AI-Driven Market Research Simulations for Actionable Business Insights
Problem Statement
Customer sought faster, scalable insights from interview transcripts and real data to answer product choice, segmentation, and market expansion questions.
Client Info
Mid-size Market Research Firm serving global consumer brands.(ex: Google, Coke, Amazon)
Outcomes
Reduced research cycle time by 60%.
Enabled simulation of thousands of realistic respondent answers in minutes.
Increased confidence in market insights and client decision-making.
How did BeautifulCode do it?
Challenges Encountered During Implementation
Profile Realism: Ensuring AI-generated profiles accurately reflected human responses; overcame by grounding in real interview transcripts.
Scalability: Handling large volumes of transcripts; overcame with optimized cloud-native pipelines.
Insight Trust: Building customer trust in AI responses; solved with transparent methodology and validation loops.
Solution
Developed an AI system that created profiles from real data and simulated responses, accelerating research cycles and improving decision confidence.
Overview of Delivery Methodology
Step 1 – Discovery: Understood analyst workflows and bottlenecks in research reporting.
Step 2 – Architecture: Designed modular system using RAG with LlamaIndex, Celery, and Qdrant.
Step 3 – Implementation: Built ingestion, retrieval, and report generation pipelines with streaming SSE.
Step 4 – UX Design: Delivered interactive editor with verification loops and inline diff review.
Step 5 – Iteration: Piloted with analysts, collected feedback, and refined workflows.
Technologies Used
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