Artificial intelligence for manufacturers
SOPs, quality holds, and the maintenance note from the last time this line stopped — in front of the people on the shift.
Request a consult for your plant.
How manufacturers use generative AI
Most large plants have started a pilot. Few have the knowledge of the line available as something an operator can ask.
Quality and nonconformance
An operator asks what to do with a hold and gets the current procedure and the last similar record.
Maintenance troubleshooting
Work orders, manuals, and the technician's last note are searchable when the line is down.
SOPs on the shift
The controlled document is the one the assistant cites, so a retired supervisor is not the only person who knows the exception.
Shift and production reports
Handover notes and the daily report are drafted from what was already logged.
Customer and audit questions
Auditors and key accounts get answers from specs, certificates, and the quality manual, with the page attached.
Cost of the pilot
A plant can give every supervisor access and still cap what each person spends.
What manufacturing surveys show
Pilots are normal. A plant that has rolled AI out across sites is still unusual.
of manufacturers in Deloitte's study had started a generative-AI pilot. 24% had a use case in at least one facility; 10% across a wider network
Deloitte, Future of Manufacturing study, about 600 respondents
of manufacturing leaders reported that the business uses generative AI
Augury, State of Production Health, 2025
the share saying half or more of their AI projects had been rolled out across all sites, from 2024 to 2025
Augury, State of Production Health, 2025
of manufacturing COOs said AI was fully embedded. About two-thirds were still exploring or implementing in a targeted way
McKinsey, From pilots to performance: how COOs can scale AI in manufacturing
Figures come from the published surveys and case reports named on each card. They describe those samples, not a promise of the same result.
What Convex puts in front of the team
The same pattern as a large plant's knowledge project, without standing up a separate AI department for one factory.
Several models
Pick the model for the task from several leading models, in one place.
Specialized assistants
Assistants for quality, maintenance, and the controlled SOP set.
A personal assistant
Each person has an assistant tuned to how they work.
Workspace assistants
Shared assistants that keep the company's context for the whole team.
Search over your documents
Answers come from your own procedures, contracts, and records, with the source attached.
Documents out
Word files and images today. CSV export is planned.
A cap per person
Token use is visible per person, and the spend can be exported.
Adoption dashboard
See where AI is actually being used, and where it is not.
Cloud, or entirely on your own infrastructure
The same product. The difference is where the data sits.
Cloud
What is available today, hosted.
- Access to several leading models
- Specialized, personal, and workspace assistants
- Search over the company's documents
- Word documents and images
- Token-use tracking and reports
- Adoption dashboard
On-premise
Full control inside your environment, including a network with no outbound connection.
Everything in Cloud, plus:
- Your own model endpoints
- Offline licensing and payment for a disconnected site
- Assistants and workflows built for your process
Pick one line
Bring the SOPs and the last quarter of quality holds. That is enough to judge the answers.