Intelligent Survey-as-a-Service

ISaaS: AI-powered, multilingual survey platform for MoSPI. Automates design, omni-channel collection, real-time validation via paradata, and instant analytics. Secure, scalable, privacy-by-design.

Comment 1

Here is a draft for your hackathon submission. I have written this assuming you used Generative AI (LLMs) to generate the questions, as that is the standard approach for "automating" content creation today.

You can copy and paste these sections, but please replace the bracketed text [like this] with your specific technology stack (e.g., React, Python, AWS, OpenAI, etc.).

Inspiration We’ve all been there: you need to collect feedback for a project, a club event, or product research, but you stare at a blank screen wondering what questions to ask. Writing good survey questions that aren't biased or confusing is actually a difficult skill. We realized that while there are plenty of tools to host forms, there weren't enough tools to help you design them. We wanted to bridge the gap between having an idea ("I want to know why customers like our coffee") and having a deployable, professional survey instantly.

What it does Intelligent Survey-as-a-Service is an AI-powered platform that transforms a simple text prompt into a fully functional survey in seconds.

Prompt-to-Form: The user simply types a goal (e.g., "Create a feedback form for a college hackathon participant").

Smart Generation: The system uses AI to generate relevant, unbiased, and chemically diverse questions (Multiple choice, text, ratings).

Instant Deployment: It automatically renders the UI for the form and generates a unique shareable link.

Data Collection: It captures user responses in real-time and stores them securely.

How we built it We built the frontend using [Frontend Tech, e.g., React/Next.js/Flutter] for a responsive and clean user interface.

The Brains: We utilized [LLM Tech, e.g., OpenAI GPT-4 / Gemini / Claude / AWS Bedrock] to handle the logic. We engineered specific prompts to ensure the AI outputs the questions in a strict JSON format.

The Backend: We used [Backend Tech, e.g., Python Flask / FastAPI / Node.js] to handle API requests and parse the AI's output.

Database: For storing the generated forms and the incoming responses, we used [Database, e.g., MongoDB / PostgreSQL / Firebase].

Hosting: The application is deployed on [Cloud Provider, e.g., Vercel / AWS / Heroku].

Challenges we ran into Structured Output: The biggest challenge was getting the AI to consistently output valid JSON data that our frontend could render. Early versions would often break because the AI added conversational text (like "Here is your survey") mixed with the code. We solved this by using [mention a fix, e.g., strict prompt engineering / Zod schema validation / instructor library].

State Management: Handling the dynamic nature of the form—where the number of questions changes every time—was tricky on the frontend.

Context Window: Ensuring the AI understood the specific context of niche topics required fine-tuning our system prompts.

Accomplishments that we're proud of Zero-to-One Flow: We successfully built a pipeline where a user inputs a single sentence and gets a working URL in under 30 seconds.

Dynamic Rendering: We are proud of our dynamic form component that can render any type of question (rating, dropdown, text) on the fly without hardcoding.

User Interface: We managed to keep the design clean and intuitive, focusing on the user experience rather than just the underlying complexity.

What we learned Prompt Engineering is Key: We learned that the quality of the output depends almost entirely on how specific and structured the input prompt is.

Handling Unstructured Data: We gained a lot of experience in converting unstructured text (AI ideas) into structured database entries.

The Power of Simplicity: Users don't want a thousand buttons; they want a magic wand. Keeping the input simple was a major design lesson.

What's next for Intelligent Survey-as-a-Service AI Analytics: We plan to add an "Analysis" tab that uses AI to summarize the responses (e.g., sentiment analysis of open-ended answers).

Multi-Language Support: Allowing users to generate surveys in their local language instantly.

Platform Integration: Building plugins for Slack or Discord so teams can generate internal polls without leaving their chat apps.

Editability: Giving users the ability to manually tweak the AI-generated questions before publishing.

Built With

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Updates

posted an update —

ISaaS Progress Log — Intelligent Survey-as-a-Service

v0.4 — Multilingual Voice Beta and Paradata Insights

  • Added multilingual voice capture with auto-transcription for Indian languages; early tests show cleaner entity extraction and higher completion rates on voice-first journeys.[1]
  • Introduced real-time paradata checks (timestamps, response latency, device markers) to flag potential fraud and interviewer effects during collection, not post-hoc.[1]
  • Rolled out explainable validation messages so reviewers see “why flagged” and can override confidently.

v0.3 — WhatsApp Distribution + Dynamic Skip Logic

  • Launched WhatsApp Business flow with dynamic skip logic and section-level progress save.
  • Added instrument templates aligned to official survey structures, enabling rapid setup for modules like household roster and time-use blocks.[1]
  • Improved accessibility with quick-reply chips and structured media prompts for proofs where needed.

v0.2 — Agentic Pipeline and Real-time Dashboards

  • Stitched the agent workflow: Survey Design Agent → Distribution Agent → Validation Agent → Analytics Agent.
  • Shipped real-time dashboards with instant indicators and drilldowns by geography, channel, and language.
  • Added audit trails and data export with role-based access control for secure collaboration.

v0.1 — Foundation and Prototype

  • Backend scaffold in Python (Flask/Django), frontend in React, and Firestore for scalable storage.
  • Early survey design scaffolding: question types, skip logic, translations, and consent flows.
  • Privacy-by-design baseline: encryption in transit/at rest, minimal data retention, and consent logging.

What’s shipping next

  • Offline-first capture and delayed sync for low-connectivity regions.
  • Enhanced standards library mapped to MoSPI manuals and UN household survey guidelines.
  • Geo-enriched paradata and interviewer management features for large field ops.
  • SDK for third-party question banks and analytics plugins.

Screenshot highlights (text-form)

  • Dashboard: “Completion rate 78% | Voice 84% | WhatsApp 76% | Web 72%” with real-time flags panel.
  • Designer: “Section: Expenditure → Auto-suggested probes; Language: Hindi/English; Skip: If Q3>0, show Q4–Q6.”

Code snippet (pseudocode)

# Paradata-informed validation
flag = any([
    response_latency  THRESHOLD,
    device_fingerprint.duplicated(),
])
if flag:
    queue_review(case_id, reasons=explanations)

Built with: CrewAI, Python (Flask/Django), React, Firestore, WhatsApp Business API, multilingual ASR/translation stack, encryption and RBAC.

Feedback is welcome—especially on voice flows and standards alignment for NSS/TUS-style modules.[2][1]

[1] interests.data_collection [2] projects.hackathon [3] https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/attachments/84492374/4ee7c26b-9344-4ff8-b98a-8aec2f40e738/statathon-FINAL.pptx

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