AI in manufacturing for the quotes, orders and quality paperwork your office team keys in by hand

We build AI agents that turn RFQs into draft quotes, enter customer purchase orders into your ERP, and prepare supplier, nonconformance and maintenance records in the systems your plant already runs. Your estimators, buyers and quality engineers approve every price, commitment and disposition. The agents run on self-hosted n8n in your cloud and write to your ERP only through its own interfaces.

Book a Discovery CallStart with an AI Audit

Jason, our COO, runs the call and scopes the first workflow with you.

Agent run: RFQ to draft quoteSample
  1. RFQ receivedBuyer email with a drawing PDF, three part numbers, 500 and 2,000 pieces
    Done
  2. Drawing readMaterial, finish, tolerances and quantities pulled into the quote sheet
    Done
  3. Past jobs matchedTwo similar parts quoted before, with their routings and actual hours
    Done
  4. Costs pulledMaterial price from the supplier list, machine and labor rates from the ERP
    Done
  5. Estimator reviewDraft quote with every assumption marked
    Waiting
  6. Sent and logged in the ERPQuote PDF to the buyer, opportunity and run log updated
    After approval

What the agent can seeOnly the records this quote needs. Everything else stays in your systems.

Customer drawing and RFQ
Visible
Item master and routings
Visible
Past quotes for this customer
Visible
Machine and labor rates
Visible
Other customers' prices
Not retrieved
Payroll and HR records
Not retrieved

Illustrative run. Sources, rules and margins are set per workflow during scoping. The agent never sends a price without an estimator's approval.

Clients include

AudinatePinkcubeBizIQCourserAllClear Travel Insurance

AI use cases in manufacturing, from RFQ to maintenance log

Start with paperwork that arrives every day, follows rules you can write down, and already gets checked by someone before it counts. These six workflows meet all three, and none of them touches a machine.

Quotes

RFQ intake and quote drafting

The agent reads the RFQ and drawing, finds similar parts you have quoted before, pulls material and machine rates from your ERP and drafts the quote with its assumptions marked. Your estimator sets the price and sends it.

Test it on last year's RFQs first
Orders

Sales order entry from customer POs

Purchase orders arrive as PDFs, spreadsheets and emails. The agent reads each one, checks part numbers, prices and dates against the quote and your item master, and keys the sales order. Any mismatch goes to customer service with the line marked.

How agents read and write to your ERP
Suppliers

Supplier documents and invoices

Certificates of conformance, material certs and acknowledgements are checked against the PO spec, and anything missing or expired is chased. Supplier invoices are matched to the purchase order and goods receipt before posting.

See AI invoice automation
Quality

Nonconformance and corrective action reports

From the inspector's notes, the agent opens the nonconformance report, fills in part, lot, supplier and quantity, and lists earlier reports on the same part. Your quality engineer owns the root cause and the disposition.

How approval gates work
Maintenance

Maintenance logs and work orders

Technicians write what they found in their own words. The agent tags each entry by asset and fault, drafts the work order, and sends your maintenance lead a weekly list of repeat faults by machine.

See the context layer it reads from
Procedures

Work instruction and SOP answers

Supervisors and new hires ask a question and get the answer from your current work instructions with the document and revision cited, so they stop waiting on the one person who knows the binder.

See the enterprise AI chatbot

The labor gap behind every quote, order and report

1.9M

US manufacturing jobs that could go unfilled through 2033 if workforce challenges are not addressed

Source: The Manufacturing Institute and Deloitte, "Taking charge: Manufacturers support growth with active workforce strategies" (April 2024).

Agents do not run machines or replace the people who do. They take the typing, matching and chasing off the estimators, buyers and quality engineers you already have.

As many as 3.8 million new employees needed by 2033

That is the net need The Manufacturing Institute and Deloitte project for US manufacturing between 2024 and 2033. Every hour an agent takes off order entry is an hour your team keeps for the customer.

65% name attracting and retaining talent as their top challenge

From the National Association of Manufacturers' Q1 2024 outlook survey. An agent that drafts the quote lets the estimator you have handle more of them.

