Foresight AI: Private Market Data Platform
Foresight AI helps venture capital and private equity firms unify investment data. It is designed for teams that need a single source of truth for cap tables and fund metrics.
At a glance
- Best for
- Venture Capital Firms, Private Equity Firms, M&A Teams, Corporate Development Firms, Lenders
- Pricing
- Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
- Key use cases
- Data-Driven Deal Sourcing, Due Diligence Workflow, Post-Investment Portfolio Modeling, LP Reporting
- Integrations
- CRM, Fund accounting, GitHub, Harmonic, Pitchbook
- Official website
- Visit Foresight AI official website
Foresight is a data management platform for the private market that uses LLMs to unify pre- and post-deal company information. It organizes this data into three functional areas: Sourcing for deal discovery, Diligence for pipeline and workflow management, and Portfolio for post-investment analysis.
The software is built for investment professionals, including deal teams, CFOs, and operating partners. It helps users manage data by integrating fund accounting, CRM data, and third-party feeds into one interface.
Practical applications include tracking company KPIs, modeling exit scenarios, and managing cap tables. Because it centralizes various data points, it may help IR teams and finance leads provide fund performance answers more efficiently.
Buyers should confirm how the platform's data unification handles their specific third-party data feeds and whether the API and data warehouse options meet their technical requirements.
Key Features
Cap Table Management
Supports creating, modifying, and managing cap tables within the platform.
Scenario Analysis
Includes tools to model pro forma rounds and potential exit waterfalls.
Fund Metrics
Provides access to fund metrics such as DPI, TVPI, RVPI, and Net IRR in real time.
Company KPI Tracking
Supports collection and monitoring of universal and custom company-level metrics.
Diligence Kanban Board
Helps users move companies through deal stages and assign diligence tasks.
Data Unification API
Provides an infrastructure API and hosted data warehouse for building internal tools.
Use Cases
Data-Driven Deal Sourcing
Unifying third-party data sources into a single feed to identify companies that match a specific investment thesis.
Due Diligence Workflow
Centralizing prospective deal notes and interactions while integrating data from sources like GitHub and Pitchbook.
Post-Investment Portfolio Modeling
Analyzing holdings and modeling future ownership after equity rounds or exits.
LP Reporting
Creating reports and answering fund questions using unified portfolio data.
Best For
- Venture Capital Firms
- Private Equity Firms
- M&A Teams
- Corporate Development Firms
- Lenders
Integrations
- CRM
- Fund accounting
- GitHub
- Harmonic
- Pitchbook
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
FAQ
What does Foresight AI do?
- Foresight AI is a platform that unifies pre- and post-deal private company data using LLMs to support sourcing, diligence, and portfolio management.
Who is Foresight AI designed for?
- It is designed for managing partners, deal teams, CFOs, and IR teams at venture capital firms, private equity firms, and corporate development teams.
What fund metrics does the platform track?
- The platform provides tracking for DPI, TVPI, RVPI, and Net IRR.
Source category: Finance & Accounting
Source subcategory: Financial Planning
More tools in Finance & Accounting
Other published listings in the Finance & Accounting category.
More tools tagged “Financial Planning”
Related listings that share the same software type tag.
Categories
Software Type
How AI is used
Foresight AI is a private market data platform for venture capital and private equity firms. It supports workflows for deal sourcing, diligence, and portfolio management by unifying fragmented data using LLMs.
Pros & Cons
Pros
- Integrates fund accounting and CRM data into one UI
- Includes native tools for modeling exit waterfalls
- Supports digital collection of portfolio company KPIs
- Centralizes third-party data feeds for diligence workflows
Cons
- Pricing details are not clearly provided in the evidence
- Buyers should verify if LLM-based unification meets their specific data accuracy requirements
