AI Agents for Home Service and Trade Contractors

What should a contractor automate first with an AI agent?

Lead response, because it is the only part of the operation where a delay directly loses revenue you already paid to generate. A homeowner who fills in a form at 8:40 in the morning is also filling in two other forms. Whoever answers first gets the appointment, and in most contracting businesses the person who would answer is driving to a job site.

An agent does not need to be clever to fix that. It needs to be awake. It reads the inbound lead, checks whether the address is inside the service area, drafts a reply that references what the homeowner actually asked about, and either sends it under a rule you set or holds it for the one approval a person gives from a phone in thirty seconds. The same agent then watches whether a reply came back, and raises the ones that went quiet instead of letting them age out of the pipeline.

After lead response, the next two in order are job documentation and estimate follow-up. Both are high volume, both are currently done by whoever has a free evening, and both are checkable, which is what makes them safe to hand over.

Why not automate the field work or the pricing?

Because the failure modes are expensive and the inputs are physical. A price that is wrong by fifteen percent is not a correction, it is a job you either lose or lose money on, and the number depends on site conditions a camera roll does not fully capture.

The same reasoning that applies to any agent deployment applies hardest here. An agent should prepare the estimate, pull the measurements it has, flag what is missing and format the document. A person prices it. That split keeps the speed and leaves the judgment where it belongs. The broader rule, and where the gates go in every deployment we ship, is set out in the admin access answer page.

What a contracting deployment actually includes

The deployment is an agent running in your own cloud account, connected to the systems you already use, with named skills for the jobs above. Deployment starts at $10,000 for a single specialised agent with up to five connected systems and a seven business day deployment target, and $25,000 for the full tier with up to four agents, orchestration between them and a fourteen business day target. An optional implementation retainer of $2,500 to $3,000 per month covers ten hours of senior engineer time for new and changed skills. The full table is on the pricing page.

Two costs sit outside those numbers and are billed to you by the providers directly: your cloud infrastructure and your model API usage. We scope expected ranges during discovery and do not mark either one up. There is no per seat licence, which matters when the people who touch the office layer are a rotating set of coordinators, estimators and subs.

Does the crew have to change how they work?

No, and a deployment that requires it will not survive contact with a Tuesday. The agent meets the field where the field already is: photos and notes from the phone the crew already carries, email and text threads that already exist, the job management system already in use. Nobody learns a new app to make the agent work.

That constraint is why the job documentation workflow is built around what crews already send rather than a structured form. The agent assembles the record. The crew keeps taking photos.

Who owns the data and the agent?

You do, which is the whole point of the Sovereign model. The runtime, the memory, the logs and the credentials live inside your own AWS, GCP or Azure account, and you can revoke our access without the agent stopping. If you ever part ways with us, the deployment stays with you, which is not true of a platform subscription. Migrating off a SaaS AI platform covers what that difference looks like in practice, and the security baseline documents the isolation model and key custody.

Contracting workflows already running on Sovereign

Each of the following is a documented deployment with its own page in the use case library. Each one was built for the office layer of a field services business, and each one runs today.

• Closing a landed job when the vendor blocks every API. A landed construction job produces a fixed packet of paperwork. An agentic employee assembles that packet end to end by driving the vendor software the same way a person does, through the browser, because the vendor grants no API access at all.
• Proving the lead survived the handoff. Every distributed lead gets its own 24-hour clock starting at the moment it arrived, not at a fixed batch hour. When the clock expires the agent checks the CRM for evidence a human actually worked it, reminds the rep once if not, and escalates if it stays cold.
• An inbox briefing that cannot send mail. An agentic employee reads the owner inbox every morning and returns a short briefing split into internal and external, filtering out newsletters and job-board noise, with a stated disposition per thread. It can read and it can draft. It has no ability to send, archive, or delete.
• Contracts on infrastructure you own. One line of natural language produces a branded, legally reviewed agreement with signature fields placed and an audit trail, running on a self-hosted signature platform under your own domain. It stays a draft until a human sends it. Always.
• Which lead sources actually make money. Ranking lead sources by revenue is the most common reporting mistake in a trades business. This use case ranks them by gross profit, evidenced line by line out of the accounting system, and publishes the revenue ranking alongside it so the owner can see the difference.

If two of those describe your week, you have found where to start. If you are still deciding whether the work needs an agent at all or a simpler automation, the sixty second flowchart test will tell you, and the Sovereign Agent page covers what a deployment ships.