Can AI help with construction bids and quotes?

Yes, AI can help with construction bids and quotes. The bigger opportunity I see is what happens to the takeoff after the estimator finishes it. Most of the time that information gets used once for the quote and then sits in a file.

When I talk to construction teams about what they want built, estimating comes up a lot. They want to put their takeoff information somewhere and keep building on it. The data is already there. They just need a way to use it across the rest of the operation.

I spent 14 years at Pulte and worked in sales, land, and construction, so I've seen a lot of good estimating work get re-entered by hand further down the line.

Turning a construction takeoff into a quote

Say an estimator marks up a Bluebeam set or types quantities into it. In the workflows we build, that information comes into a portal and becomes the starting point for the next steps.

From there, the system can create the quote and proposal in a clean, professional format that looks however the company wants it to, with their branding on it. They can email it to their customer right from that same screen.

Using AI to track quote approvals

We can also set up AI to watch their inbox for the customer's reply. When a quote comes back accepted, it updates the portal and lets the owner know they're good to go. Once that approval is in, the purchase orders, invoicing, and material and cost tracking can all get kicked off from inside the same portal. The exact setup depends on what the company needs and the systems it already uses.

That's the part I think is pretty cool. Once the information lives in one place, you can do a lot with it. You can build a real process around it, so the takeoff keeps working for you long after the quote goes out.

Where AI fits in construction estimating, and where code fits

We use a mix of AI and scripting, and we connect systems through APIs so the software can share information. Which one we use depends on the task.

If a calculation follows defined rules, I want those rules written in code and tested. Code runs the same math the same way every time. It still needs the right inputs and the right logic, so we check it.

AI isn't 100% accurate 100% of the time. There's no reason to let it guess at a calculation we can define. Where it does help is in other parts of the workflow, like drafting the emails and updates that go out to clients and vendors, or catching an accepted quote in your inbox. How closely someone reviews that output should match how it's going to be used.

We also use AI to help us build the software faster. That's a separate question from whether the finished system needs AI to do a particular task. Plenty of portals are valuable simply because they organize information and move it through the business. That's the idea behind the operating systems we build.

Why the estimator still matters

I'd always keep an experienced person responsible for reviewing the estimate and the assumptions behind it. Putting incomplete information into a clean-looking quote doesn't make it complete.

Each company also needs to decide which outputs need approval before they move downstream. A draft email and a purchase order that commits money carry very different risk. We work those decisions into the scope of the build.

When I look at an estimating workflow, I start with what information the team already produces and where it needs to go next. From there we figure out which steps need code, where AI is actually useful, and where a person needs to make the call.

If your estimating process ends at the quote and everything after it is still manual, that's usually where we start. An AI Readiness Audit is the easiest way to map it out with us.

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Kelly Stephens is the CEO of EyeOn Automations, a custom software and AI company for construction and real estate operators. She spent 14 years at PulteGroup in sales, land development, and construction across Arizona and California before building software for the industry. Based in Southern California.