ML-Powered Estimate Assistant for Construction Cost

At this firm, the owner personally reviewed every estimate. The busier we were, the longer the bottleneck grew, eroding confidence from clients and momentum on kick offs. Passing off estimates left the executive lacking confidence in the expected margins. Each project's unique architecture made unit pricing unreliable, while time-card and schedule data sat unused in Buildertrend and QuickBooks with no analytic impact on how estimates were generated. The result: slow turnaround, inconsistent pricing logic, low confidence, high friction,and limited scalability.

I designed a modular estimation assistant that ingests your company's business data from labor rates to local material pricing data, and feeds an AI estimator trained on historical profitability, labor efficiency, and schedule density. The system can review existing estimates and provide a confidence metric for each line item, with traceable logic—helping executives to justify pricing and protect margin consistency.

• System Architecture — Designed modular pipeline around Buildertrend + QuickBooks integration
• Workflow Analysis — Mapped executive and estimator processes for optimization opportunities
• Technical Specification — Defined data schema, risk controls, and validation logic
• Proposal Development — Created technical roadmap for development engagement

This demonstrates translating business inefficiency into technical systems that balance automation with explainability.

Data Access Limitation: Buildertrend doesn't offer direct API access, forcing us to use manual file uploads instead of automatic data sync
Data Quality Issues: Historical estimates had inconsistent formatting and missing information, requiring extensive data cleaning
Integration Complexity: Connecting multiple systems (Buildertrend, QuickBooks, Excel) created compatibility challenges
User Adoption Risk: Estimators were skeptical of AI recommendations, requiring clear explanations for every suggestion