Trades & Construction
AI employees for the work before, during, and after the job.
Contractors and trades already run estimating, project management, field, and accounting systems that mostly do their job. What is left is the work people carry between them: chasing information, moving field updates to the office, and getting completed work billed and paid. Code Quarry builds AI employees to do that work.
- General contractors
- Commercial specialty contractors
- Residential contractors
- Service trades
- Field-service businesses
The systems involved
One job usually moves through several of these before the money is collected. Not every business uses every one.
Plain boxes on purpose. These are yours, and they stay where they are.
The work between them
- Following up on bids, estimates, approvals, and outstanding documents.
- Reading plans, scopes, and contracts to find what actually matters.
- Carrying field updates, changes, photos, and completion details back to the office.
- Re-entering the same information into the next system so the job can move.
- Checking whether finished work is ready to bill, and then chasing the payment.
This work scales with volume, not with software. Every job you add means more of it, which is why the answer has always been another person in the office. The operations we help are the ones carrying several people doing this by hand, where that headcount is growing faster than margin.
Where the manual work lives
Before the work. During the work. After the work.
The manual work shows up in three places. In each one, a person is reading something, looking something up, moving information, or following up, and none of that requires their judgment.
One job, from first contact to payment
- Bid and estimate follow-up
- Gathering information
- Document review
- Field-to-office information
- Changes and approvals
- Exceptions
- Documentation
- Billing readiness
- Job-cost visibility
- Margin monitoring
- Payment follow-up
What AI employees take off your people
- Read bids, scopes, and project documents and pull out what matters
- Follow up on estimates, approvals, and open items without anyone remembering to
- Turn field notes, photos, and messages into structured job records
- Check whether completed work has what it needs to bill
- Watch job cost and margin, and flag the jobs moving the wrong way
Around your platform
Better information in. Better decisions out.
Your core platform is often already strong. The remaining work sits at its edges: improving the information going into it, and turning what's already inside it into answers someone can act on.
- Technician voice note
- Job photo
- Customer text
- Completed checklistincomplete
- 1Structure
- 2Validate
- 3Flag what is missing
- 4Update the record
- Complete job record
- Invoice-ready
- Margin alertfor a person
- Customer update
Better data starts before the form is submitted.
The job record often begins outside the core platform, in a technician's spoken notes, photos, texts, calls, and details remembered after leaving the site.
- Turn technician voice notes into structured job notes
- Check that required fields, photos, and documentation are there
- Prompt for what is missing before the job is closed
- Move information from calls, texts, and emails into the right record
- Accurate job costing
- Faster invoice readiness
- Less office cleanup
The data is there. The answer often is not.
Your platform can hold an enormous amount of information and an owner can still struggle to answer a practical question quickly.
Which jobs are consistently losing margin?
Where are estimates stalling?
Which invoices are delayed by an incomplete job record?
What should management pay attention to this week?
- Plain-language questions answered across your operational data
- Weekly management summaries, written for a person to read
- Job-margin and follow-up exception alerts
- Revenue-leakage detection
These are the kinds of solutions we can build where data access and platform permissions allow, around your platform rather than by replacing it.
Already using ServiceTitan or another platform?
Good. The platform does its job. AI employees handle the work around it.
They improve the information going into it, take the manual work happening around it off your people, connect it to the rest of the operation, and turn the data inside it into clearer action.
The same principle applies to Housecall Pro, Jobber, FieldEdge, BuildOps, Procore, and other systems where access and permissions allow.
What changes
A better business feels different.
The benefit is not more technology. It is what your people and your business can do when routine work stops consuming the day.
- 01
Save operational cost
The office stops re-entering information, chasing documents, and assembling the same updates by hand.
- 02
Give valuable people their time back
Estimators, project managers, and owners spend their time on customers, crews, and the calls that need them.
- 03
Catch revenue slipping through the cracks
Estimates, change orders, completed work, and invoices are less likely to stall between the field, the office, and accounting.
How we work
Three phases, from first look to a running AI employee.
- 1We prove what is worth doing together.
- 2We build it with you.
- 3We operate and improve it for you.
Nobody is asked to buy a proposal for work they have not seen quantified, and nobody is left running the AI on their own after launch.
- 1
Prove what is worth doing
Feasibility and business case
We come in and watch how the work actually happens. Together, we identify what an AI employee can realistically take over, where a person still needs to be involved, and what the opportunity is worth.
Step 1Watch the work
See how the process actually happens. The people closest to the work share their screens and walk through the real systems, handoffs, documents, decisions, exceptions, and workarounds.
You getA clear picture of where people are carrying work around and between the systems you already have.
Step 2Find the work
Identify the costly operational work that AI employees and automation can take over.
You getA short list of the work worth taking off your people, in your own vocabulary.
Step 3Do the math
Quantify the current state: who, times minutes, times frequency. Also delays, error risk, capacity limits, missed revenue, cash trapped, and what happens when nobody catches an exception.
You getThe cost of the work as it runs now, in numbers you can check against your own operation.
Before we recommend a build, we confirm the work is technically and operationally feasible, and we put a number on the cost, time, capacity, or revenue opportunity.
- 2
We build it with you
Collaborative build
Code Quarry owns the design and build, while working closely with your team to make sure the AI employees fit your operation. Your people help define the rules, exceptions, approvals, and human boundaries. We test the work together before launch.
Step 4Build the employee
Design what the AI employee will understand, decide, do, communicate, monitor, and escalate. Keep deterministic rules deterministic. Keep people responsible for judgment, approval, relationships, and high-risk exceptions. Then build it around the systems already in place.
You getWorking AI employees, integrated with your existing tools and tested against criteria you agreed to up front.
Code Quarry
- Design and build the AI employees
- Handle the technical work, integrations, and testing
- Fit the result to how the business actually operates
Your team
- Share how the work really happens
- Define the rules, exceptions, and approvals
- Decide where a person stays involved
- 3
We operate and improve it for you
Managed AI partnership
After launch, Code Quarry stays responsible for watching, validating, improving, and proving the performance of your AI employees. You stay focused on the business. We make sure the AI employees keep doing the job they were built to do.
Continuous monitoring, weekly validation, tested changes, monthly improvement, and a written report that proves the value.
Your first step is a conversation.
Let's talk about the work.
A practical conversation about where your people are doing manual work today, and whether AI employees can take it over.