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Beyond Generic AI: How We Built an Agentic 'Contractor' Army to Unlock Home Improvement Insights


Imagine needing critical insights from a notoriously hard-to-reach audience – professionals who are always on the go, with limited time for traditional research. This was the exact challenge faced by a major Home Improvement Retailer, one of our enterprise clients.

Portrait of AI-generated contractor with icons for ideas, tools, and communication

Use Case

Portrait of AI-generated contractor with icons for ideas, tools, and communication

Use Case

Portrait of AI-generated contractor with icons for ideas, tools, and communication

Use Case

Portrait of AI-generated contractor with icons for ideas, tools, and communication

Use Case

Portrait of AI-generated contractor with icons for ideas, tools, and communication

Use Case

They needed to test new product concepts and messaging with a diverse group of tradespeople: plumbers, electricians, general contractors, builders, and specialist renovators. Traditional methods like quantitative surveys or focus groups were falling short, failing to generate the depth of participation or the rich, nuanced feedback needed to move forward with confidence. How could they truly understand what motivates these professionals, their decision-making processes, and their reactions to new ideas at scale?

Why Generic LLMs Miss the Mark for Deep Consumer Understanding

It might seem tempting to simply ask a generic LLM like ChatGPT: "What do plumbers think about this new tool?" While seemingly straightforward, this approach has significant limitations for obtaining actionable business intelligence:

  1. Generic Outputs: LLMs trained on internet data often produce vague, surface-level answers. They reflect what people say about a group, not what those people actually think or feel.

  2. Missing the "Edges": Most models can’t capture the full range of opinions, especially from niche segments, early adopters, or skeptics that often shape market direction.

  3. Inconsistent Personas: Without a strong framework, AI agents tend to drift. They don’t hold a consistent point of view, making it hard to trust them in strategic scenarios.

Agentic 'Contractor' Populations Powered by People Model™

Illustration of diverse AI-generated contractor personas, each representing different trades and backgrounds, connected through a digital network symbolizing Vurvey's People Model™

To address the Home Improvement Retailer's challenge, we used our proprietary People Model™ to build a bespoke Agentic Population for the brand to engage with 24/7. This included distinct AI 'contractor' agents, each designed to mirror the life experience, diversity, and personality traits of tradespeople in the real-world.

Each agent was a sophisticated representation, grounded in:

  • Foundational stories: Each agent was trained on first-party data from our proprietary video survey interviews with real contractors

  • Unique characteristics: Each agent was personalized using over 18 trillion different personality facet combinations, enabling researchers to uncover decision drivers, motivations, and trade-offs across the population.

  • Rigorous scenario testing: Agents were tested across 2,000+ scenarios to simulate a full range of reactions and conditions reflective of the real-world.

Graphical representation showcasing the uniqueness of AI agent responses, highlighting less than 1% duplication, and emphasizing the authenticity of insights generated

Proving Originality, Accuracy, and Trust

As evaluations serve as a true test of what AI models can generate, we measured the outputs of agents to confirm that their responses genuinely mirror human thought and behavior. The results were transformative:

  1. Unprecedented Originality: Beyond the AI Echo Chamber (<1% Duplicate Responses)
    Our Agentic Population produced a natural, human-like spread of responses, from widely held opinions to unique perspectives. With less than 1% duplication, the results demonstrated true diversity and non-repetitive thinking across agents.

Chart comparing response diversity between Vurvey and ChatGPT models
  1. Remarkable Concurrence: A 93% Agreement with Real Human Responses
    We compared agent responses to real tradesperson feedback and saw a 93% agreement. The agents did more than sound realistic. They reflected authentic sentiment, reasoning, and tone, including subtle traits like humor and relatability.


    Comparison chart illustrating a 93% agreement rate between AI agent feedback and actual tradesperson responses, demonstrating the reliability of the agentic models
  1. Steadfast Adherence: 95% Persona Consistency
    Each agent maintained its assigned profile across thousands of scenarios. From early adopters to late majority segments, the population remained consistent in behavior, decision-making, and adoption patterns. This level of consistency is critical for reliable strategic insight.

    Visual depiction of AI agents maintaining consistent personas across various scenarios, emphasizing the 95% consistency rate in behavior and decision-making patterns

The Future of Testing

Using agents powered by our People Model, enterprise teams now have unlimited possibilities to generate new insights with speed, cost effectively, and safely without the risk of exposing their intellectual property (IP.) This practice can accelerate product innovation, claims testing, and more.If you are ready to move beyond asking ChatGPT or using synthetic users, let’s talk. We’d love to bring your segment to life.

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