Start with the work someone already checks

An agent that prepares an order, a supplier check or a nonconformance report gets reviewed by the person who already reviews that work. Prices, releases and dispositions stay with your people.

Rank the workflows before you build one

The AI Audit scores your candidate workflows by value, effort and risk, so the first build goes where your hours are. The automation ROI calculator gives you a first estimate today.

Sources: The Manufacturing Institute and Deloitte, "Taking charge: Manufacturers support growth with active workforce strategies", press release of 3 April 2024; National Association of Manufacturers, Manufacturers' Outlook Survey, Q1 2024, as cited in that release.

How the agents handle your drawings, prices and ERP data

Customer drawings and your pricing are the data you can least afford to leak. Your IT team will ask what the agent saw, what it changed and who approved it, so we build those answers into the system before it goes live.

Your environment
  1. SourcesERP, quality system, shared drives, inboxRead through the interfaces they already expose
  2. Context layerScopes each taskOnly the records this job needs, other customers' data walled off
  3. AgentSelf-hosted n8nRuns the workflow and its rules in your cloud
  4. ApprovalNamed sign-offEstimator, buyer or quality engineer, before write-back

run 8812 · RFQ to draft quote · 3 sources read · drawing parsed · 2 past jobs matched · approved by estimator · written to ERP

Outside the boundary

A model provider receives scoped fields only, under your own agreement with that provider, or is replaced by a model you host.

Never allowed
  • A price, PO or disposition sent without sign-off
  • Customer drawings used to train models
  • Commands to machine controls
Approval gates

A named person on every commitment

Quotes, purchase orders and nonconformance dispositions wait for a named approver before anything is written back or sent. Each workflow has an owner and an escalation path before it goes live.

See enterprise AI governance
Second agent

A second agent checks every draft quote

Before a draft reaches your estimator or a customer, a second agent checks it against your rules and sends it back with the problem named.

What works for agents in production
Your cloud

Run and logged in your own cloud

We deploy on self-hosted n8n in your cloud or data center, behind your own sign-in, with permissions inherited from the systems the agent reads. Every run is logged with the records it read, what it changed and who approved it.

See self-hosted n8n for enterprise

From the first call to an agent working your order desk

The same team carries your workflow from the first call to the system running in production, and every build is tested in staging before it goes live. See how we work

  1. Jason
    Jason, COO

    AI Audit, if you need one

    Eight phases across strategy, operations, data and systems over 4 to 8 weeks, ending in a ranked 90-day roadmap. The fee is credited toward your first build.

  2. Jason
    Jason, COO

    Scope

    One workflow, the systems it touches and what it will take, agreed before you commit. Owen joins the first calls until you are onboarded.

    • The documents and ERP fields each agent may read
    • The prices and releases that always go to a person
    • The timeline, before you sign
  3. Maciej
    Maciej, CTO

    Build

    Built against your real data, in your environment, as production software from the first week.

    • Staging first, on last year's RFQs and orders
    • Your estimators and buyers use it on live work before go-live
    • Self-hosted n8n in your cloud
  4. Jason
    Jason, COO

    Integrate

    Into production behind your sign-in, your governance and your monitoring, with your team trained on it.

    • Separate development, pre-production and production environments
    • Write-back to the ERP only through its own interfaces
    • Your people trained on the system
  5. Maciej
    Maciej, CTO

    Run and improve

    Watched, fixed and extended in weekly sprints by the engineers who built it.

    • Error alerts to the engineers who built it
    • Weekly sprints that you direct
    • The next workflow, scoped with you

What your plant office gets from AI solutions for manufacturing

We build the agent around how your shop quotes, orders and inspects, and run it in your environment.

What you get
You receiveWorking agents inside your ERP, quality system and inbox
Where drawings and prices liveIn your cloud or data center, on self-hosted infrastructure
Fit to your rulesBuilt around your quoting, ordering and inspection procedures
CommitmentsMade by your people, approved and logged
TimelineScoped per workflow and agreed before you sign
After launchThe engineers who built it monitor and extend it

What manufacturers ask before building AI agents

Ask us something else

How is AI used in manufacturing?

