Semantic intent
Free-form founder questions are converted into structured intent, parameters, and operating context.
Agentic Startup Finance & Operations Platform
Simulate how finance agents interpret founder questions, call revenue and operations tools, model runway impact, and generate explainable recommendations for human review.
5Finance agents
6Tool interfaces
1Decision layer
Project Overview
Agentic Startup Finance & Operations Platform
This demo models an AI-native operating layer for startup founders. A founder can ask a question about hiring, runway, revenue risk, overspending, activation drop-off, or board readiness. The system extracts intent, selects the right agents and tools, gathers evidence, and generates ranked recommendations with human review.
Tool-using agents
Specialized agents call Stripe-like, forecasting, expense, customer-health, and LLM tools.
Dynamic planning
The orchestrator selects relevant agents, skips unnecessary work, and creates an evidence-backed plan.
LLM synthesis
OpenAI reasons over grounded findings to generate concise recommendations and tradeoff analysis.
Human review
High-impact actions are ranked, reviewed, and routed to a founder or finance lead for approval.
Problem
Startup operating decisions depend on fragmented signals
Founders often decide whether to hire, reduce spend, pursue collections, or prepare investor updates using disconnected views across billing, subscriptions, customer health, onboarding, expenses, and cash.
Dashboards can show what happened, but they rarely explain what action matters most, which team should act, and how the decision affects runway or revenue risk.
Solution
Question → plan → tools → evidence → action
The platform converts founder questions into adaptive AI workflows. It identifies the operating intent, extracts parameters such as segment, role, expense type, and timeframe, then selects the agents and tools needed for the decision.
The output is an explainable recommendation, not a generic chatbot answer. Every recommendation is tied to tool calls, evidence, confidence, financial impact, and a reviewable next step.
How It Works
Adaptive finance and operations workflow
A founder starts with a natural-language question. The backend interprets the question, creates a dynamic workflow plan, calls the selected tools, streams agent activity, and synthesizes a recommendation for human approval.
Question
Interpreted
Interpreted
The orchestrator converts free-form founder language into structured operating intent.
Intent
Extracted
Extracted
The semantic layer identifies parameters like department, timeframe, hiring count, or revenue-risk category.
Tools
Selected
Selected
The planner selects only the relevant Stripe-like, forecasting, expense, customer-health, and LLM tools.
Evidence
Gathered
Gathered
Agents gather metrics, signals, failed payments, spend anomalies, and operational context.
Reasoning
Generated
Generated
OpenAI synthesizes grounded recommendations, tradeoffs, confidence, and risk analysis.
Review
Routed
Routed
High-impact recommendations are ranked and routed to a founder or finance lead before action.
System Architecture
Frontend, API, tool layer, and AI reasoning workflow
Frontend
Next.js / React
Demo tabs, founder command center, adaptive workflow stream, semantic extraction, tool traces, charts, recommendations, and review queue.
API Layer
FastAPI agent service
Routes founder questions, returns metrics and scenarios, orchestrates agents, exposes workflow results, and maintains review actions.
Tool Layer
Mock Stripe + operating tools
Stripe-like subscription and invoice tools, forecasting tools, expense analysis, customer-health tools, and investor summary tooling.
AI Layer
Semantic routing + OpenAI reasoning
Extracts intent and parameters, plans agent execution, synthesizes evidence, and produces grounded operating recommendations.
Agent Workflow
Multi-agent operating model
- Semantic router: converts free-form founder questions into structured intent and parameters.
- Planner: selects relevant agents and skips unrelated work based on the question.
- Revenue agent: calls Stripe-like subscription, invoice, and revenue-at-risk tools.
- Runway agent: models hiring, burn, cash balance, and runway impact.
- Operations agent: identifies overlapping or underutilized vendor spend.
- Growth risk agent: evaluates customer health, activation drop-off, and churn exposure.
Results & Impact
What the demo proves
- Shows how agentic AI can move beyond chat into operational decision infrastructure.
- Connects Stripe-style billing data with runway, expense, activation, and customer-health signals.
- Explains which agents and tools were selected, skipped, and used to generate evidence.
- Ranks recommendations by impact, confidence, feedback weight, and review status.
- Demonstrates a human-in-the-loop path for finance and operating decisions.
- Provides a credible foundation for future Stripe sandbox, Plaid, CRM, and Slack integrations.