Published on 2026-07-21

Building GapFoundry: An AI Agent That Finds Market Gaps in Minutes

How I built GapFoundry: an AI competitive research platform that automates market gap analysis with cited reports and interactive follow-up. Includes full demo.

GapFoundryAgentic AIAI EngineeringLangGraphLangChainRAGpgvectorFastAPIPythonNext.jsCompetitive ResearchVector DatabasesBackend Engineering

Introduction

Two months ago, I built GapFoundry, an AI-powered competitive research platform designed to automate market gap analysis for founders, product teams, and agencies.

The idea started from a frustration I kept hitting while exploring new product concepts: competitive research is essential, but the manual workflow is brutal. You scrape reviews, map feature matrices, open dozens of tabs, and still walk away unsure where the actual gap is. Generic LLM chat makes it worse by giving you confident answers with no sources and no structure.

GapFoundry condenses what is often a three-hour research workflow into minutes. You hand it a raw business idea or a competitor URL, and it produces a structured, cited research report you can chat with and update.

The project lived on my local machine for a while before I recorded a demo to share how it works. This post covers what GapFoundry does, what I built, and what it demonstrates for hiring teams.

I did not set out to build another LLM wrapper that answers basic prompts. The focus was a multi-stage agentic workflow where the system searches, extracts, analyzes, and produces structured output with sources. That is where the real value of generative AI shows up in production.

Project page: /projects/gapfoundry

Watch the Demo

The demo shows the full workflow:

  • Entering a product idea or competitor URL to research
  • Autonomous multi-stage research running in the background
  • Structured competitor and feature analysis output
  • Confidence scores and source citations on major claims
  • Chat over the generated report with follow-up questions
  • Dynamically updating the final output based on new questions

If you are a recruiter or founder evaluating engineering depth, start here.

The Problem

Manual competitive research, done properly, involves:

  • Scraping and reading reviews, landing pages, and comparison articles
  • Building feature matrices across competitors
  • Hunting for positioning gaps and underserved segments
  • Cross-checking claims before making product or investment decisions

That process routinely takes hours. Founders and agencies know the value of this work, but the time cost means it either gets skipped or done shallowly.

GapFoundry was designed to deliver three things a basic chatbot cannot:

  1. Structured output you can act on (competitor lists, feature matrices, gap analysis)
  2. Sources and citations so you can verify claims before making decisions
  3. Updatable research that improves when you ask follow-up questions

You pass in a business idea or a competitor URL. The system returns a structured, cited report. Then you treat that report as an interactive knowledge base: chat with it, ask follow-up questions, and revise the output as your understanding of the market evolves.

Why Not a Simple LLM Wrapper?

The easiest version of this product is a chat box with web search enabled. That is also the least useful version for serious research.

Simple prompt-response systems fail market research for predictable reasons:

  • They hallucinate competitors that sound plausible but do not exist
  • They merge features across companies without attribution
  • They cannot maintain structure across a long research session
  • They do not update documents when you ask follow-up questions

GapFoundry treats research as a multi-stage process, not a single completion call. Building it reinforced something I believe strongly about AI engineering: the true value of generative AI lies in deep agentic workflows, not simple prompt-response interactions.

What GapFoundry Does

From a user's perspective, the platform delivers:

  • Automated competitive research from a business idea or competitor URL
  • Structured reports with competitor profiles, feature analysis, and gap identification
  • Source citations on claims throughout the report
  • Confidence scoring that flags where the evidence is thin
  • Interactive chat over the generated research
  • Report updates when follow-up questions surface new angles worth exploring

The output is meant to accelerate research, not replace human judgment.

Three Design Focus Areas

These were the engineering priorities that shaped the product:

1. Autonomous research pipeline

GapFoundry runs a sequential workflow: web search, structured data extraction, and LLM analysis, coordinated as a multi-stage agent rather than a one-shot prompt.

2. Interactive knowledge base

The generated report is not the end of the session. Users can chat with it, ask follow-up questions, and dynamically update the final output when new angles worth exploring come up.

3. Data integrity

Market research fails if you cannot trust the output. GapFoundry attaches confidence scoring and source citations to claims throughout the report, so users know what is well-supported and what needs manual verification.

Tech Stack

FastAPI, Next.js, PostgreSQL, pgvector, LangGraph, LangChain, Python, and TypeScript.

Agent tools: Tavily, DuckDuckGo, and SearXNG for hybrid web-search orchestration.

Challenges I Worked Through

These are the kinds of problems that show up when you build agentic AI for real research workflows:

  • Turning messy web data into structured competitor analysis with named sources
  • Reliable research across different query types (broad market vs specific competitor)
  • Chat that updates reports, not just retrieves from them
  • Trust and verification in a domain where LLM hallucination is especially costly
  • Long-running agent jobs that need to stay resilient when individual steps fail

I am happy to discuss these in depth in an interview context. This post stays focused on outcomes rather than implementation specifics.

Limitations and Honest Scope

GapFoundry is a strong research accelerator, not an oracle.

  • Search quality is the ceiling. If the web does not have good data on a niche market, the report reflects that gap.
  • Human review is still required. Confidence scores help, but important decisions should be verified against primary sources.
  • Not publicly deployed yet. It is a portfolio project demonstrating agentic AI engineering, available for demo and walkthrough.

Being clear about limitations is part of what makes this useful for hiring evaluation.

What This Demonstrates for Hiring Teams

If you are looking for an AI or backend engineer who can build internal tools and agent systems, GapFoundry shows:

  • Multi-stage agentic AI beyond basic chatbot patterns
  • Tool use and web search integration in a production-shaped workflow
  • RAG and retrieval over generated documents with conversational access
  • Structured LLM output with citations and confidence scoring
  • Backend engineering with FastAPI, PostgreSQL, and async job handling
  • Full-stack delivery with a Next.js frontend for reports and chat

I am currently looking for my next challenge in the AI and backend space. If your team is scaling AI engineering capabilities or building complex LLM applications, I would love to connect.

Related Work and Connect

Check out the demo above. I would love to hear your thoughts or talk about what your team is building.

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