Manufacturers use AI in two places. On the plant floor, equipment and software vendors sell machine monitoring, predictive maintenance and vision inspection. In the office around the plant, AI agents read RFQs, customer purchase orders, supplier certificates, inspection notes and maintenance logs, prepare the next step in your ERP or quality system, and hand it to the person who signs it off. We build the second kind. For more examples by department, read 25 enterprise AI use cases and what each takes to ship.

What is agentic AI in manufacturing?

An agent takes several steps toward a goal and chooses which system to read next, where a chatbot answers one prompt. In a plant office that means an agent that opens the RFQ, reads the drawing, looks up past jobs and rates, and drafts the quote, then stops at the estimator's approval. The steps it may take, the records it may read and the point where a person takes over are written down before we build it. Read what works for agents inside real systems.

Can the agents work with our ERP?

We connect through what your ERP exposes: its API, a database view with read access, scheduled exports, or the documents that arrive by email. For SAP, read how n8n works alongside SAP Joule Studio. For an older ERP, scheduled exports loaded into a staging database often work best, so the database computes every figure before the agent reads it. The AI Audit confirms what each of your systems allows before anything is quoted.

Do you build predictive maintenance or vision inspection models?

Not today. Where we help maintenance and quality teams is the paperwork around the asset and the part: technicians' logs tagged by asset and fault, work orders drafted, nonconformance reports opened from inspection notes, and a weekly list of repeat faults for your maintenance lead.

Will an agent send a quote or a purchase order on its own?

No. In the systems we build, the agent drafts the quote, the sales order or the supplier PO with its sources attached, and a named person approves it before anything leaves your building or changes in the ERP. Fully automated steps are limited to work with written rules, such as asking a supplier for a missing certificate.

How long does it take to implement AI in a manufacturing business?

We scope each workflow and agree the timeline with you before you sign. A workflow that spans several systems, or needs a context layer built first, takes longer than one on a single system with a clean interface. If you want proof first, an AI pilot runs 2 to 6 weeks on your own RFQs or orders and costs $35,000 to $75,000, with the fee credited toward the build.

Where should a manufacturer start with AI?

With the office queue that has the most volume and the clearest rules, usually RFQ intake or order entry. If you already know which one, book a discovery call and we will scope it. If you have several candidates across plants, the AI Audit ranks them by value, effort and risk over 4 to 8 weeks and ends in a 90-day AI roadmap. It costs $10,000 to $100,000 depending on scope, and the fee is credited toward your first build. For a second opinion on a plan you already have, see AI consulting.

Who keeps the agents working once they are live?

The engineers who built them, in weekly sprints that you direct, with error alerts going straight to them. See AI managed services. If your own team wants to run and extend the workflows, corporate AI training covers your managers and n8n training covers your builders.

AI agents in industries close to manufacturing

The same document, order and reporting work shows up for your contractors, your retail customers, your owners and your accountants.

Construction

AI for construction

Subcontracts, submittals and pay applications checked before your team signs them.

See AI for construction
Private equity

AI in private equity

Deal screening, data room review and portfolio KPI packs, for the firms that own manufacturers.

See AI in private equity
Retail

AI for retail

Product data, stock exceptions and returns for the retailers and online stores that sell what you make.

See AI for retail
Accounting

AI in accounting

Bank reconciliations, close files and tax workpapers, for the firm or finance team that keeps your books.

See AI in accounting

Size the first build before you call us

Scope

AI use-case canvas

One page to scope your quoting or order entry process before anyone builds it.

Get the use-case canvas
Assess

AI readiness scorecard

Ten questions that score your data, systems, workflows, people and governance.

Take the readiness scorecard
Read

Why most AI pilots stall

What separates a proof of concept that reaches production from one that stays a demo.

Read the article

Put your first workflow on the board

Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.

Owen, Maciej, Jason, Oskar and Ben