AI in agriculture and environment

Automatic analyses and summaries of measurement data

Analytics and reporting

Problem: Measurement and reporting data come from many sources and end up in spreadsheets

How it works: In-chat code execution for calculations on data; scheduled generation of periodic summaries

What you gain: The periodic summary is produced automatically from the same calculations — with a result you can check and repeat

Assistant for environmental applications and reports

Knowledge and documents

Problem: Filling in environmental applications and reports eats up specialists’ time

How it works: A central RAG knowledge base with documentation and structured answers that fill in the form according to a template — a document for a human to check and sign

What you gain: The application is filled in from source documentation, and the specialist checks and signs it — instead of retyping the same data yet again

Checking that regulations and procedures are up to date

Knowledge and documents

Problem: Reporting requirements change faster than procedures are updated

How it works: CogOps (CDF-F6) — a knowledge currency indicator and a quarterly review

What you gain: The unit knows which reporting procedures are outdated before it submits a report under old rules

Processing photos, maps and field notes

Knowledge and documents

Problem: Field documentation consists of photos, maps and voice notes

How it works: OCR that describes images; voice chat (STT/TTS) and AI Powered Notes

What you gain: Photos and notes from the field become searchable documentation the same day — no evening spent retyping

Cost-effective use of AI in a small team

Cost and efficiency

Problem: A small team and a limited budget for AI tools

How it works: Budgets with alerts; a semantic cache that cuts the cost of repeated questions; routing by model cost

What you gain: Repeated questions do not cost a second time, a cheaper model handles simple tasks, and the budget holds no surprises at the end of the month

Secure AI for beneficiaries’ and farms’ data

Security and sovereignty

Problem: Personal data of beneficiaries and farms must not reach an external model

How it works: PESEL, NIP and REGON detected and masked before sending; an “EU only” switch

What you gain: Staff use AI to work on applications, and beneficiaries’ data is masked before it leaves the unit

When does EUDR apply, and how do you document due diligence?

From 30 December 2026 for large and medium-sized companies, and from 30 June 2027 for micro and small companies. You need geolocation data and documents from suppliers — TWIN:DESK organizes them and answers with citations

What ESG data will customers demand?

Most often data on emissions, the origin of raw materials and EUDR compliance. It is worth collecting it in one place from which you can quickly prepare a response for the customer