Industry offer · Electronics and automation
SAIE for electronics and automation is an AI environment that reads schematics and technical documentation, connects engineering systems and does not let code or documentation leave the company. AI that reads schematics, connects engineering systems and keeps code inside
Technical documentation consists of drawings and schematics that cannot be searched as text; service knowledge sits in the heads of a few people; data between engineering systems is moved by hand. External AI tools tempt engineers — and take code and documentation outside the company. From 11 September 2026, manufacturers of products with digital elements must report actively exploited vulnerabilities under the Cyber Resilience Act (CRA), and from 11 December 2027 the CRA applies in full. The Machinery Regulation 2023/1230 applies from 20 January 2027, and AI in products listed in Annex I to the AI Act, including machinery, will be subject to obligations from 2 August 2028
Technical director — faster access to documentation and service knowledge
Head of IT / OT — protection of code and design data
Quality manager — repeatable test reports
Knowledge and documents
TWIN:DESKProblem: Technical documentation contains schematics and drawings that cannot be searched as text
How it works: Advanced OCR describes drawings, charts and schematics; metadata filtering narrows the result to the right documentation version
What you gain: Engineers find the right schematic with a question, not by browsing folders — also in documentation from years ago
Adoption and skills
TWIN:DESK + CDFProblem: Service knowledge exists only in the heads of a few experienced people
How it works: The knowledge audit (CDF-F0.K) turns conversations with experts into a knowledge base, and the Knowledge Gap Register flags areas without a documented procedure
What you gain: Service technicians’ experience becomes a company asset, and the list of “what we have not written down” is visible to management
Process automation
TWIN:DESKProblem: Data is moved between engineering systems by hand
How it works: Triggering n8n and Langflow flows, integration with GitHub and Jira, and if/then rules in natural language
What you gain: Data flows between systems according to rules described in plain language — no developer needed for every small integration
Integration and reliability
CDFProblem: Nobody knows which of the older control systems and databases are ready for AI
How it works: CDF-F0.L: assessment of older systems for AI — a priority list: deploy / rebuild / postpone / not recommended
What you gain: The modernisation decision is based on an assessment, not intuition — the budget goes where AI adds value
Analytics and reporting
TWIN:DESKProblem: Test and measurement reports follow a repetitive template and draw on several sources
How it works: Structured answers that follow the report template, and in-chat code execution for calculations on measurement data
What you gain: The test report always has the same structure, and the calculations are reproducible — fewer corrections at acceptance
Security and sovereignty
PROXY:AIProblem: Design documentation and code reach external AI tools without control
How it works: Data classification and routing to local models; blocking the pasting of secrets, keys and passwords; detection of data exfiltration attempts
What you gain: Engineers use AI for code and documentation, and the company has a technical guarantee that its intellectual property stays inside
Integrations: GitHub, Jira, n8n, Langflow, VSCode/Cursor
Offline operation and isolation from the public network
A Proof of Value on the documentation of one product line — OCR of schematics and a service assistant in your infrastructure
Manufacturers, importers and distributors of products with digital elements placed on the EU market, including controllers and devices with software
Yes. Local models in SAIE are used within the company’s infrastructure, so code and documentation do not go to external services
With a list of products and components, a reporting procedure and access to documentation in one place. TWIN:DESK answers with citations from technical documentation, which shortens the time needed to establish which products a vulnerability affects
Machine know-how stays in the company, reports compile themselves, and every pilot ends with a decision
AI for a small team: reports, field data and regulations under control, within budget
https://www.allclouds.pl/en/oferta-branzowa/elektronika-i-automatyka/