Deepfield

What the company appears to be: Deepfield is described as an AI-powered operating system for market research, opportunity discovery, and product strategy. The author states it helps founders explore markets visually through an interactive graph, evaluate opportunities using a scoring model, and simulate paths to build products.

What changed: This is a self-reported project submitted by one individual (dikshit vig) for the OpenAI 2026 hackathon. It represents a single developer's attempt to build a tool that combines AI with structured market exploration and opportunity planning.

The single most important open question: Is there any evidence of traction, revenue, or customer adoption beyond the author’s own development work?

• The description states Deepfield is an "AI operating system for market research, opportunity discovery, and product strategy."
• Users can explore markets such as Travel, Healthcare, or Artificial Intelligence via an interactive graph.
• It allows expanding nodes to dive deeper into topics like companies, users, pain points, solutions, signals, and opportunities.
• The tool supports comparing different founder paths (Bootstrapped, VC Scale, Solo Builder).
• It includes an "Opportunity Simulator" that generates structured build plans including evidence, customers, pricing, risks, assumptions, MVP steps, and a build stack.
• The system exports opportunity plans as Markdown.
• It integrates with multiple AI providers and allows switching between model-quality levels.

Inference: Based on the description, Deepfield appears to be a developer-built prototype aimed at helping founders navigate markets using AI-generated insights. However, no evidence of actual usage or adoption exists beyond the author’s own development process.

• The author claims Deepfield is inspired by “a map for markets” — a way to explore industries through users, companies, pain points, technologies, signals, and underserved opportunities.
• It positions itself as a tool that helps founders focus their efforts rather than generate random ideas.
• The product aims to move beyond simple chatbots to provide structured workflows for market exploration and decision-making.

Inference: The positioning evolves from a general-purpose AI assistant to a specialized framework for strategic market navigation, tailored specifically for startup founders. However, this is a self-described evolution without external validation or evidence of how it differs from existing tools.

• The description states that Deepfield targets founders who struggle not with lack of ideas but with knowing where to focus.
• It caters to those looking to understand markets deeply before building products.
• The tool supports three founder paths: Bootstrapped, VC Scale, and Solo Builder.

Inference: The intended customer segment is early-stage founders or product strategists who need structured support in identifying viable market opportunities. However, no evidence of actual customers or user feedback exists beyond the author’s own testing.

• No pricing information, business model, or monetization strategy is provided.
• The project was submitted as a hackathon entry and deployed on Vercel; no indication of paid services or subscriptions.
• The system uses server-side API key handling for hosted deployment, suggesting potential future commercialization.

Inference: There is no evidence of any revenue-generating mechanism or pricing structure. The tool may be intended for future monetization but currently lacks any indication of how it would generate value or income.

• Built with vanilla JavaScript ES modules and Node.js backend.
• Uses AI providers like GPT-5.6 and Codex for implementation assistance.
• Implements serverless architecture via Vercel.
• Includes features such as in-memory caching, request cancellation, and fallback mechanisms for AI responses.
• Supports structured JSON validation and normalization of opportunity scores.
• The frontend is modularized into feature, service, UI, core, and data layers.

Inference: The technical stack suggests a lightweight, developer-centric prototype built with modern web technologies. While the architecture shows some sophistication in handling AI integration and performance concerns, there is no evidence of production-grade scalability or robustness.

• The project was submitted to the OpenAI 2026 hackathon.
• It has a single developer (dikshit vig) as the team member.
• The author mentions iterating based on real testing feedback, but no specific metrics or user data are shared.
• Deployment is live on Vercel, indicating some level of completion.

Inference: There is no evidence of traction, adoption, or measurable user engagement. The project appears to be a personal development effort rather than a product with market validation or growth signals.

• No mention of competitors or competitive landscape.
• The author does not reference similar tools or platforms in the market research or opportunity discovery space.
• The concept overlaps with AI-powered market intelligence and strategic planning tools, but no direct comparison is made.

Inference: Without any evidence of competitor analysis or positioning within existing markets, it's unclear how Deepfield would differentiate itself from other tools in this domain. This is a self-reported claim without external verification.

• The project has only one developer and no team.
• No revenue, customer base, or traction data are available.
• The scoring model is described but not validated or tested with real-world data.
• The tool relies heavily on AI providers like GPT-5.6, which may introduce dependency risks.
• There is no indication of long-term sustainability or scalability beyond the hackathon prototype.

Inference: The biggest risk is that this remains a personal project without any commercial viability or market traction. The lack of team, funding, and user feedback raises concerns about its potential for growth or impact.

1. What specific problem are you solving, and how does Deepfield address it differently from existing tools?
2. Have you tested the scoring algorithm with real users or data? How reliable is it?
3. Are there any plans to monetize this tool? If so, what is your business model?
4. What kind of feedback have you received from potential users during development?
5. Is there a plan for scaling beyond the current prototype, and how will you build out the team?