AI for industry and manufacturing: GDPR-compliant and under control — for sales, QA and order processes | meinGPT
In many manufacturing companies, AI use is already happening outside central control — through private ChatGPT accounts, with sensitive design, quotation and order data. meinGPT brings that AI use under control: GDPR-compliantly in the EU, several leading models behind one interface, connected to ERP/PLM/QA — with real assistants for inside sales, quality assurance and order processes. As at world market leader LAUDA, where by its own account around 430 of 600 employees use meinGPT, over 370 of them daily.
For Managing directors, plant and operations management, IT and digital leads and departments in industrial mid-sized companies.
- Who it is for
- Managing directors, plant and operations management, IT and digital leads and departments in industrial mid-sized companies
- Impact
- Speed up recurring sales, QA and order processes — measurable in daily usage and handling time saved
- Task
- Introduce AI assistants for inside sales, quality assurance, order processes and technical knowledge management
For many manufacturing companies, AI in industry today begins with steering the AI use already happening on the shop floor — often uncontrolled — into orderly, GDPR-compliant channels, instead of employees putting sensitive design, quotation and order data into private ChatGPT accounts. On a platform operated in the EU, manufacturers bundle several leading models behind central permission management and build their own assistants from them without programming, for their recurring tasks: inside sales and quotes, quality assurance and test protocols, technical knowledge search across ERP, PLM and QA documents, and order processes. Because the decisive knowledge sits distributed across design, test and order documents and in the heads of experienced staff, AI assistants make it searchable and take on the preparatory routine while specialists review and own the result — which also relieves the pressure of the skills shortage. What matters is GDPR-compliant EU operation, protection of sensitive design and IP data, connection to existing systems, and a rollout that leads to daily use — not model access alone.
From the task to productive AI use
Manufacturers bundle several leading language models behind a GDPR-compliant interface and build their own assistants on top of it for recurring tasks, without programming. An assistant encapsulates context (product catalogue, test standards, quote templates, technical documentation as a knowledge base) and working steps, and is repeatable for the whole team. Through connectors (MCP) and an API, the AI can be linked to permitted internal systems — ERP, CRM, PLM as well as document and quality management — so answers come with sources from your own company knowledge rather than general internet information; access follows the permissions granted on a least-privilege basis and is logged. That is how an assistant for inside sales, an assistant for capturing test protocols in quality assurance or an automated order process from lead to quote comes about. LAUDA, world market leader for precise temperature control, has built exactly these three assistants with meinGPT. An accompanying academy and champion development per area make sure access turns into measurable daily use.
- Who it is for
- Managing directors, plant and operations management, IT and digital leads and departments in industrial mid-sized companies
- Impact
- Speed up recurring sales, QA and order processes — measurable in daily usage and handling time saved
- Task
- Introduce AI assistants for inside sales, quality assurance, order processes and technical knowledge management
What Manufacturing / industry gets done with AI
Concrete, repeatable flows — from the first prompt to a dependable result.
Bring AI under control — instead of shadow AI
Instead of private AI accounts, the whole workforce gets central, GDPR-compliant access to several leading models — with single sign-on, roles and permissions, and without input being used for training. Sensitive design, quotation and order knowledge therefore does not end up in uncontrolled private accounts but in a vetted environment whose access is logged and reviewed regularly. For most manufacturers this is the real starting point: first order the AI use that already exists, then build the specialist assistants on top. Details on operation are in the "GDPR-compliant AI" solution.
Speed up inside sales & quotes
A sales assistant helps inside sales find products and classifications faster in an extensive catalogue and produce quotes from product information more quickly. Instead of searching at length in ERP and CRM, the back office gets evidenced answers from its own product knowledge. LAUDA, world market leader for precise temperature control, has built such a sales assistant supporting inside sales in the back office — finding and classifying products and producing customer quotes faster. See the "AI in sales" solution for depth. Professional review of the quote stays with sales.
Quality assurance & test protocols
Quality assurance produces large volumes of test documents. An assistant captures, structures and prepares these protocols, extracts measured values and flags anomalies — the QA specialist assesses and approves. At LAUDA, a test-protocol assistant the company is particularly proud of takes on part of quality assurance for over 1,000 documents a year. Recurring documentation work is thereby accelerated without handing over professional responsibility; the final assessment and sign-off stay with quality assurance.
Make technical knowledge searchable (RAG)
Design, test and service documents, standards and manuals often sit distributed across ERP, PLM, DMS and file shares. Connected through connectors (MCP) and an API, an assistant answers questions with source references directly from these permitted sources — new and experienced staff find the right approach faster, particularly as experienced specialists retire. Access follows the permissions granted on a least-privilege basis and is logged, so sensitive design and IP data stays accessible only to authorised people.
