AI agents for manufacturing companies

A manufacturer's website is a technical library with a sales desk attached. Engineers come for datasheets, tolerances and CAD files. Buyers come for certifications, capacity, minimums and lead times. Both tend to arrive with a specific question, and the answer usually exists somewhere on your site, in a PDF three menus deep. This page covers where AI agents help a manufacturer, and the guardrails that keep them from inventing specs.

Who visits a manufacturer's website

• Design engineers. Materials, tolerances, ratings, dimensions, compatibility, CAD models. They want a precise answer and the document behind it.
• Buyers and sourcing managers. Certifications, capacity, minimum order quantities, lead times, plant locations, how your RFQ process works, and the supplier documents they need for onboarding.
• Existing customers. Reorders, certificates of conformance, safety data sheets, order status.
• Distributors and reps asking about territories, and candidates looking at jobs on the shop floor and in engineering.

Which ASDK agents fit

All four agents answer from one knowledge base: your product pages, datasheets, capability pages and quality documents.

Sales: qualify RFQs before they reach an estimator (early access)

Sales gathers what your estimators need and filters out requests you do not take, using qualification rules you write in plain language: materials and processes you run, quantity ranges you want, industries and certifications you support. Good fits get a call booked with the right engineer or rep, and the lead goes to your team with a summary, by email or Slack, and to your CRM through webhooks and Zapier or Make.

Example flow

1. An engineer writes: "We need 500 brackets in 6061 aluminum, plus or minus 0.005 in, anodized, in six weeks."
2. Sales checks your capability page (you machine 6061 and list anodizing as a finishing option), then asks for annual volume, whether a drawing is ready, and the industry the part is for.
3. It explains that drawings go through your secure RFQ upload, after an NDA if needed, and that an estimator sends the quote.
4. The annual volume puts this in your target range, so Sales books a 20-minute call with the applications engineer and captures the contact.

Chat: answer from datasheets, with the source (early access)

Chat answers product and capability questions from your own documents and links the page or datasheet each answer came from. When a document does not cover the question, it says so rather than reasoning its way to a number.

Example flow

1. A visitor asks: "Is the 4410-B valve rated for 300 psi at 200 °F?"
2. Chat finds the pressure and temperature rating on the 4410-B datasheet and quotes it with a link.
3. If the datasheet lists 300 psi only at 100 °F and says nothing about 200 °F, Chat says the datasheet does not cover that combination and offers to connect the visitor with applications engineering.

Search: find the right part in a large catalog (coming soon)

Search is planned to replace your site search box with a direct answer and the best matching pages, so typing a part number, a competitor's part number or "M8 stainless shoulder bolt" returns something useful. Its planned "searched but not found" report shows which parts and terms people look for and miss, often a cross-reference or a product name you never published. See AI site search for how answer-style search works.

Agent-Ready: make capabilities readable to AI agents (early access)

Agent-Ready publishes an llms.txt file that leads AI systems to your capability pages, datasheets and quality certificates rather than to a brochure PDF. A sourcing agent can "ask a question" about a material or tolerance and get the same datasheet-backed answer Chat gives, or "contact sales" with the basics of an RFQ. On Pro and above, its visit analytics show which AI agents read which pages, so you can tell whether your datasheets are among them.

Guardrails for manufacturers

In 2024 a British Columbia tribunal held Air Canada responsible for wrong information its website chatbot gave a customer (Moffatt v. Air Canada). For a manufacturer, a wrong rating is worse than a wrong refund policy. Set these limits:

• Specs only from published documents. No interpolation, no "should be fine", no fitness-for-purpose judgments. Safety-related questions (pressure, load, temperature, chemical compatibility) get the datasheet value, a link, and an offer to confirm with engineering. The grounded answers guide covers how to keep answers tied to sources.
• Quotes come from estimators. The agent can share published price breaks and minimum order quantities. It never prices a custom part.
• No drawings in chat. Send files to your RFQ portal or secure upload, behind an NDA if you require one.
• Keep controlled technical data out of the knowledge base. If you make defense articles, US rules require registration with the State Department's Directorate of Defense Trade Controls, and "a manufacturer who does not engage in exporting must nevertheless register" (22 CFR 122.1). Drawings or specs that carry ITAR or export-control markings should never be among the agent's sources, and the agent should not discuss them.
• Certifications exactly as issued. State the standard, edition, scope and sites covered. ISO published ISO 9001:2026 on September 16, 2026, and certificates to the 2015 edition stay valid until September 30, 2029 (DNV), so buyers will ask which edition yours covers. Answer from your certificate page, not from memory.
• Lead times as ranges with a date. Capacity changes. Publish ranges and when they were last updated, and let the agent quote only those.

How AI agents research suppliers

A sourcing manager can now ask an assistant for a shortlist: "US contract manufacturers that machine titanium to plus or minus 0.0005 in, with AS9100, and lead times under eight weeks." Assistants such as ChatGPT, Gemini and Claude search the web and compare a few supplier sites. Agentic browsers can also fill in your RFQ form for the buyer. A supplier whose capabilities are written as numbers on plain pages is easier to include in that comparison than one whose site says "precision machining solutions" and hides the details in a brochure.

Publish these as text, not only as PDFs or images:

• Processes and materials, with tolerances, maximum part sizes and finishing options.
• Certifications and registrations, with standard, edition, scope, registrar and expiry.
• Minimum order quantities and price breaks where you can publish them.
• Lead time ranges for prototypes, first articles and production, with an "as of" date.
• Plants and shipping: where you make things and where you ship.
• Industries served and the requirements you meet for each.
• Your RFQ process: accepted file formats, NDA terms, response time.
• Key specs on HTML pages as well as in downloadable datasheets, so they can be quoted and linked.

For catalog products, Product structured data with part numbers lets an agent match the exact item; for job-shop capabilities, plain text with numbers matters more than markup. List your certifications and plants in llms.txt with the same wording as the certificates. The free agent readiness checker shows how much of your capability page an AI agent can read without running scripts, the agent-ready website checklist covers the rest, and Agent-Ready keeps the summary current.

Where to start

Start with Chat on your product and capability pages and Sales on your RFQ page, with the guardrails above in their instructions. Read the first week of conversations: every "the datasheet does not cover that" answer points to a document worth improving. Pricing shows which plan includes each agent.