# AI in Construction Business Development > AI terms for GC business development: RFP analysis, go/no-go scoring, and CRM records that stay current without data entry. - Stage: Business Development - Canonical: https://buildr.com/library/business-development Business development is where AI hits a GC first, because the raw material is text. RFPs, emails, meeting notes, call recaps. Every one of those used to need a person to read it and retype it before anything reached the CRM. The terms below cover what language models do with that text, how agents keep pipeline data current, and which calls still belong to the BD lead. ## Terms - [Activity Capture](/library/activity-capture.md): Activity capture uses AI to log emails, meetings, calls, and site visits against the right contacts and opportunities in a construction CRM. It gives business development teams a usable relationship history without manual data entry. - [Agentic Workflow](/library/agentic-workflow.md): An agentic workflow is a business process in which AI agents handle defined steps, such as intake, extraction, and record updates, while people review the work at checkpoints. A preconstruction team might use one for bid intake or CRM upkeep. - [AI Agent](/library/ai-agent.md): An AI agent uses a language model and connected tools to carry out multi-step tasks. For example, it can read a bid invitation, check it against go/no-go criteria, and create an opportunity in the CRM. - [AI Go/No-Go Scoring](/library/ai-go-no-go-scoring.md): 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. - [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. - [AI Proposal Generation](/library/ai-proposal-generation.md): AI proposal generation uses language models to draft construction proposal content, such as approach narratives, past project descriptions, and team bios, from a general contractor's own history and the RFP requirements. The pursuit team then reviews and edits the draft. - [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-Native vs. AI Bolted-On Software](/library/ai-native-vs-bolted-on.md): AI-native software puts AI models inside its core workflows and data model. Bolted-on AI is usually a separate feature added to an existing product. For a construction firm, the difference affects whether the AI can work with pipeline, estimate, and staffing data or only discuss it. - [Autonomous CRM Updates](/library/autonomous-crm-updates.md): Autonomous CRM updates use AI agents to read emails, calendar events, and call notes, then create or update contacts, opportunities, and activities in a construction CRM without manual data entry for every interaction. - [Copilot vs. Agent](/library/copilot-vs-agent.md): Copilots and agents are two ways AI can appear in construction software. A copilot helps a person with a task, such as drafting a proposal section. An agent takes a goal and carries out its steps, such as processing a bid invitation and proposing a CRM update. - [Embeddings and Semantic Search](/library/embeddings-and-semantic-search.md): Embeddings are numeric representations of text that place similar ideas near each other even when the wording differs. Semantic search uses them to find construction documents, subs, or past projects by meaning rather than exact keywords, so acoustical ceiling can match ACT and lay-in tile. - [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. - [Large Language Model (LLM)](/library/large-language-model.md): A large language model (LLM) is an AI system trained on large amounts of text that can read, summarize, and generate language. In construction, it can help parse RFPs, sub proposals, and specifications for preconstruction teams. - [Model Context Protocol (MCP)](/library/model-context-protocol.md): Model Context Protocol (MCP) is an open standard that lets AI assistants connect to outside tools and data sources through a common interface. For a construction firm, it can let an AI agent use a project management system, CRM, or document library without a separate custom integration for each connection. - [Natural Language Querying](/library/natural-language-querying.md): Natural language querying lets a construction team ask questions about business data in plain English, such as 'what is our weighted pipeline for Q4' or 'which superintendents are free in March,' and get answers from the CRM, backlog, and workforce plan without building reports or formulas. - [Prompt Engineering](/library/prompt-engineering.md): Prompt engineering is the practice of writing instructions, examples, and context for an AI language model so it produces reliable output, such as telling it exactly how to classify exclusions in a construction subcontractor proposal. - [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. - [Structured Data Extraction](/library/structured-data-extraction.md): Structured data extraction uses AI to turn unstructured construction documents, such as sub proposals, RFPs, and specs, into typed fields like line items, prices, exclusions, and dates that can be sorted, compared, and loaded into an estimate or CRM. ## How Buildr applies this Buildr's construction CRM logs calls, emails, and meetings on its own and keeps pipeline records current. See [Buildr CRM](/crm). ## Go deeper - [The Days of Manually Updating Your CRM Are Over](/blog/manual-crm-updates-are-over.md) - [Your Clients Should Be Calling You Before the Architect](/blog/call-before-the-architect.md) - [Your Preconstruction Has a Leak](/blog/preconstruction-has-a-leak.md)