Purpose

This article is intended for investors and analysts using DiligenceGPT. Its purpose is to set expectations and explain how the different parts of the platform work together across the full due-diligence lifecycle.

DiligenceGPT is built to assist investors, not make decisions on their behalf. It adds structure and consistency to the diligence process so teams can evaluate opportunities more efficiently.

DiligenceGPT is StartupFuel’s AI-powered due diligence platform that helps investment teams evaluate startups efficiently and consistently. It automates time-consuming analysis while keeping decision-making firmly in human hands.

The platform connects sourcing, screening, diligence, decision-making, and portfolio tracking into a single, continuous workflow. This ensures that insights gathered early in the process are never lost and remain accessible after investment.

🗂 Core Objects in DiligenceGPT

DiligenceGPT is built around a small number of core objects. Understanding these objects explains how the platform stays organized and scalable.

🏢 Workspace

A Workspace is the secure environment for your organization. It contains all deals, portfolio companies, dashboards, and users. Investment criteria, workflows, and permissions are defined at the workspace level, ensuring consistency across your team.

💼 Deal

A Deal represents a startup under evaluation. As a deal moves forward, documents, AI summaries, scores, analyst notes, and status updates are added to the same record. This creates a single source of truth throughout the diligence process.

When a deal is approved or invested in, it becomes a Portfolio Company. This transition preserves all historical diligence data while enabling post-investment monitoring and reporting.

Deal Radar supports sourcing and discovery by surfacing potential startups in one centralized view. It surfaces potential startups and It uses AI to provide initial signals and insights that help teams review opportunities more efficiently. 

The Dashboard provides a high-level view of activity across the workspace. It summarizes deal status, portfolio health, and key signals so investors and analysts can quickly understand what requires attention.

DiligenceGPT intentionally separates automation from judgment so teams understand what the platform does automatically and where human input is required.

AI is used to:

• Extract information from website, likedIn and notes

• Summarize business, market, and traction data

• Benchmark startups against comparable companies

• Generate standardized diligence reports and scores
  

Humans are responsible for:

• Interpreting insights and risks

• Adding qualitative judgment and context

• Collaborating with internal stakeholders

• Making approval, rejection, and investment decisions
  

Reports, scores, and statuses are designed to complement one another rather than operate in isolation.

1. Scores provide structured, quantitative signals that reflect how a startup aligns with your investment criteria. They enable consistent comparison across deals.
   
2. Reports bring together extracted data, benchmarks, and insights into a readable format suitable for internal review and investment committees.
   
3. 
   Statuses show where a deal is within the diligence lifecycle and help teams manage workflow, accountability, and handoffs.
   

Together, these elements create a transparent and auditable diligence record.

DiligenceGPT supports the full investment journey in a single platform. The typical lifecycle includes:

• New: Startups enter the workspace through Deal Radar or manual uploads.
  

• Reviewing: Teams perform an initial screen to determine whether a deal fits their investment thesis. At this stage, users review Deal Fit, examine deal details such as AI-generated website and LinkedIn summaries, and improve data completeness using the Data Sufficiency report by adding missing information. This stage results in a decision to either move the deal forward or archive it.
  

• First Diligence: Initial analysis begins using structured reports, notes, and collaboration tools. A Deal Brief and Market Intelligence reports give teams a shared, structured starting point—summarizing the company, market dynamics, competitive landscape, and key risks—so analysts can quickly align, add insights, and collaborate efficiently on early diligence.

• Deep Diligence: Teams conduct detailed evaluation using Valuation and Metrics reports to analyze deal economics, financial performance, and key metrics. 

• Offer Signed: Approved deals move forward once terms are finalized and signed.

• Portfolio Company: Closed deals transition into portfolio companies for long-term tracking and monitoring.
  

This end-to-end approach ensures continuity from first review through post-investment monitoring.

💡 Why This Matters for Investors and Analysts

By centralizing data and standardizing workflows, DiligenceGPT helps teams reduce manual effort, improve consistency, and preserve institutional knowledge. Analysts spend less time gathering information and more time evaluating it, while investors gain clearer visibility across the pipeline and portfolio.