16 Cornerstone Winter 2025–2026 agchouston.org DRILL DOWN A Contractor’s Guide to Construction Project Intelligence — sector trends, feasibility questions, rough orders of magnitude — before the formal bid stage. The time saved on manual research becomes time spent evaluating and preparing. Mercator.ai customers report: ] 40% improvement in BD efficiency ] 60% reduction in time spent quali- fying leads ] 3–5× more early-stage opportunities entering their pipeline These are not theoretical figures; they reflect the aggregated results of dozens of Texas firms using Mercator.ai. Why Texas Is the Proving Ground Texas is a natural test bed for project intelligence because of its scale, pace and decentralization. ] Large private-sector activity means many projects never hit public bid boards. ] Fragmented data makes it difficult to track early signs manually. ] Fast-growing metros — Houston, Austin, Dallas, San Antonio — pro- duce thousands of planning and zon- ing events each month. ] Developers assemble sites quietly, often through multiple intermediaries and filings across jurisdictions. This environment benefits from scale: The more properties and filings a platform can monitor, the earlier it can identify credible projects. As one Texas GC put it, “Tracking this by hand just isn’t possible anymore. AI is the only way to see the real market.” Integrating Project Intelligence into Daily Workflows Contractors who find sustained value with project intelligence tend to follow a similar adoption approach. Step 1: Define what “good” looks like Leadership aligns on geography, sector focus, preferred delivery models, project size thresholds and existing relationships to prioritize. These become the filters Mercator.ai uses to surface relevant work. Step 2: Build a weekly review routine A small team (often BD and precon- struction) reviews new early signals, flags opportunities and assigns outreach. Strong teams tie this to a weekly standing meeting, similar to a backlog review. Step 3: Integrate into the CRM Opportunities selected for pursuit move directly into the CRM, where notes, contacts and timelines are tracked consistently. This eliminates spreadsheets and lost email threads. Step 4: Measure outcomes Contractors track early qualified oppor- tunities, meetings generated, bid-to-win ratios on upstream opportunities and time saved on manual research. These metrics provide an objective view to help quantify ROI. What Project Intelligence Is — and What It Isn’t Project intelligence is not a replacement for relationships, instincts or experience. Nor is it a prediction engine. It’s an organizing system that struc- tures market information so contractors can act earlier and more strategically, especially in regions where private devel- opment dominates and public visibility is limited. As more areas of construction adopt AI for operations, scheduling, safety and risk forecasting, project intelligence applies the same logic at the earliest point in the pipeline. They can find pat- terns, connect records and give teams the information they would otherwise have learned too late. A more predictable pipeline One contractor summarized the shift simply, saying, “We used to learn about projects when they were fully baked. Now we see them when ideas are form- ing. We didn’t change our team; we changed our timing.” That difference — reacting versus pre- paring — is at the heart of construction project intelligence. For firms operating in markets like Texas, Florida and the Southeast, where private development moves quickly and quietly, earlier visibility is becoming less of an advantage and more of a requirement. The question is straightforward: Would learning about your last major project three or six months earlier have changed the outcome? For a growing number of contractors using Mercator.ai, the answer has been yes. By linking scattered data, intelligence turns isolated records into actionable project profiles.
