Specialist toolCase studyAIDataAutomation

Multi-Source Research Assistant

Start with a research question, gather selected social sources, track collection and explore summaries through evidence-based follow-ups.

Audience
Individuals & teams

No public application link is provided. Explore the case study and screenshots.

Screenshots & demos

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Project overview

This multi-source research assistant supports preliminary investigation of product feedback, user discussion and market topics. Users define what they are studying and what they want to understand, select sources and start a scoped research task. Collection, progress tracking and synthesis are connected to help identify issues worth investigating further.

Define the question and source scope

  • A request can specify a product category, audience or concern, such as price, experience or after-sales support.
  • Users select supported social platforms and set the collection scope for each source.

Requests without enough product, industry or audience context are asked for more information, so the subsequent work can be organised around a clearer research objective.

Check progress and actual coverage

Once started, a task exposes stages such as collection and analysis, distinguishing work that is waiting, complete or has encountered an error.

  • Results include the total material collected, counts by platform and unsuccessful sources. This helps establish what the research covered.
  • A task with no usable material is marked as failed rather than presenting an empty collection as a completed research finding.

Synthesise material and ask follow-up questions

  • Collected material can be synthesised into a summary and key points. Completed tasks also support follow-up questions, such as asking what kinds of discussions contain a particular concern.
  • Responses use the collected content, summary and relevant source context to turn broad impressions into questions that can be checked further.

If deeper synthesis does not complete, the result may contain only basic information such as collection counts and should be interpreted accordingly.

Reuse material and interpret the findings

Tasks with similar conditions can reuse existing collection results, while an explicit refresh requests new material. This allows repeated questions about a topic to build on available material while retaining the option to collect again.

The current project is suited to validating a research workflow and connecting it to existing business tools. Findings cover the material actually obtained and provide a starting point for interviews, product discussions or further research, not a statistical estimate of all users’ opinions.

Ownership & delivery

I independently planned the research tool, designed its interface and architecture, and implemented, deployed and iterated on its frontend and backend. I built a pipeline from question planning and multi-source collection through deduplication, vector indexing, reranking and source-grounded follow-up, with persistent tasks, execution recovery and bounded retries.

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