Benchmarks that compound with every bid.
Your bid history is a benchmark engine waiting to be turned on. PreconIntel classifies, normalizes, and serves it back to your next estimate automatically.
Why most GC benchmarks don't work.
RSMeans data is stale by definition
National cost books lag reality by 6–18 months, don't reflect your region, and don't know what your subs actually charge. Useful for ROM, dangerous for award.
Your history isn't a benchmark
Three years of bids in email, Excel, and Procore aren't benchmarks — they're archaeology. Turning them into unit costs you can use takes an FTE most GCs can't justify.
Blank-sheet estimates lose money
Every new project that starts from scratch inherits zero learning. Your estimator's gut is the benchmark. When they leave, the benchmark leaves with them.
Four steps from bid history to live benchmark.
Every parsed bid feeds the engine
AI Bid Inbox classifies line items to CSI subdivisions automatically. One year of bidding generates thousands of data points across your top 10 divisions.
Normalized across projects and time
Unit costs adjust for project type, region, and market conditions. Austin labor rates don't mislead Dallas estimates. 2024 concrete doesn't skew 2026.
Outlier detection on new bids
When a sub bids 20% under the cluster, the system flags it against your historical range — not just against their peers in this bid.
Procore closes the loop
Awarded commitments and CO data flow back from Procore. Your benchmark learns the difference between what you estimated and what the project actually cost.
Every CSI subdivision, in the unit it bids.
Division-level $/SF is too noisy. Subdivision-level in the natural bid unit is what's actually useful. PreconIntel benchmarks in the unit each subdivision typically bids.
The products behind this solution.
Cost benchmarking runs on AI Bid Inbox (source data) and Procore Integration (actuals feedback).