AI Vendor Evaluation

Why it matters in construction

Every precon software vendor now says AI. Some have rebuilt their product around it. Others added a chat box to a ten-year-old form. The difference usually appears later, when the tool cannot read a scanned proposal or the vendor’s terms permit training on your pricing.

A GC evaluating AI tools is also making a data decision. Whatever tool wins gets your estimates, sub relationships, and pipeline. Treat the evaluation with the same seriousness as a sub prequal.

How it works

A practical evaluation covers five areas.

1. Data and training. Does the vendor or its model provider train on customer data? Is that in the contract? Who are the subprocessors? What is the retention policy? Get every answer in writing.
2. Security. SOC 2 Type II or equivalent, covering the AI components. Which features read outside documents, and which can take actions or send data out? If a feature does both, what approval sits between them?
3. Accuracy on your data. Run a structured trial. Put the same five proposals, the same bid package, and the same RFP through each vendor. Score extraction completeness, false flags, and whether every output traces to source text. Keep the scoresheet; it becomes the baseline for later.
4. Depth of AI. Is the model connected to your project history, or does it answer from the document alone? When it is uncertain, does it say so or guess?
5. Fit. Integration with the ERP, project management, and CRM. Who owns the data if you leave. Pricing, per seat or per document or per project, against actual volume.

On reference calls, ask a similar-size GC what the tool got wrong in the first quarter and how the vendor responded. The response is often more useful than the original error.

Example in practice

For example, a $200M/year commercial GC shortlists three AI estimating tools. Precon assembles a test kit: a 40-page bid package for a $6M mechanical scope, six real sub proposals including one scanned and one with handwritten alternates, and the leveling sheet the team produced on that job.

Vendor A extracts 94 percent of line items, misses the handwritten alternate, flags two real exclusions and one false one, and links every cell to source text. Vendor B extracts 88 percent, has no source links, and confidently reports zero exclusions on a proposal with three. Vendor C declines to run the trial and offers a curated demo instead, which is its own kind of answer.

IT’s security review finds B’s MSA silent on training. Vendor A wins, and the scoresheet becomes the acceptance criteria for the pilot.