FinStartAI

Financial Advisor AI Agent For Startup Founders

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FinStartAI - Deep Research AI Agent For Startup Finances

Our core category-

  • Artificial Intelligence & Machine Learning
  • FinTech & Digital Economy

Inspiration

Early-stage startup founders consistently struggle with one of the most mission-critical aspects of building a startup: navigating the financial ecosystem.

Finding grants, accelerators, investors, or even sizing the market can take weeks, and even months, of manual research and often require expertise most founders do not yet have.

Many founders also miss funding opportunities simply because the news of the opportunity never surfaced in time or was buried across hundreds of websites. And market sizing (TAM/SAM/SOM) is notoriously time-consuming, requiring structured data collection, modeling, and assumptions.

We wanted to build a system that acts like a dedicated financial research team: on demand, always up-to-date, and personalized to each startup.

This led to FinStartAI, a multi-agent financial intelligence system build using Google ADK.

What it does

FinStartAI serves as an AI-driven financial advisor for early-stage founders.

At its core is a Financial Advisor Agent, which acts as the orchestrator, routing user requests to three specialized deep-research & reasoning agents:

Diagram of AI Agent with SubAgents

1. Market Sizing Agent

A high-accuracy research agent that:

  • Computes TAM/SAM/SOM using structured formulas
  • Analyzes target geographies, ICPs, pricing models
  • Generates market assumptions based on industry data
  • Produces a quantified market entry path

This lets founders instantly understand the scale and viability of their market.

2. Fundraising Strategy Agent

A reasoning agent trained on investment patterns that:

  • Analyses founder profile (First time founder,domain expertise,experience etc.)
  • Inspects traction metrics (users/ revenue/ growth etc.) and startup details.
  • Designs tailored fundraising strategies
  • Identifies suitable investor types (angels, VCs, strategic)
  • Recommends fundraising stages
  • Suggests pitch positioning, valuation logic, and milestones needed

It gives founders clarity on how to raise and from whom.

3. Grant & Non-Dilutive Funding Agent

A deep-search agent that:

  • Searches grants, accelerators, government programs, and competitions according to startup details.
  • Filters results based on industry, geography, founder attributes, and business model
  • Outputs requirements, deadlines, funding amounts, and fit reasoning

Founders get a curated list of opportunities they would otherwise spend hours discovering.

4. Root Financial Advisor Agent (Orchestrator)

The orchestrator agent collects context (startup stage, industry, geography, funding needs) and intelligently routes the query to the correct expert agent, ensuring:

  • Minimal user effort
  • Targeted research
  • High-quality structured outputs

Together, these agents behave like a virtual financial team capable of deep research, financial modeling, and funding intelligence.

How we built it

  • Built using the Google Agent Developer Kit (ADK)
  • Implemented a multi-agent architecture with an orchestrator → subagent pattern

  • Designed domain-specific prompts for each agent, focusing on:

    • Data extraction
    • Structured financial reasoning
    • Multi-step research workflows

  • Used environment variables and ADK web tools for local agent testing

  • Created a modular design so more financial agents can be added later

The core engineering challenge was constructing high-precision, instruction-tight prompts to structure output and prevent reasoning drift.

Challenges we ran into

  • Learning Google ADK, which is a new framework with limited examples
  • Debugging agent loading, environment variable handling, and missing dependency issues
  • Designing prompts that are concise but cover complex financial context
  • Getting agents to return structured, multi-section financial reports reliably
  • Coordinating subagent calls through the orchestrator without losing state

Accomplishments that we're proud of

  • Built a working multi-agent financial intelligence system
  • Achieved structured, high-quality output from domain-specific agents
  • Created a scalable architecture where additional financial agents can be added easily
  • Reduced complex startup financial workflows (TAM/SAM/SOM, grants search, investor strategy) into automated AI processes
  • Most of all, we're proud to be able to solve a real headache for startup founders.

What we learned

  • How to architect multi-agent systems using Google ADK
  • Prompt engineering for domain-specialized reasoning
  • Designing financial workflows that can be broken down into callable agents
  • Handling state, context-passing, and orchestrator logic
  • Effective structuring of AI output for founders

What’s next for FinStartAI

  • Integrate MCP to connect agents with external APIs (funding databases, grant portals, investor CRMs)
  • Add a Financial Modeling Agent for projections, runway, and burn calculations
  • Add a Competitive Landscape Agent to benchmark against industry players
  • Deploy as a lightweight web interface for self-serve financial intelligence
  • Enable scheduled scans for new grants and investor programs

Built With

  • agent-development-kit
  • git
  • github
  • google-search-api
  • googleadk
  • litellm
  • mcp
  • pip3
  • python
  • uv
  • web-scraping
  • yahoo-finance
  • yfinance
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