A New Era of Scalable, Always-On Consumer Research

Imagine running a survey, and getting answers instantly, from thousands of people who match your exact audience. Now imagine those people aren’t real… but their responses are grounded in behavioral data, decision logic, and demographic realism.
That’s the power of synthetic respondents.

Synthetic respondents are not fake. They are AI-generated agents trained to simulate real consumer behavior. Built with rules, memory, and demographic diversity, these agents act like real people, without the cost, delay, or bias of traditional sampling.

👉 Too many buzzwords? Our Glossary will help you navigate the new industry of AI panels, synthetic audiences and silicon samples.

Why Are Synthetic Respondents Gaining Traction?

In industries like banking, energy, telecom, and media, research teams face pressure to deliver insights faster, cheaper, and more frequently. But traditional methods can’t keep up. Recruitment takes time. Surveys get skipped. Ethnographies are expensive.

That’s why more organizations are turning to AI-powered research tools that simulate customer thinking at scale.

Synthetic respondents solve key bottlenecks:

• ✅ Speed: Instant responses without recruitment delays

• ✅ Scale: Simulate thousands of personas or niche audiences

• ✅ Continuity: Run tests 24/7, across product, design, or comms

• ✅ Safety: No need for sensitive PII or invasive tracking

• ✅ Explorability: Re-run scenarios, tweak assumptions, simulate “what ifs”

But Are They Just GPT Wrappers?

Some tools on the market simply wrap a chatbot like ChatGPT in a survey-like UI. These “GPT wrappers” are fast and fun, but often lack rigor. They generate plausible-sounding text, not behavior grounded in theory, data, or replicable logic.

True synthetic respondents (like those built with multi-agent simulation) go further. They simulate trade-offs, constraints, and decisions, not just language.

🧠 If the goal is to understand why consumers choose one thing over another, you need more than text generation, you need behavior modeling.

How Do Synthetic Respondents Work?

They typically consist of:

• Structured profiles: Demographics, preferences, and prior behavior

• Logic models: Rules that simulate how decisions are made (e.g., price sensitivity, brand trust)

• Memory: So agents don’t “reset” between questions

• Variability: Each agent is unique, not just a cloned archetype

• Auditable output: Responses that can be tracked, tested, and explained

Unlike humans, synthetic respondents are always available. But when designed well, their responses still reflect the diversity, nuance, and complexity of real consumers.

TIP: Read more about the technology beyond Lakmoos AI on our Science page.

What Can You Do With Them?

Synthetic respondents are used to:

• 🧪 Test products and pricing before launch

• 🧠 Explore messaging strategies across different segments

• 💬 Compare choices to predict churn, adoption, or brand switch

• ⚡ Run fast, low-risk simulations before investing in full research

• 📈 Benchmark insights alongside traditional panels for validation

For many organizations, synthetic panels become a first layer of insight, used to prioritize deeper qualitative or quantitative research, not replace it.

👉 Check out how A-players like Volkswagen Group or Raiffeisenbank have used synthetic audiences to improve their customer experience.

Choosing the Right Partner

The synthetic respondent space is growing fast. Tools like Synthetic Users, Simulatrex, Simile, Bluepill, and Aaru each offer variations on the concept. But not all are created equal.

Here’s what to look for:

• 🔍 Is it LLM-only (language generation) or does it include behavior modeling?

• 🧾 Are the responses auditable and explainable, or just fluent text?

• 🧩 Can the model simulate trade-offs, or does it just answer in isolation?

• 🧠 Is there demographic and logical variation between agents?

  
  

At Lakmoos, we specialize in enterprise-grade synthetic panels that go beyond chat. Our agents simulate how real people choose, hesitate, switch, or stay, depending on context. We work with leaders in regulated industries, where “plausible” isn’t enough, and decision-grade trust is essential.

To promote transparency in AI, we answered 20 questions for AI buyers as outlined by ESOMAR. 👉 Read our answers in this article.