Automated Survey Generation for Market Leaders

The Challenge

Manual survey design is fragmented. Researchers often begin with a mix of briefs, deck slides, email notes, and evolving requirements. Turning that into an executable questionnaire usually means switching across tools, reusing old templates manually, and coordinating with operations later in the process.

Every edit can create downstream rework. A change in section order, question wording, or response options can affect routing, numbering, translations, and export formatting. In traditional workflows, those dependencies are handled manually, which slows turnaround and increases QA burden.

Historical knowledge is hard to reuse consistently. Organisations accumulate valuable question banks and prior studies over time, but that knowledge is often static. Teams still need to search old assets manually, interpret what is reusable, and rewrite large portions by hand.

What the platform needed to achieve

• Convert raw briefs and uploaded files into structured study context.
• Select the right sections, information blocks, and categories for each survey type.
• Generate screener and main survey questions with historical-question grounding.
• Preserve routing integrity when questions are edited, regenerated, or reordered.
• Support bilingual outputs and export questionnaire assets for downstream deployment.

The Solution WeBuildTech Delivered

WeBuildTech designed a modular AI questionnaire engine with production-grade controls around it. Instead of treating survey generation as a single LLM prompt, the backend breaks the process into distinct steps: intake, context extraction, conversational clarification, planner selection, question generation, logic generation, structure remapping, persistence, and export. This makes the system more auditable, easier to refine, and safer to operate at scale.

Architecture overview

• FastAPI-based API layer with typed request and response models, JWT protection, and controlled CORS.
• Redis-backed session memory for conversational clarification and multi-turn survey setup.
• MongoDB persistence for project documents, planner selections, generated questions, and export artefacts.
• Vertex AI Gemini orchestration for context extraction, selection, personalisation, question generation, and logic creation.
• OpenSearch vector retrieval with embeddings to surface similar historical questions during generation.
• DOCX export and JSON-builder layers to bridge human review and downstream survey-platform deployment.

Capability stack

What Made This Solution Different

Business Value

Faster brief-to-questionnaire motion. Study intake, planning, question generation, logic creation, and export are connected in one backend flow rather than scattered across manual steps.

• Higher consistency across deliverables — context, planner structure, questions, and exports are all tied together.
• Better reuse of institutional knowledge through vector retrieval instead of static reference material.
• Lower downstream operational friction — when questions are updated or reordered, logic can be remapped without restarting the full questionnaire build.
• Cleaner research-to-ops handoff — the same system generates both human-readable documentation and system-ready outputs.

Technology Stack