Automate the order process
From an incoming lead through qualification to the quote, the order process can be largely automated: the assistant checks whether it is an existing or a new customer and produces a quote based on the product information. LAUDA is building exactly this order process — from an arriving lead that gets qualified through to a quote from the product data, largely automated. Binding approvals stay with sales. For chaining several steps across systems, see "AI workflow automation".
Take the load off the back office
Alongside the core processes, assistants take in the back office: preparing receipts and invoices, translations for international sites, meeting minutes with action items, and research with sources. These building blocks run in the same GDPR-compliant environment rather than sprawling into individual tools each with their own data-protection status. For commercial processes see "AI in accounting", for chained processes "AI workflow automation".
A real prompt, a real answer
Nothing hidden — you see the input and the result before you sign up.
Here is a test protocol for a temperature control unit (PDF). Extract in a structured way: the item under test and its serial number, the measured variables with target and actual values, the tolerance range and the status (OK / check) per measuring point, and any open anomalies. Give locations (section in the document), make no final quality assessment and flag only what QA should review.
| Test point | Target | Actual | Tolerance | Status | Location |
|---|---|---|---|---|---|
| Temperature stability | ±0.02 K | ±0.01 K | ±0.05 K | ✓ OK | Section 3.1 |
| Heat-up time | ≤ 12 min | 11.4 min | ≤ 12 min | ✓ OK | Section 3.2 |
| Cooling capacity | ≥ 1.5 kW | 1.42 kW | ≥ 1.5 kW | ⚠ check | Section 3.4 |
| Leak tightness | tight | tight | — | ✓ OK | Section 4.1 |
| Final assessment | — | — | — | ⚠ QA decides | — |
Put it to work in your own company
In a short live demo we show how this solution runs in your company with meinGPT, GDPR-compliant — using your own use cases.
Or download the Choosing the right AI platform — the requirements catalogue (PDF, German) as a PDF:
A work email is enough — processed in line with the GDPR.
Built for enterprise compliance
Manufacturers process sensitive design, test and order data as well as intellectual property, and the operation is designed accordingly. meinGPT is operated by SelectCode GmbH in the EU, a data processing agreement (DPA) is standard, and input is not used to train the models. SelectCode is ISO 27001 certified and has its security reviewed regularly through independent penetration tests (most recently SySS, 2025). Access to ERP, PLM and QA data follows strict permissions through central permission management with SSO, is limited by least-privilege scopes and is traceable through audit logs — so design and IP data stays accessible only to authorised people. Where particularly sensitive cases require it, European and open-source models can be chosen without changing governance or the data-protection level. The information security management system governs access control, logging, the handling of personal data and the secure deletion of information no longer needed; the corresponding policies and evidence are available through the Trust Center. Control over data, models and permissions therefore stays inside the company — instead of scattered across private AI accounts (shadow AI) — and can be evidenced to data protection officers, the works council and auditors.
- Data protection & hosting: Is the platform operated in the EU, is there a data processing agreement (DPA) and is it assured that input is not used to train the models?
- Certification & evidence: Is there independent evidence such as ISO 27001 certification and regular penetration tests?
- Protection of IP & design data: Can European and open-source models be chosen for particularly sensitive cases — without changing governance or the data-protection level?
- Depth of integration: Can ERP, CRM, PLM and document and quality management be connected through native connectors, MCP and an API, or does it stay an isolated chat?
- Roles, permissions & audit: Is there central permission management, SSO, least-privilege scopes and audit logs, so design and order data is accessible only to authorised people?
- Custom assistants: Can departments build their own assistants for sales, QA and order handling without development, and share them across the team?
- Enablement & adoption: Is there training, a champion programme and usage reporting — or does the service end at access?
- Cost & transparency: Are licence and usage costs traceable and predictable (tiered pricing by headcount, usage credits instead of a flat rate)?
What this solution cannot (yet) do
Honesty is part of the solution. These limits are known — and therefore plannable.
AI does not replace professional review — quotes, test protocols and calculations must be reviewed and approved by the responsible people (sales, QA, design) before use.
The value drawn from internal data depends on clean connections to ERP, PLM and QA and on maintained permissions — without reliable sources and scopes, answers stay generic.
Adoption does not come from access alone: without training and champions per area, experience shows only a few teams use AI regularly.
Models can be wrong or transfer values incorrectly; statements relevant to safety, quality or standards need source grounding and human control.
Governance decisions stay with the company: which design, IP and export-control-relevant data is released and which use cases are permitted must be decided by the company itself.
Frequently asked questions
In industrial mid-sized companies, AI mainly supports the recurring, knowledge-intensive tasks: inside sales and quoting, quality assurance and test protocols, technical knowledge search across ERP, PLM and QA documents, and order processes. The value arises because manufacturing companies hold a lot of knowledge distributed across design, test and order documents and in the heads of experienced staff. AI assistants make that knowledge searchable and take on the preparatory routine — specialists review and own the result. That saves handling time and helps against the skills shortage, because experience-based knowledge becomes accessible faster.