Lakmoos Answers ESOMAR’s 20 Questions for AI-Based Research Buyers

If you're evaluating AI-based research providers, ESOMAR’s 20 Questions framework is the global standard for due diligence.
At Lakmoos, we don’t just comply, we welcome transparency. This article offers plain-language answers to all 20 ESOMAR questions, tailored to our synthetic AI panel solution.

You’ll learn how our hybrid models work, how we simulate behavior instead of surveying humans, and what guardrails are in place to ensure accuracy, privacy, and ethical use.

🧠 Want the technical version? Download our full 25-page whitepaper here (PDF).
Or keep scrolling for the straightforward, no-fluff explanation.

🧾 What is this framework?

The ESOMAR 20 Questions to Help Buyers of AI-Based Services is a global checklist to evaluate AI-based research. It ensures providers are transparent about:

• Data sources

• Model design

• Sampling approach

• Bias and validation methods

• Use cases and risks

This blog post follows ESOMAR’s exact structure, so you can evaluate Lakmoos AI with confidence.

🔍 Lakmoos AI: Answers to ESOMAR’s 20 Questions

1. What is the purpose of your AI application or service?

Lakmoos simulates human research respondents using AI-powered synthetic panels. Our purpose is to replace traditional surveys and interviews where human data is too slow, expensive, unavailable, or biased so organizations can ask more questions, more often, with full control.

2. What type of AI techniques do you use, and how do they work?

We use a hybrid AI architecture combining:

• Neuro-symbolic AI for behavioral simulation and reasoning

• Natural Language Processing (NLP) for open-ended responses

• Classifiers for segmentation and persona matching

• Expert systems to interpret and explain model outputs

💡 These components work together to generate realistic responses from simulated personas.
Read more on our Science page.

3. What types of data does your system use and where is it from?

Our system uses:

• Behavioral data: aggregated and anonymized (e.g. mobility, ecommerce)

• Historical research data: from previous projects, academic datasets

• Open-source corpora: to train reasoning and language modules

• Client-provided inputs: to fine-tune specific brand or audience contexts

We do not collect or process personal user data. All training inputs are reviewed for compliance and representativeness.

💡 Learn more about data from our Youtube video explainer!

4. How is the data collected, cleaned, and validated?

Behavioral and research datasets are:

• Acquired from GDPR-compliant, legitimate sources

• Cleaned using automated de-duplication, anomaly detection, and category mapping

• Validated via replication studies, benchmark tests, and human-AI comparisons

We disclose provenance metadata and model lineage upon request.

5. How do you ensure data quality and relevance over time?

We run:

• Ongoing benchmark tests against recent human panel data

• Drift detection to track when model responses deviate

• Quarterly updates and re-training for domain-specific modules

This ensures relevance as market behavior evolves.

6. How do you ensure transparency in your system?

Every Lakmoos project includes:

• A full model description

• Prompt inputs and configurations

• Sampling logic

• Output summaries with explanation logic (for AI agents)

We encourage auditability and version tracking.

7. Can users understand and interpret the AI-generated results?

Yes. Clients receive both:

• Structured outputs (quant, tables, scored attributes)

• Interpreted summaries via our AI agents
  Our platform avoids “black box” outputs. You can trace the logic used to reach conclusions.

8. How do you address and minimize bias in your AI system?

We apply bias control at three levels:

• Training data audits to avoid structural bias

• Synthetic population balancing for demographic and attitudinal realism

• Prompt sensitivity testing to reduce framing effects

We also provide bias impact disclosures for sensitive studies.

9. How is your AI system evaluated for accuracy and reliability?

We benchmark all outputs against:

• Human panel responses (where available)

• Previously validated survey studies

• Industry-specific KPIs (e.g., concept rankings, price thresholds)

Most Lakmoos studies replicate human trends within 5–10% variance, with higher consistency on behavioral questions.

10. How are errors or inaccuracies detected and addressed?

Our QA system includes:

• Auto-flagging inconsistent responses

• Red-teaming of prompt chains

• Comparison with human gold standard responses

Clients can report anomalies, and we rerun or revise simulations as needed.

11. What limitations or risks should users be aware of?

Lakmoos AI panels are not suitable for:

• Personal stories or lived experience

• High-emotion qualitative depth

• Legal or compliance substitution without validation

They're best used for fast, scalable, behavioral simulation.

12. How do you support fairness and inclusivity in your system?

We simulate audiences across age, gender, income, geography, and behavior types.
You can explicitly include:

• Underrepresented personas

• Low-incidence use cases

• Multicultural segments

We avoid tokenism by weighting responses realistically, not just checking diversity boxes.

13. How do you comply with legal and ethical standards, including GDPR?

We never process personal data or store user input.
We are:

• Fully GDPR-compliant

• Aligned with ICC/ESOMAR Code of Conduct

• Non-invasive by design: No scraping, no tracking, no profiling

Our models simulate populations, not individuals.

Keep in mind, we have supplied insights to 10+ teams in EU-based banks. One of them was Raiffeisenbank and we explain how we handled compliance there in our case study.

14. How do you explain the outputs and decisions of your AI system?

We use explainable AI agents to narrate decisions:

• Why a segment responded a certain way

• Which features drove a preference

• What confidence level each insight carries

Clients can access summary reports, transcripts, or agent interactions.

15. How do you handle feedback and model updates?

Clients can:

• Flag edge cases or poor performance

• Request reconfiguration or constraints

• Submit corrections to simulated logic

We update models regularly and offer feedback-to-training mechanisms for enterprise clients.

16. What human oversight is involved in the process?

All simulations are client-initiated and human-reviewed.
Lakmoos analysts oversee:

• Simulation design

• Output integrity

• Insight generation

• Data safety

17. Can users customize the AI system for their needs?

Yes. You can:

• Define custom personas

• Integrate your own datasets

• Set brand or tone constraints

• Use specific behavioral triggers or filters

Enterprise clients can co-develop domain-specific modules.

18. What are the commercial terms and limitations of use?

Lakmoos licenses AI panel access per:

• Subscription (always-on access)

• Custom integration (API or white-label)

You own all outputs generated from your questions. We retain model IP, not your data.

19. What security measures do you use to protect data?

All data and simulations are:

• Encrypted in transit and at rest

• Isolated by client and project

• Non-personal and non-identifiable

  
  

  We follow ISO 27001-aligned internal security practices.

20. Where can I find more information or documentation?

📄 Download our full 20-page whitepaper
📫 Contact us at hello@lakmoos.com
🌐 Explore more at lakmoos.com/science

Lakmoos fully aligns with the ESOMAR 20 Questions framework for AI-based research.
We believe AI panels should be:

• Transparent

• Ethical

• Explainable

• Useful today, not just in theory

If you’re exploring AI research, don’t skip this standard. It separates buzzwords from substance and we’re happy to show you the difference.