# AI Go/No-Go Scoring > AI go/no-go scoring uses language models and a general contractor's historical pursuit data to rate a construction opportunity against fit, capacity, competition, and margin. It gives the team a consistent basis for deciding which bids to chase. - Stages: Business Development, Preconstruction - Concepts: LLMs, Data - Published: 2026-08-28 - Canonical: https://buildr.com/library/ai-go-no-go-scoring ## Why it matters in construction Every GC has a go/no-go process on paper, and most of them break down under pressure. A favorite client calls, the pipeline looks thin, or a principal likes the project, and the scoring sheet gets filled in to justify a decision already made. The cost is real: estimators burn weeks on pursuits with a 10 percent hit rate while better-fit jobs get a rushed bid. AI go/no-go scoring applies the same process to each opportunity, using the firm's actual history. It records the reasons for a score before the meeting starts. ## How it works 1. **Opportunity profile.** The model pulls owner, project type, size, location, delivery method, schedule, and competition if known from the RFP or lead record. 2. **Historical comparison.** The model retrieves similar past pursuits from the CRM and estimating history, looking at hit rate, margin, and outcome by owner, project type, and size band. 3. **Capacity check.** The system pulls in current backlog, estimating workload over the bid window, and staff availability during the proposed construction schedule. 4. **Criteria scoring.** Each criterion on the firm's go/no-go rubric, such as client relationship, relevant experience, competitive position, margin potential, and risk terms, gets a score with a short written rationale. 5. **Recommendation and reasoning.** The model produces a weighted score and names the factors that had the most influence on it. The output needs to show its work. A score of 62 without an explanation is not useful. A score that notes the firm lost its last four hard-bid jobs for that owner by an average of 6 percent gives the team something concrete to discuss. ## Example in practice Suppose a commercial GC is looking at two opportunities in the same week: a 28 million dollar hard-bid public school addition and a 14 million dollar negotiated office renovation for a client they have built for twice. The scoring shows the school at 41 out of 100. The firm has bid seven public K-12 hard-bid jobs in five years and won one, at a 2.1 percent margin. Eight bidders are expected. Estimating already has three bids due in the same two-week window. The office renovation scores 78: two prior projects with this owner at 7 and 8 percent margin, one competitor expected, and the schedule lands in a quarter where backlog drops off. The BD meeting takes twenty minutes. They pass on the school, put a senior estimator on the renovation, and log the decision with the score attached so they can check it against the outcome later. ## Go deeper - [Go/No-Go in Construction: 3 Tips to Save Time and Capital](/blog/go-no-go-construction.md) - [5 Go/No-Go Criteria That Separate Profitable GCs From Busy Ones](/blog/go-no-go-criteria-profitable-gc.md) - [The Preconstruction Manager's Guide to Go/No-Go Decisions](/blog/preconstruction-go-no-go.md) ## How Buildr applies this Buildr scores opportunities in your pipeline against your win history and current backlog so go/no-go decisions are grounded in your own data. See [Buildr CRM](/crm). ## Related terms - [AI RFP Analysis](/library/ai-rfp-analysis.md): AI RFP analysis uses language models to read a construction request for proposals, extract the requirements, deadlines, evaluation criteria, and risk terms, and summarize them so a general contractor can decide whether and how to pursue the job. - [AI Pipeline Forecasting](/library/ai-pipeline-forecasting.md): AI pipeline forecasting estimates how much construction revenue may convert from active pursuits. It uses historical outcomes to score each opportunity's win probability and timing alongside the BD team's judgment. - [Data Readiness](/library/data-readiness.md): Data readiness is how complete, consistent, and accessible a construction company's project, pipeline, cost, and staffing records are. It determines whether AI tools can produce useful forecasts and comparisons or confident answers built on gaps. - [Human-in-the-Loop](/library/human-in-the-loop.md): Human-in-the-loop is an AI design pattern in which a person reviews, corrects, or approves the model's output before it takes effect. In construction, an estimator still owns the award and a BD lead still approves a pipeline update, while the AI handles reading and drafting. - [AI Evals](/library/ai-evals.md): AI evals are structured tests that measure how well an AI system performs on a defined task using examples with known answers. A construction firm can use them to test whether a tool reads its sub proposals or RFPs accurately before relying on it on bid day. ## Referenced by - [AI Subcontractor Matching](/library/ai-subcontractor-matching.md): AI subcontractor matching uses a general contractor's sub database and bid history to recommend which subcontractors to invite for each trade package on a construction project, based on trade, location, capacity, past performance, and qualification status. - [AI Workforce Forecasting](/library/ai-workforce-forecasting.md): AI workforce forecasting uses pipeline, backlog, and historical staffing data to estimate how many superintendents, PMs, and field crews a construction company will need each month. It gives leadership time to plan for staffing gaps. - [Reasoning Models](/library/reasoning-models.md): Reasoning models are AI language models designed to spend more effort on a problem before answering. They can be useful for multi-step construction judgments, such as reconciling a spec conflict across three divisions or scoring a go/no-go with competing criteria. ## FAQ ### Does an AI go/no-go score make the decision for us? No. It produces a consistent score and the reasons behind it. Leadership still makes the call, but the discussion starts with the same facts. ### How much historical data do we need? Enough closed pursuits to see patterns, usually a few dozen wins and losses with basic fields like project type, size, owner, delivery method, and outcome. Firms with sparse or messy CRM data get weaker scores until the data catches up. ### What if the model scores a job we know we should chase? Override it and record why. Those overrides are useful signal, and a good tool tracks whether the overrides or the model turned out to be right over time.