AI Automation for Manufacturing Operations
AI-enabled workflow automation for manufacturing and operations teams: AI document processing, data extraction from documents and spreadsheets, component data enrichment, an LLM or other AI capability integrated into your existing software, and a retrieval (RAG) workflow that answers questions from your own documents, all built to move from pilot to production.
acty.dev’s AI automation hub for manufacturing operations. We build AI workflow automation, AI document processing, and data enrichment pipelines that move from pilot to production.
The problem we solve
Manufacturing and operations teams have documents, spreadsheets, and component data that still get processed by hand: datasheets to search, records to enrich, and reports to compile, with no automated workflow connecting the data to the systems that need it. Other teams already have a working application and want an AI capability wired into it, or want their own manuals, specifications, and quality documents to return reviewable answers instead of being searched by hand.
How we work
- Discovery.We review your documents, data sources, and current manual process, and define a scoped automation before any build starts.
- Build.We build the document processing, data extraction, or enrichment workflow against the agreed scope, with human review of AI-assisted output.
- Deploy.We deploy to production with environment configuration and a repeatable release process.
- Support.We hand over documentation and a monitoring checklist, and offer ongoing support or a development retainer.
What you bring / what you get
Inputs
- Your documents, spreadsheets, or data sources
- Access to the systems the workflow needs to connect to
- The manual process or bottleneck you need automated
Outputs
- An AI-enabled document processing or data extraction workflow
- A component or spreadsheet data enrichment pipeline
- A deployed workflow with documentation and a monitoring checklist
- An LLM or other AI capability integrated into your existing application or internal tool, delivered with test cases, documentation, and deployment notes
- A retrieval (RAG) workflow that turns your own manuals, specifications, or quality documents into reviewable answers, deployed with documentation
Definition of done.A working, deployed AI-enabled workflow that processes your documents or data automatically, delivered with a Git repository, documentation, environment configuration, a monitoring checklist, and a next-phase roadmap.
Fit and anti-fit
Good fit
- Manufacturing and operations teams with a real document, spreadsheet, or data process
- Teams with an existing workflow, dataset, or operational bottleneck
- Buyers who need implementation and production delivery, not just advice
Not a fit
- Micro-budgets with no real scope
- Idea-only requests with no real process, data, or system
- Solo hobby projects
Questions
- What does AI automation for manufacturing include here?
- AI document processing, data extraction from documents and spreadsheets, component data enrichment, and workflow automation built to move from pilot to production for manufacturing and operations teams.
- Can you connect this to our existing systems?
- Yes, when the integration is scoped with you. We build the workflow against the systems and data sources you give us access to during discovery.
- Can you integrate an LLM or another AI capability into software we already use?
- Yes. We wire a scoped AI capability into your existing application, internal tool, or workflow rather than building a separate product, and deliver it as a UI or API flow with test cases, documentation, and deployment notes. It is a defined capability, not a universal AI agent. We test it against agreed cases during the build and leave you a checklist for reviewing uncertain output.
- Can we ask questions and get answers from our own documents (RAG)?
- Yes. We build a retrieval workflow over your manuals, specifications, supplier records, or other internal documents, with rules for how sources are ingested and a repeatable way to refresh them as documents change. Answers stay grounded in your own sources rather than generic output, and answers the system is unsure of can be routed to a person for review. We never guarantee perfect retrieval.
- What is out of scope?
- We stay within AI-enabled workflow automation for manufacturing and operations. We do not make unproven performance, client, or capacity claims.
- What do you need from us to start?
- Your documents, spreadsheets, or data sources, access to the systems the workflow needs to connect to, and the manual process or bottleneck you need automated.
- How is an engagement scoped and priced?
- Engagements start with a task review; scope and price are defined after that review. Ongoing support and development retainers are scoped after the first review.
Explore
Have a process, dataset, or camera footage to work from?