AI TRAINING
AI-Powered Quoting Assistant for Job Shops
Leave with a working AI tool that reads customer PDFs and drafts quotes from your past jobs.
What it covers
A one-day hands-on workshop where estimators and sales staff at small manufacturers build a functional quoting assistant using AI. Participants connect a language model to their historical job data, configure PDF spec extraction, and generate structured quote drafts automatically. By end of day, each team leaves with a working prototype they can use immediately — no coding background required. The session balances brief concept intros (30%) with guided build time (70%).
What you'll be able to do
- Configure an AI tool to extract key specs (dimensions, materials, tolerances) from a customer PDF quote request
- Build a prompt template that references historical job costs to produce a structured first-draft quote
- Apply a review checklist to catch AI errors before sending a quote to a customer
- Export or integrate the quoting assistant output into an existing spreadsheet or ERP workflow
- Identify the job types where AI-assisted quoting saves time versus where human judgement must take over
Topics covered
- Extracting specs and dimensions from customer PDF drawings using AI
- Structuring and querying historical job cost data as an AI reference
- Prompting a language model to generate a first-draft quote
- Reviewing, editing, and approving AI-generated quotes safely
- Handling edge cases: missing specs, unusual materials, first-time jobs
- Basic data hygiene for past-job records used as AI input
- Connecting the tool to a shared folder or simple spreadsheet workflow
Delivery
Delivered in-person at the client's site or a training facility; remote delivery is possible with participants sharing screens. Participants work in pairs on real or anonymised company data. Facilitator provides a pre-built starter template and a no-code AI environment (e.g. a configured GPT or a Make/Zapier workflow). Materials include a PDF extraction guide, prompt library, and a post-workshop checklist. Hands-on build time represents approximately 70% of the day.
What makes it work
- Starting with a narrow, well-defined job category (e.g. laser-cut steel brackets) before expanding scope
- Assigning one internal owner who maintains the historical job dataset and prompt templates
- Pairing the AI draft with a mandatory estimator sign-off before any quote leaves the building
- Running a 4-week pilot on real incoming RFQs and tracking time-to-quote and error rate
Common mistakes
- Feeding the AI poorly formatted or inconsistent historical job data, producing unreliable quote drafts
- Sending AI-generated quotes to customers without a human review step, causing pricing errors
- Trying to automate every job type on day one instead of starting with the 20% of repeat jobs that represent 80% of volume
- Neglecting to update the historical job dataset regularly, causing the AI to quote based on outdated material costs
When NOT to take this
A job shop where every order is fully custom with no historical comparable jobs — the AI has nothing to learn from and will produce unreliable drafts that take longer to correct than to write from scratch.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.