
- IoT platform for 1 field/line
- Real-time dashboard + alerts
- Basic traceability module
- Measurable KPI report
- Full code handoff
Rollout
Full production scale
6-12+ weeks
- Everything in Pilot
- Scaling to all areas
- ERP/WMS integration
- AI prediction models
- User training & onboarding
- Infrastructure-as-code
Maintenance
Ongoing operations & SLA
Monthly
- 24/7 monitoring + on-call
- Seasonal model updates
- Sensor health checks
- Data pipeline optimization
- Monthly performance reports
Final pricing depends on scope, sensor count, and integration complexity. Free estimate after Discovery call.
What is NOT in scope
- AI without historical data — models need training material
- Being a complete ERP — we integrate with yours, not replace it
- Hardware procurement — we specify, you purchase
- Agronomic consulting — we build tools, not farming advice
COMMON CONCERNS
Most common objections — our principles
A single recall incident costs €10M on average. One season of yield loss from missed drought data exceeds the cost of a pilot. We start with Discovery (2 weeks) to prove ROI before any larger investment.
Pilot runs 2-6 weeks. You see real sensor data on a dashboard in the first week. Measurable KPI comparison after the first production cycle. Decision based on numbers, not promises.
You own the code, infrastructure, and documentation from day one. We conduct knowledge transfer with your team. If you prefer, we offer Maintenance SLA with defined response times. Zero vendor lock-in.
We use open standards: MQTT, PostgreSQL, Python, React, Terraform. No proprietary protocols, no custom clouds. Your team (or any other vendor) can take over the codebase at any time.
That's exactly why we start with Discovery. We audit what you have, identify gaps, and design a data pipeline that improves quality from day one. Even 2 seasons of historical data is enough to train initial AI models.
Weekly demo with your team (you see working software, not slides). Dedicated Slack channel for daily questions. Monthly progress report with KPIs. You always know the status.
FAQ
FAQ: IoT, AI, traceability in agriculture
Any sensor that communicates via MQTT, HTTP, or LoRaWAN: soil moisture, temperature, humidity, wind, rain gauge, GPS trackers, weight sensors, camera traps. We integrate with existing hardware or help specify new deployments.
2-6 weeks depending on scope. A basic soil moisture + weather dashboard can be operational in 2 weeks. Full IoT platform with alerts, fleet tracking, and traceability takes 4-6 weeks for the pilot phase.
Yes. We design for EU Regulation 178/2002 compliance from the start: full batch genealogy, one-step-back/one-step-forward tracking, automated recall simulation, and audit-ready reports. The system generates compliance documentation automatically.
With 2+ seasons of historical data combined with satellite imagery and sensor data, our models achieve 90-95% accuracy for yield prediction and 85-92% for disease detection. Accuracy improves with each season as the model learns your specific conditions.
Edge devices buffer data locally and sync when connectivity returns. LoRaWAN sensors have range up to 15 km. The dashboard works on mobile with cached last-known-good data. Critical alerts use SMS fallback when internet is unavailable.
Yes. The architecture is multi-tenant from the start: each farm is an isolated data silo with shared analytics layer. Adding a new producer is configuration, not development. We've designed systems handling 500+ sites on a single platform.
GET STARTED
Start with a pilot. See data in a week, decisions in a month.
You'll get: process audit, architecture, pilot plan. NDA on request. Zero spam.
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