Research Connect

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Inspiration

Modern research—whether academic, UX, or product testing—is slow, expensive, and biased toward limited participant pools. Recruiting the right participants, screening them correctly, and extracting insights from responses often takes more time than the actual experiment.

We wanted to build a system where AI acts as a co-researcher, handling the repetitive and error-prone parts of the research workflow so humans can focus on discovery and decision-making.

What We Built

Research Connect is a full-stack, AI-powered research and user-testing platform that connects:

Researchers who want to run studies, product tests, or surveys

Participants who want to contribute and get rewarded

Collaborators who want to work across disciplines

Data consumers who want access to structured research datasets

The platform supports multiple research verticals:

Product testing

Surveys

Research labs & academic studies

At its core, Research Connect uses AI agents to:

Match participants to studies using eligibility and context

Pre-screen users before participation

Summarize and analyze research responses

Surface patterns, anomalies, and insights automatically

How We Used AI

AI is not an add-on—it’s the backbone of the system.

We implemented:

AI Match Scoring: Each participant receives a match score for a study based on eligibility, background, and context.

AI Screening & Summarization: Responses are automatically cleaned and summarized into researcher-ready insights.

AI Collaboration Discovery: Researchers can discover peers working on related topics.

Agent-Driven Pipelines: When a study closes, AI agents process results end-to-end.

This turns raw qualitative data into structured intelligence.

How We Built It

We built Research Connect as a modular, scalable platform:

A clean multi-role frontend for researchers and participants

AI workflows running in secure, isolated environments

Agent-based processing pipelines for screening and analysis

A flexible architecture that supports multiple AI providers and models

We intentionally focused on realistic production architecture, not just a demo, so the system could scale beyond the hackathon.

Challenges We Faced

Designing AI outputs that are explainable, not just “smart”

Balancing automation with human trust in research workflows

Structuring research data so it remains reusable and shareable

Making AI visible and intuitive in the user experience

Solving these forced us to think deeply about human-AI collaboration, not just model performance.

What We Learned

AI is most powerful when it augments expert workflows, not replaces them

Clear UX around AI decisions builds trust faster than raw accuracy

Infrastructure and agent orchestration matter as much as models

Research tools benefit enormously from automation done right

Future Work

Real-time voice-based research interviews

Deeper trust and verification layers for researchers and participants

Advanced analytics dashboards for longitudinal studies

Embedded research inside product flows and live user journeys

Built With

• .cv
• ai/ml-apis
• concept
• daytona-(secure-execution)
• elevenlabs-(voice)
• google-gemini
• identity
• leanmcp-(ai-agents)
• next.js
• node.js
• typescript