What is this course about?
Slow quoting loses more work than high pricing does. This course builds an estimating workflow from your own completed job history, keeps material and labor pricing current, captures scope from a photo or voice note, and enforces a margin floor the agent cannot cross — with your approval always required before a quote leaves.
Who is this course for?
- Trades contractors and service operators
- Estimators in small firms
- Field service managers
- Owner-operators quoting their own work
What do I need before starting?
- Access to at least fifty completed jobs with final costs
- No technical background required
What will I be able to do afterwards?
- Quantify what your current quoting delay costs in lost work
- Build an estimating model from your own completed job history
- Keep material and labor pricing current without manual updating
- Capture scope from a photo, voice note, or site visit
- Enforce a margin floor the agent cannot cross
What does each module cover?
What does slow quoting cost you?
35 minMeasuring quote turnaround and the win-rate difference it produces.
Objectives
- Measure your real quote turnaround time
- Correlate turnaround with win rate
- Set a target turnaround
Topics
Activity. Analyze your last fifty quotes for turnaround time and win rate.
How do you build an estimate model from your own jobs?
50 minUsing completed job history — the data you already have and do not use.
Objectives
- Structure historical job data for estimation
- Identify the drivers that predict cost
- Handle jobs that went wrong without discarding them
Topics
Activity. Structure fifty completed jobs and identify your three strongest cost drivers.
How do you keep material pricing current?
40 minMaterial and labor rates that update without someone remembering to update them.
Objectives
- Build a pricing update process
- Handle supplier price volatility
- Flag quotes affected by a price change
Topics
Activity. Build the pricing refresh process and test it against a supplier increase.
How do you capture scope from a photo or voice note?
45 minTurning a field capture into a structured scope without a second site visit.
Objectives
- Extract scope from photos and voice notes
- Identify what a capture cannot determine
- Prompt for the missing information
Topics
Activity. Capture one job by photo and voice, and compare the extracted scope with reality.
How do you enforce a margin floor?
40 minThe rule an agent must never cross, and how to make it structural rather than advisory.
Objectives
- Set margin floors by job type
- Implement the floor as a hard constraint
- Handle the jobs that cannot meet it
Topics
Activity. Implement the floor and attempt to generate a below-floor quote.
How do you present a quote that wins against a cheaper bid?
40 minQuote presentation as the place where scope clarity beats price.
Objectives
- Present scope so the comparison is fair
- Make exclusions explicit without sounding defensive
- Give the buyer a reason beyond price
Topics
Activity. Rewrite one quote for clarity and test it with a past customer.
How do you follow up on unaccepted quotes?
35 minThe follow-up most businesses never do, and the work it recovers.
Objectives
- Design follow-up timing and content
- Learn why quotes were declined
- Recover work without discounting reflexively
Topics
Activity. Build the follow-up sequence and run it against last quarter's declined quotes.
Building the quoting agent on your job history
50 minThe lab module: an agent trained on fifty real jobs, validated against known outcomes.
Objectives
- Build the agent on your historical data
- Validate estimates against known final costs
- Establish the approval workflow
Topics
Activity. Validate the agent's estimates against ten jobs with known final costs.
What is the capstone project?
Quoting agent validated against known job outcomes
Build a quoting agent from your completed job history with current pricing, field scope capture, an enforced margin floor, clear presentation, and a follow-up sequence — validated against ten jobs whose final costs you know.
Deliverable: A working quoting agent with validation error figures and a measured turnaround improvement.
How are learners assessed?
- Estimates validated against real final costs with reported error
- Margin floor tested — a below-floor quote must be impossible to generate
- Turnaround measured against the Module 1 baseline
What ships with the course?
Facilitator guide
Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
Learner workbook
Exercises, checklists, and the templates each module's activity produces.
Hands-on lab environment
A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
Assessment bank
Scenario questions and rubric criteria mapped to each stated learning outcome.
Source bibliography
Every primary regulation and standard cited on this page, linked and dated.
Which AI agents does this course use?
The hands-on modules run against agents already deployable on the ibl.ai platform for small business.
Where does the course material come from?
Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.
- U.S. Small Business Administration
SBA
Pricing and margin guidance for service businesses.
- Internal Revenue Service
IRS
Job costing and recordkeeping requirements.
- Occupational Safety and Health Administration
OSHA
Safety requirements that must be scoped and costed into trade work.
- Business guidance
Federal Trade Commission
Disclosure obligations in quotes and estimates.
Delivery notes
Binding guidance for anyone preparing and delivering this course.
- Module 8's validation against known final costs is non-negotiable. An estimating model that has never been checked against actuals will be confidently wrong and cost the business real money.
- Module 5's margin floor must be a hard structural constraint, not a warning. Contractors under pressure will override a soft warning, which is exactly when the floor matters.
- Include jobs that lost money in Module 2. Excluding them produces an optimistic model, and the losses contain the most information about cost drivers.
- Scope capture in Module 4 should include its own failure cases. A photo cannot determine what is behind a wall, and the agent must ask rather than assume.
- Have an experienced estimator review the course. Estimating expertise is deep and a course written without it will miss the judgment that matters.
Why run AI training on a platform you own?
You own the course, not a licence to it
Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
Model-agnostic delivery
Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
No per-seat training licences
Usage-based or self-hosted, so cost tracks actual use rather than headcount.
Deploy anywhere
Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.
Frequently asked questions
What does the Quoting and Estimating with AI for Trades and Services course cover?
Slow quoting loses more work than high pricing does. This course builds an estimating workflow from your own completed job history, keeps material and labor pricing current, captures scope from a photo or voice note, and enforces a margin floor the agent cannot cross — with your approval always required before a quote leaves. It runs 4 hours across 8 modules across 8 modules, at foundational level, and closes with a capstone: Quoting agent validated against known job outcomes.
Who should take Quoting and Estimating with AI for Trades and Services?
It is written for Trades contractors and service operators, Estimators in small firms, Field service managers, Owner-operators quoting their own work. Prerequisites: Access to at least fifty completed jobs with final costs; No technical background required.
Can we run this course on our own infrastructure?
Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for small business teams that cannot send work to a public AI tool.
How do we get access to Quoting and Estimating with AI for Trades and Services?
Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.
How much does AI training for small business cost on ibl.ai?
There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.