Faster Insights
Drastically reduce time-to-insight from weeks to hours by automating manual analysis.
Why Your Team Needs Gen AI in Market Research
Move from raw data to trusted insights in minutes. Lyzr automates research workflows with governance and citations, turning qualitative and quantitative data into clear actions.
Get insights faster
Automate qual and quant
Secure and compliant
Gen AI for Research:
Velocity Meets Total Rigor
Automate summarization, theme extraction, and segmentation. Synthesize VoC feedback and test concepts, all while maintaining complete human oversight and control over all outputs.
Rigor & Consistency
Ensure structured, traceable themes across all your research projects for fewer misses.
Analyze Everything
Effortlessly handle large volumes of data from surveys, interviews, and customer product reviews.
Maintain Control
Uphold compliance with role-based access, audit trails, and human-in-the-loop reviews.
Align Stakeholders
Use a central research repository to align teams and democratize access to insights.
Workflows
Workflows
From discovery to reporting, Lyzr supports your entire research lifecycle, automating both qualitative and quantitative analysis for faster, deeper understanding.
Qualitative Synthesis
Instantly find themes from interviews, focus groups, and open-ended survey data.
Quantitative Analysis
Interpret survey results, uncover key drivers, and identify customer segments automatically.
Concept & VoC Testing
Summarize concept feedback, track competitor intelligence, and monitor VoC channels at scale.
Tired of research backlogs and scattered data? Get clear, actionable insights your team can trust, faster.
The True Benefits of Gen AI
in Market Research
Drastically Cut Timelines
Save hundreds of project hours by eliminating tedious manual analysis and reporting cycles.
Improve Research Consistency
Apply standardized models for theme extraction, ensuring repeatable and reliable outputs.
Increase Decision Confidence
Back every insight with clear evidence trails and traceable data sources for stakeholders.
Boost Cost Efficiency
Reduce dependency on external vendors for routine analysis and data synthesis tasks.
End-to-End Platform
Capabilities
Our platform handles everything from multi-source data ingestion to governed, collaborative insight generation, all in one secure environment.
Data Ingestion
Connect surveys, transcripts, reviews, and VoC data from your favorite sources and tools.
Automated Theme Extraction
Automatically code qualitative data, cluster themes, and analyze all recurring patterns.
Actionable Summarization
Generate executive summaries with key quotes and direct links back to the source evidence.
Central Research Q&A
Ask questions in natural language and get grounded answers directly from your entire research repository.
Governance Controls
Manage PII, user access, and audit trails with human-in-the-loop reviews.
Gen AI Tools vs Traditional
Research Methods
| Feature | Generic AI Tools | Traditional Tools | Lyzr |
|---|---|---|---|
| Multi-source research | Limited inputs | Manual data uploads | Automated multi-source sync |
| Qualitative theme coding | Inconsistent outputs | Slow and inconsistent | Consistent, structured |
| Open-end analysis | Lacks full context | Time-intensive | Deep context-aware analysis |
| Summaries | No citations or proof | Requires manual reporting | Evidence-linked summaries |
| PII controls | Data privacy risk | Process-dependent | Built-in PII redaction |
| Human-in-the-loop review | No integrated flow | Ad-hoc | Integrated review workflows |
| Customer segmentation | Surface level | Requires statisticians | AI-assisted segmentation aid |
| Competitor intelligence | Often unreliable | Manual collection | Automated intelligence briefs |
| Research repository | None available | Scattered file storage | Central, queryable repository |
| Concept testing | Basic feedback | Slow turnaround | Automated feedback analysis |
Why Lyzr for Gen AI in
Market Research?
Trustworthy Outputs
Mitigate hallucinations with data grounding and evidence trails.
Built for Teams
A shared repository, standardized briefs, and user roles drive true team collaboration.
Secure by Default
Protect sensitive data with robust privacy controls, PII redaction, and access management.
Fast to Deploy
Get started in days, not months, using pre-built templates for common research workflows.
Trusted by The Best
Insight-Driven Teams
Leading consumer brands, retailers, and SaaS companies rely on Lyzr to power their market research, turning complex data into confident business decisions at scale.
Lyzr has been a game-changer. We've cleared our research backlog by automating synthesis from interviews and open-ends. Our briefs are now faster and more consistent, and the built-in governance gives our stakeholders the confidence to act on the insights we deliver.
Director · Head of Consumer Insights
Zero
Data exfiltration incidents
Implementing Gen AI for Market
Research
Define Workflows
We map your key research use cases and define all success criteria.
Connect Your Data
Securely ingest data from surveys, CRMs, and other sources with permissions.
Configure Rules
Set up governance, PII handling, user access controls, and human review gates.
Launch and Scale
Run a pilot project, gather team feedback, and scale across your organization.
Frequently Asked Questions
About Gen AI Market Research
What is Gen AI in market research used for day to day?
It automates repetitive tasks like transcribing interviews, coding qualitative responses, summarizing key themes from surveys, and drafting initial insight reports. This frees up researchers to focus on strategic analysis and storytelling, with governance controls ensuring all outputs are reliable.
How does Gen AI in market research handle qualitative interviews?
Our platform ingests audio or text transcripts, automatically identifying key themes, sentiment, and direct quotes. It clusters related concepts and produces structured summaries with evidence links, ensuring you never lose the original context while dramatically speeding up analysis.
Can Gen AI in market research analyze open-ended survey data?
Absolutely. It excels at processing thousands of open-ended text responses, categorizing them into coherent themes, and quantifying their frequency. This transforms unstructured feedback into structured data you can use for driver analysis, with human review to validate all categories.
What data sources can be used?
You can connect a wide range of sources, including survey platform exports, interview transcripts, focus group notes, customer reviews, social media comments, and CRM data. Our platform unifies these disparate sources into a single, analyzable research repository.
How do you reduce hallucinations in AI research outputs?
We use Retrieval-Augmented Generation (RAG), which grounds every AI-generated summary in your specific source documents. All insights include direct citations and links back to the evidence, allowing for easy verification and building trust in the outputs.
Does it support customer segmentation analysis?
Yes, by structuring qualitative data and integrating it with quantitative metrics, our AI can help identify distinct customer segments based on their feedback. It can also help pinpoint key drivers behind satisfaction scores from survey data.
How does it fit into existing insight team workflows?
Lyzr is designed to augment, not replace, research professionals. It acts as a powerful assistant, handling the heavy lifting of data processing and synthesis. Teams use it to accelerate their existing workflows, deliver insights faster, and increase their overall strategic capacity.
What privacy, PII, and compliance controls are available?
Security is paramount. Our platform includes automated PII redaction to protect customer data, role-based access controls to manage who sees what, and detailed audit logs. It's built to operate within a secure, compliant environment, meeting enterprise governance requirements.
Can it create concept testing summaries and competitor briefs?
Yes. You can feed it concept feedback from surveys or interviews to get instant summaries of likes, dislikes, and suggestions. For competitor intelligence, it can process news, reviews, and reports to generate concise briefs on market movements, strengths, and weaknesses.
What does implementation look like and how fast is value seen?
Implementation is a straightforward, four-step process: define workflows, connect data, configure governance, and launch. Most teams are able to run their first pilot project and see tangible value in reducing analysis time within two weeks of getting started.
Got a use case in mind?
8 weeks from use case to
8 weeks from use case to
agents running in production.
Platform, people and FDEs, all in. Bring your environment. We’ll co-build and stay until it’s
live.