How to qualify public bids with AI you can check

Most teams that sell to government have more bids than people to read them. A regional estimator or capture lead opens a package, scrolls to the bond section, checks the insurance limits, looks for a mandatory site visit, skims the scope, and decides in 20 minutes that the bid isn't worth pricing. Multiply that by every bid in every region, and qualification becomes the job.

AI is good at a large part of this work, and bad at a smaller part that matters a lot. This guide is about telling the two apart and setting it up so your team trusts what it gets.

Start with the questions you already ask

Every team has a qualification checklist, even if it lives in one estimator's head. Write it down. A typical one for construction or facilities work looks like this:

• When is it due, and is there a mandatory pre-bid meeting or site visit?
• What bid bond, performance bond and payment bond are required?
• What insurance limits, and must the agency be named as additional insured?
• What license or certification does the bidder need?
• Is it set aside for small, minority-owned, women-owned or veteran-owned businesses?
• Is it low bid or best value, and what are the evaluation criteria?
• Are there liquidated damages, and how much per day?
• Does the scope include work we don't self-perform?
• Are named products specified, and are substitutes allowed?

For software and services, swap in questions about security requirements, data location, contract term, references and required certifications.

Those questions become columns. Each bid becomes a row. Qualification becomes filling in the table and reading across it.

What AI does well

Finding the answer in a long package. The bond requirement might be in the notice, the instructions to bidders, the general conditions or an addendum. A model can read all of it in seconds and pull out the answer to each column.

Keeping up with addenda. When an addendum changes the due date or adds a site visit, the answer should change too. Reading every addendum by hand is the step people skip under deadline.

Reading the same way every time. A tired estimator on a Friday reads differently than a fresh one on a Monday. A model reads the hundredth bid like the first.

Working across regions. One table across every territory lets a manager see where the good work is, instead of hearing about it region by region.

What it shouldn't decide

Whether to bid. Fit with your strategy, your backlog, your relationship with the agency and your read on the competition are judgement calls. The table should make that decision faster, not make it for you.

The price. Pricing depends on your costs, your crews and your appetite for the job.

Anything it can't point to. If the documents don't say, the answer should be "not stated." A confident guess on an insurance limit is worse than no answer.

Make every answer checkable

The single most important feature of AI for this work is a citation on every answer: the document, the page and ideally the sentence it came from. Without it, your team has two bad choices. They can trust the answer and occasionally get burned, or re-read the package to check, which saves no time.

With citations, checking an answer takes one click. Estimators stop re-reading packages and start spot-checking cells, which is the behavior you want.

When you evaluate any tool, open ten answers at random and click through. If you can't see where an answer came from, the tool isn't ready for this job.

Roll it out in a way people trust

1. Copy the checklist your best estimator uses. Start from the columns your team already asks. New columns can come later.

2. Start with one region or one team. Pick people who'll tell you when it's wrong.

3. Run it next to the current process for two or three weeks. Have the team qualify bids the old way and compare. Track every answer that was wrong or missing, and why.

4. When an answer is wrong, fix the column. Most misses come from a vague question. "Bonding?" gets a worse answer than "What bid bond percentage is required, and is a cashier's check allowed instead?"

5. Then widen it. Add regions, then add the columns that only some teams need.

6. Connect it to where decisions are recorded. When a bid is worth chasing, it should move into your CRM or pursuit pipeline without someone retyping it.

Measure the right things

• Time per qualified bid, before and after
• Bids reviewed per week per person
• Missed requirements: bids you priced and then found you couldn't submit
• Coverage: bids you wouldn't have seen at all before

The last one surprises most teams. Once reading is cheap, you can look at every bid in your territories instead of the ones that happened to reach you.

Where Capveon fits

This is what a review in Capveon does. You write your columns as questions, and Capveon reads every document and addendum on every matching bid and fills them in, with the page and sentence behind each answer. When the documents are silent, the cell says so. Your team sorts and filters on the answers and moves the bids worth chasing into a pursuit, or into Salesforce or HubSpot.

If you're making the case for AI to your team, talk to us. We'll build a review with your columns on your territories so you can check the answers yourself.