Recommender Systems
A recommender system is an AI method that looks at a construction company's historical data — past bids, project outcomes, supplier performance, crew productivity — and surfaces patterns that help you make better decisions on the next project. It's pattern recognition applied to the data you already have but aren't fully using.
What I Build
1. Bid Strategy & Proposal Optimization
The system analyzes your past proposals — wins and losses — to identify what made the difference. Was it pricing? Scope approach? The team you proposed? Client relationship history? It then recommends adjustments for the current bid based on those patterns.
In practice:
- Before submitting a bid, the system compares your proposed pricing against comparable past projects and flags significant deviations
- It identifies which team members have the highest win rate with this type of client or project
- It surfaces scope items you commonly include on winning bids that might be missing from the current proposal
What changes: the bid gets reviewed against your win record before it goes out, not after it loses.
2. Supplier & Subcontractor Selection
Instead of relying on the same three subs you've always used, the system ranks your subcontractor pool based on actual performance data: schedule adherence, change order frequency, safety record, and price accuracy. It factors in the specific project type and conditions to recommend the best fits.
In practice:
- When you need a mechanical sub for a hospital project, the system shows you which subs have the best track record on similar institutional work — not just who's cheapest
- It flags subs whose pricing has been consistently off (high change order rates), even if their base bids look competitive
- It identifies subs you haven't used in a while who performed well on past projects, which is how good subs quietly fall off a bid list
What changes: sub selection runs on performance data instead of on who called most recently.
A free two-page sample of the market intelligence work: three projects in your market you are probably not tracking.
Related
- AI for construction estimating: where bid recommendations get applied.
- AI for construction profitability: which work is worth recommending in the first place.
- Predictive analytics: the forecasting layer underneath.