FinGTM Agent

Context

FinGTM Agent is a full-stack AI agent application for early-stage B2B SaaS FinTech founders, product marketers, SDRs, and BDR teams. Users fill in one structured product brief and receive a deployment-ready GTM and sales enablement pack in minutes.

Problem

• Early-stage FinTech teams often spend weeks creating positioning, ICP, outbound copy, discovery scripts, pricing, and launch plans manually.
• Generic AI writing tools do not understand B2B enterprise selling mechanics such as buying committees, procurement friction, security review, and stage-specific messaging.
• Long AI text outputs are hard to scan, copy, validate, and turn into working sales assets.
• FinTech messaging needs guardrails to avoid unsupported revenue claims, fake certifications, or unsafe compliance wording.

Design Goal

• Turn one 16-field product brief into a structured 16-section GTM report.
• Make the workspace feel like a professional SaaS tool, not a chatbot.
• Keep recommended actions, FinTech guardrails, KPI readiness, and generated report sections visible and actionable.
• Use market data enrichment to make output more grounded than a generic strategy template.
• Support copy-ready and download-ready outputs for real GTM workflows.

Key Features

• 16 GTM sections including positioning, ICP, buyer personas, pricing, outreach, discovery questions, objection handling, and a 30/60/90-day launch plan
• Stripe-style SaaS workspace with sidebar navigation, KPI status cards, product input form, next actions, and guardrails panels
• DeepSeek-powered FastAPI backend with function calling tools for company financials, macro context, and competitor identification
• Live market data enrichment through yfinance and FRED API, with competitor radar chart, TAM/SAM/SOM funnel, and macro badges
• Structured JSON extraction using typed Pydantic models for market size, competitor tables, and executive summary data
• Section-level copy, full Markdown download, sample PayFlow AI data, error states, and API key isolation in the backend

Outcome / Reflection

FinGTM Agent helped me understand that AI product design is a systems problem: the interface, prompt structure, typed outputs, tool calls, guardrails, and export workflow all shape whether AI output becomes useful. The strongest design decision was treating the agent as a structured workspace rather than a blank chat box.

Design Process