Problem
Software estimation is notoriously difficult, time-consuming, and often inaccurate.
- Sales Bottleneck: Pre-sales teams struggle to give quick, reliable quotes without pulling engineers off billable work.
- Engineering Drain: Developers spend hours breaking down requirements for projects that might never close.
- Scope Creep: Vague initial requirements lead to misunderstood scope and budget overruns.
Solution: EstimatorGPT
The AI-Powered Estimation Copilot. EstimatorGPT is a specialized agentic workflow that turns rough project ideas into detailed technical specifications, feature breakdowns, and effort estimates in minutes. It acts as a senior architect and technical lead, asking the right questions to clarify scope before generating a comprehensive implementation plan.
How it Works
- Input: User provides a high-level project description (e.g., "Uber for dog walking").
- Clarification (Agent): The ClarifierAgent analyzes requirements and asks targeted questions to resolve ambiguities.
- Architecture (Agent + Human): The ArchitectureSpecAgent designs the system (Stack, DB, Integrations). Human-in-the-loop review & approval.
- Breakdown (Agent + Human): The FeatureDecomposerAgent and TaskBreakdownAgent split the work into granular tasks. Human-in-the-loop review & approval.
- Estimation (Agent): The EstimationAgent assigns calibrated effort hours (XS-XL) to every task.
- Output: A complete, exportable report with timelines, costs, and technical specs.
Inputs/Outputs
Inputs:
- Project Description (Natural Language)
- Clarification Answers (User responses)
- Technology Preferences (Optional)
Outputs:
- Executive Summary: High-level timeline and effort overview.
- Technical Specification: Architecture diagrams, Tech Stack, Database Schema, API Strategy.
- Detailed Breakdown: Feature-by-feature task list with hour estimates.
- Implementation Guide: A developer-ready Markdown/PDF manual to start coding immediately.
- JSON Export: Structured data for import into Jira/Linear.
Integrations
- LLM Providers: Configurable (OpenAI GPT-4o default) for reasoning.
- Supabase: Session persistence and history management.
- PDF Generation: Professional report generation for client presentations.
Limits & Risks
- Estimation Variance: AI estimates are calibrated benchmarks but require senior dev review for final contracts.
- Context Limit: Extremely large monolithic legacy migrations may need manual decomposition before input.
- Human-in-the-Loop: Designed to assist, not replace, technical decision-makers. Review is required at Architecture and Feature stages.
Next Steps
- Try the Live Demo: Visit estimatorgpt.ai.
- Run a Shadow Test: Use EstimatorGPT on your next 3 inbound leads and compare with manual estimates.
- Deploy Kit: Customize the "Estimation Copilot Kit" for your internal workflows.