Azati developed a unique AI and Computer Vision-based solution to help pharmaceutical companies generate assessment reports based on insights from medical practitioners. The solution automates the process of analyzing questionnaires and provides valuable data for enhancing pharmaceutical marketing strategies.
key phrase coverage in doctors’ responses
identification actionable market insights
improved campaign targeting accuracy
The goal of the project was to create an MVP that uses AI and Machine Learning technologies to build assessment reports for pharmaceutical companies. The reports should be based on questionnaires and insights from doctors, helping the companies enhance trust in their products and improve marketing strategies.
Traditional checkbox-based surveys were limiting the depth and authenticity of healthcare professional feedback. Respondents were forced into predefined answer options, leaving critical nuances, hesitations, and real-world clinical opinions uncaptured. This made it difficult for pharma brands to base strategic decisions on research that truly reflected HCP perspectives.
As the volume of open-ended audio responses grew, manual analysis became a bottleneck — slow, costly, and inconsistent across studies. Without an automated way to transcribe, cluster, and score hundreds of spoken answers, extracting actionable insights required significant analyst effort and introduced delays in the research cycle.
Existing commercial survey platforms were built for form-based research and could not support the combination of audio collection, AI-driven follow-up logic, and quantitative insight scoring the client needed. Relying on third-party tools meant accepting critical gaps in functionality, limited data ownership, and no path to the proprietary research product the client envisioned.
The client, launching a pharma market research, needed a technology partner to build the entire platform from scratch, not just code, but architecture decisions, AI strategy, and product thinking together. Specifically:
Azati was selected after winning a formal competitive process. The proposal stood out not just technically, but because the team demonstrated a credible understanding of pharma market research methodology and proposed a concrete, non-generic AI architecture.
The project required more than integration, it needed custom scoring models, clustering algorithms, and LLM-based conversational logic built from first principles. Azati brought ML engineers and AI specialists capable of owning that depth.
Rather than delivering a fixed scope and stepping back, Azati embedded as a continuous development partner from MVP in 2021 through ongoing support, adapting the product as the client's research methodology matured.
Azati structured a unique collaboration involving two PhDs in ML and NLP as intellectual advisors, while the engineering team acted as the implementation force. This ensured algorithms were rigorous and academically grounded, not improvised.
Ready to move beyond checkbox surveys and capture the real voice of your audience with AI?
Let's design your AI research platform| Metric | Before | After |
|---|---|---|
| Survey research cycle | 5–6 weeks (manual analysis) | 2–3 weeks (automated pipeline) |
| Response analysis | Manual, analyst-dependent | AI-powered NLP clustering + scoring |
| Follow-up question logic | Static predefined options | Dynamic AI agent per respondent |
| Report generation | Manual slide/spreadsheet creation | Auto-generated XLS + PPT reports |
| HCP voice capture | Checkbox-based questionnaires | Open audio responses with ASR |
| Platform maturity | No platform existed | Full SaaS from survey design to insights |
The platform is secured with SSL/TLS encryption and deployed on AWS infrastructure with access controls managed through AWS Guard. Environment monitoring is handled via CloudWatch, with a load balancer ensuring availability under variable respondent load. All audio recordings and sensitive HCP response data are stored in Amazon S3 with access policies aligned to the sensitivity of pharmaceutical market research data.
Automated Insight Generation: Reduced manual effort in analyzing medical questionnaires and automatically extracted actionable insights.
Enhanced Marketing Strategies: Insights from doctors allowed pharmaceutical companies to adjust campaigns and improve product positioning.
Improved Trust: Structured reports increased consumer confidence by showing alignment between medical recommendations and marketing claims.
Faster Decision-Making: Automated processing and visualization accelerated strategic decision-making for marketing teams.
Domain-Specific Accuracy: Integration of domain-specific NLP and speech-to-text ensured precise interpretation of medical feedback.
The choice of incremental modernization over a complete rewrite ensured:
Replacing checkbox surveys with open audio responses processed by an AI agent fundamentally changed data quality, HCPs could express nuanced opinions, making insights more authentic and strategically valuable.
Custom scoring models and clustering algorithms mean the client owns a defensible technical asset, one that isn't replicable by switching providers.
Two PhD advisors in ML and NLP ensured the analytical methodology met research-grade standards, critical in pharma, where insight credibility directly drives strategic decisions.
From a founder with an idea to a fully operational SaaS, survey design, AI data collection, post-processing portal, and automated report generation, built and evolved over four years.
A cross-functional team, backed by two PhD advisors, took the platform from zero to a fully operational AI-powered pharma research product over four years of continuous delivery.
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