INDUSTRY · MANUFACTURING

AI in manufacturing: your experts find information faster.

Manuals, failure history, sales references, contracts: we connect your ERP and your documents to AI, with trade secrets protected. Your technicians, sales reps and buyers decide.

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A 30-minute conversation with the team that will run your project, with no commitment.

  • Bridgestone: AI workshop and keynote for the teams
  • Trade secrets protected
AI IN MANUFACTURING
For
Shop floor, sales, purchasing
Who decides
Your experts
Updated
October 15, 2026

THEY TRUST US

  • Celio
  • Plein Sens
  • Shiseido
  • Green-Got
  • Promologis
  • Bridgestone

IN BRIEF

What are examples of AI in a mid-sized manufacturer?

Beyond predictive maintenance, which requires sensors and factory data, the uses quickest to put into service concern information: finding the right procedure in hundreds of manuals, preparing a tender response from your references, checking invoices and contracts, reporting on field sales visits.

They rely on your documents and your ERP, with one simple rule: every answer cites its source, and the technician, the sales rep or the buyer decides. France’s Direction générale des Entreprises has published a guide on this approach (RAG) applied to company documents.

FIVE EXAMPLES

Technical documentation · Tenders · Invoices and contracts · Sales visit reports · Rebuilt in-house tools.

  • Executive management
  • Manufacturing management
  • Sales
  • Purchasing
  • IT

Updated October 15, 2026.

WHAT YOUR TEAMS FACE

The information exists, no one can find it.

What manufacturing and sales leaders tell us.

  • PLANT

    “The right procedure is somewhere in the manuals, or in a veteran’s head.”

  • TENDERS

    “We turn some down for lack of time to study them.”

  • TOOLS

    “Our in-house tool doesn’t talk to anything else.”

WHO IT’S FOR

  • Executive management
  • Manufacturing management
  • Maintenance
  • Sales
  • Purchasing

EXAMPLES

AI prepares, your experts decide.

Uses connected to your documents and your ERP, with the source of every answer.

  • Failures analyzed before the repair

    AI cross-checks manuals, failure history and field feedback. The technician validates, then repairs.

  • More tenders reviewed

    AI finds your references and prepares a first response. Your sales reps price and sign.

  • Invoices and contracts checked

    AI matches orders and invoices, then flags risky clauses. The buyer or the lawyer decides.

  • Sales visits in the CRM

    The sales rep dictates on the way out; the report is filed, after review.

  • In-house tools rebuilt

    Your quoting or deal-tracking tool, rebuilt AI-ready.

  • Teams trained on their own cases

    In a hands-on session, everyone leaves with an agent that solves a real problem.

AI-READY

Your AI agents read and act in the tool, through MCP.

MCP is an open standard: a universal plug, under your control, between your software and AI. Your IT department approves every access.

  • SAMPLE REQUESTS TO YOUR AGENTS
  • “Which procedure for this fault code on line 3?”
  • “Find our references for this type of contract.”
  • “Which invoices don’t match the order?”

BEFORE / AFTER

What changes for your teams.

TODAYWITH AI, APPROVED BY YOUR TEAMS
ProceduresSearched for in binders.Found with one question, with the source.
TendersTurned down for lack of time.First response prepared from your references.
InvoicesSpot-checked.Matched against orders, discrepancies flagged.

Every gain is measured in your company, from your starting point.

OUR METHOD

Four steps, from the shop floor to headquarters.

The four steps of our method, in a loop. Each can be done on its own; together, they organize AI across the whole company.

  1. Inspire your leadership team

    A keynote built on cases from your business. Your leadership decides where AI will be useful, and where it will not.

  2. Map your operations

    We spot the bottlenecks, rank use cases by gain and difficulty, and name the tools to keep.

  3. Bring your teams on board

    In a hands-on session, everyone arrives with a real problem and leaves with the AI agent that solves it. An AI policy sets the shared rules.

  4. Build with the AI Factory

    Our engineers build AI-ready business tools with you. You keep the keys.

You take back the keys.

The code and data stay with you, documented. We keep improving the tool with you, or you take it over. This is step 4 of our method: Build with the AI Factory.

YOUR QUESTIONS

What manufacturers ask us.

What are concrete examples of AI in manufacturing?

An assistant for technical documentation, preparing tender responses, matching invoices and orders, field sales visit reports, and rebuilding in-house tools AI-ready.

Where should a mid-sized manufacturer start?

With a keynote for leadership and a map of operations: we choose a first useful use case, quick to put into service, then extend.

Do you need sensors and factory data to get started?

No. The first uses rely on your documents, your ERP and your tools. Predictive maintenance is a different, longer project.

Does the AI Act apply to industrial AI?

An internal document assistant is generally not a high-risk system. AI systems built into regulated products, such as machinery, follow specific rules.

How do we connect AI to our ERP?

Through connectors approved by your IT department, including MCP. If your in-house tool does not allow it, we can rebuild it AI-ready.

Do our drawings and confidential documents stay protected?

Yes: hosting chosen by you, access rights by team, and no model training on your data.

Does AI decide in place of your teams?

No. AI prepares: it sorts, checks, summarizes and writes a first draft. A named person reviews and decides. Every AI action is logged.

SOURCES

The texts and studies cited on this page.

Every figure on the site is sourced. See all our sources.

LET’S TALK ABOUT YOUR PROJECT

Let’s talk about your teams.

Thirty minutes to understand your priorities, and tell you where we would start.

Your contact

The AI Origin team that runs the projects

You talk directly with the people who will run your project, from scoping to go-live.

No specific project yet? Start with a hands-on session: everyone brings a real problem and leaves with the AI agent that solves it. See step 3, Bring your teams on board

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