Field & Signal

The description states that Field & Signal is a platform that turns a business question into a complete research engagement led by six clearly disclosed AI specialists. These specialists perform distinct roles including planning, public evidence investigation, methodology design, survey/interview conduct, integration of findings, and final brief preparation.

Each specialist has a defined role:

1. John plans the research.
2. Maya investigates public evidence.
3. Aisha designs the methodology.
4. Daniel conducts interviews.
5. Sofia integrates the findings.
6. Marcus prepares the recommendation.

The system uses GPT-5.6 as its core AI engine, with support from tools like ChatGPT, Codex, Next.js, Supabase, and Vercel. It is described as an application that keeps client approval, participant consent, evidence links, and research limitations visible throughout the process.

Inference This is a self-contained workflow designed to automate parts of market research using AI agents, but it is not evidenced to be in production or used by clients beyond the author’s own testing.

The project claims to offer an AI-native market research agency that can be engaged as easily as describing a business decision. It positions itself as a way for small businesses to access full research workflows without needing to assemble or hire traditional specialists.

It also emphasizes that the system avoids “multi-agent theatre” by ensuring each agent performs distinct, real work and passes persistent artefacts between stages.

Inference The positioning is that of a self-service, AI-powered research-as-a-service platform targeting small businesses. However, no evidence exists of market testing or adoption.

The author states that the target customer is “small businesses” making decisions about customers, pricing, products, and expansion. These businesses are said to find traditional market research expensive and slow.

Inference The ICP is small businesses seeking affordable, fast, and accessible market research. However, no evidence of actual customers or usage exists.

There is no evidence in the description of a business model or pricing structure. The author does not describe how Field & Signal would generate revenue, whether through subscriptions, per-project fees, or other mechanisms.

Inference The business model is not described. It remains unknown if this is intended to be a commercial product or a prototype.

The system is built using Next.js, TypeScript, Supabase, the OpenAI Responses API, GPT-5.6, and Vercel. The author used ChatGPT for ideation and Codex for implementation, including interface design, database integration, and workflow validation.

Key technical features include:

• Structured schemas to validate model outputs.
• Progress indicators showing which specialist is working.
• Approval controls for key actions.
• Evidence links and research limitations visibility.

Inference The system is a prototype built on modern web and AI stacks. It includes UI/UX design elements for transparency and control. However, no evidence of deployment or scalability beyond the hackathon.

There is no evidence of traction, revenue, customer adoption, or product maturity beyond the author’s own development and testing. The project was submitted to a hackathon and has not been independently verified or launched.

Inference This is an early-stage prototype. There is no indication that it has moved beyond the development phase.

The author does not describe any competitive landscape or existing players in the market research space. The project makes no mention of competitors, pricing, or differentiation from traditional market research firms or AI tools.

Inference The competitive context is unknown. It is unclear how this product would compare to existing solutions in the market.

1. Unproven commercial viability: The project has not been validated with real customers or revenue.
2. No evidence of scalability: The system was built for a hackathon and lacks evidence of production deployment.
3. Unclear monetization strategy: No business model is described.
4. Lack of external validation: The product is self-reported, unverified, and lacks third-party feedback or testing.
5. Potential over-reliance on AI: There is no indication of how the system handles edge cases or errors beyond those addressed in development.

Inference The project is at a very early stage and carries significant risk due to lack of real-world validation.

1. What specific market research tasks are you planning to automate, and how do you validate the quality of AI-generated outputs?
2. Have you tested this with any actual small business clients or stakeholders?
3. How does the system handle errors in AI responses or incomplete data?
4. What is your plan for scaling beyond the current prototype?
5. Are there any legal or ethical considerations around conducting surveys and interviews using AI agents?
6. How do you intend to monetize this product, and what pricing model are you considering?