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Industry

AI for Manufacturing: Quality, Procurement, and Production Coordination

Plants run on paperwork between shifts, suppliers, and systems: quality records, purchase requests, supplier emails, shift reports, and the manuals nobody can find. DPI deploys AI that captures, reads, and routes that work, and gives operators and planners answers from your own documentation.

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Who This Is For

Built for plants with more paperwork than planners

  • Quality teams logging non-conformances, certificates, and inspection records by hand and assembling audit packets under deadline.
  • Purchasing teams chasing supplier confirmations, acknowledgments, and delivery dates by email and phone.
  • Operations leaders who cannot see inventory, WIP, and production status without exports and spreadsheets.
  • Maintenance and production staff who need answers from SOPs, equipment manuals, and past incidents on the floor.

Where AI Pays Off

Manufacturing workflows we automate

Quality documentation

Inspection records, certificates of conformance, and non-conformance reports captured from forms, photos, and emails into structured records with the right routing.

Supplier communication

Order acknowledgments, delivery confirmations, and expediting handled by email and voice agents, with exceptions escalated to buyers.

Procurement requests

Purchase requests read, checked against approved suppliers and budgets, routed for approval, and converted into orders in your ERP.

Inventory and production visibility

Status assembled from ERP, MES, and spreadsheets into daily views and answers to questions like which orders are at risk this week.

Shift and production reports

Reports captured from supervisors by phone or message, structured, and distributed with issues flagged.

Knowledge for the floor

SOPs, equipment manuals, safety procedures, and incident history searchable with citations, in the languages your crews speak.

Paperwork between the machines

Modern plants have automated the line and left the paperwork around it manual. Quality records are typed from forms, suppliers are chased by email, purchase requests wait for a buyer, and the answer to a maintenance question lives in a binder or in one technician's head. AI is good at exactly that layer: reading, classifying, routing, and answering from documentation.

What we deploy

Quality documentation captured from forms, photos, and emails; supplier communication handled by email and voice agents with exceptions to buyers; procurement requests checked and converted into orders; production and inventory visibility assembled from ERP, MES, and spreadsheets; shift reports captured and distributed; and knowledge assistants over SOPs, manuals, and incident history for operators and maintenance crews.

Fit with plant systems

We integrate with ERP, MES, quality, and document control systems through APIs or database access, and through structured exports where APIs do not exist. Models can run on servers inside your plant or private cloud, so drawings, specifications, and supplier terms never leave your network. Assistants work in the languages your crews speak.

Measuring the result

Quality record cycle time, supplier confirmation lead time, purchase request throughput, report completeness, time to find a procedure, and hours returned to quality, purchasing, and supervision, reported monthly.

How It Works

How a manufacturing deployment runs

  1. Shop-floor discovery

    We map how quality, purchasing, and production information moves between people, paper, and systems, and measure the volume.

  2. Integration design

    ERP, MES, quality, and document systems connected; approval and escalation rules defined with the teams that own them.

  3. Pilot on one line or one process

    Quality intake on one line, supplier follow-up for one commodity, or a knowledge system for one department, tuned on real cases.

  4. Plant-wide rollout

    Expansion across lines and departments with dashboards for cycle times, exceptions, and hours returned.

Stack & Integrations

Systems we connect to

ERP and MES systems Quality management and document control systems Supplier portals and email Your phone system over SIP WhatsApp Business API, SMS, Mailgun email OpenAI, Anthropic Claude, AWS Bedrock, self-hosted models PostgreSQL, pgvector, Qdrant

Engagement Models

Engagement models

Operations audit

Quality, procurement, and production workflows mapped, with the first deployment scoped and its metric defined.

Deployment

Fixed-scope delivery of the first workflow, integrated with your ERP and quality systems, with training for supervisors and buyers.

Managed operations

Ongoing monitoring, tuning, new workflows, and monthly reporting on cycle times and hours returned.

FAQ

Questions from manufacturers

Can the AI work with our ERP?

Yes. We integrate through APIs or database access with common ERP and MES systems, and through structured exports where APIs do not exist. Records are created and updated where your team already works.

How does it handle handwritten forms and photos from the floor?

Layout-aware and vision models read forms and photos; values with low confidence go to a person for confirmation. Corrections feed back into the pipeline.

Can it run on premises?

Yes. Open-source models can run on servers in your plant or private cloud, with data staying inside your network.

Does it support multiple languages for crews?

Yes. Knowledge assistants and voice or message interfaces can operate in the languages your crews speak while documentation stays in one source.

What about safety and compliance records?

Safety procedures, training records, and incident history are indexed with access controls, and audit packets can be assembled on request with gaps flagged.

How quickly can we see results?

Quality intake and supplier follow-up typically go live within weeks after the audit; visibility dashboards and knowledge systems follow as integrations are connected.

Related

Next Step

Bring one workflow. Leave with a production plan.

Tell us where calls, tickets, documents, or approvals pile up. We map the workflow, size the impact, and propose a deployment you can measure.

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