InsightAgent Is Transforming Systematic Reviews with AI-Powered UI
This user interface introduces a new accelerated paradigm in performing research…
Something I have come to realise recently is that horizontal solutions can be bought, but vertical solutions need to be built. This tool (InsightAgent) is an example of a specific use vertical focussed UI.
When it comes to systemic reviews, the user needs to
- Clearly specifying the scope and objectives
- Collecting all relevant studies from databases using predefined criteria
- Filtering studies based on inclusion/exclusion criteria
- Analysing and summarising findings from selected studies
- Presenting results in a clear, reproducible manner
InsightAgent is a system that allows you to upload a large corpus of information, such as scholarly literature, and perform an in-depth analysis (akin to a deep search) to identify relevant studies and extract key insights.
The system uses semantic clusters and multi-agent collaboration to process the data .
As seen above, InsightAgent provides intuitive visualisations of the literature corpus and the analysis process, helping you understand the data’s structure and the system’s progress.
The visualisations, combined with human-in-the-loop feedback, make it a powerful tool for synthesising and exploring large datasets.
Below, a screenshot of the InsightAgent interface while conducting a systematic review.
The central Canvas is an infinite- scrollable space for creating multiple Environments (E1, E2, E3) and attaching any number of agents or collaboration panels.
This design allows users to freely drag and drop AI Agents, documents, or synthesis outputs as they refine the review process.
And below, an overview of the InsightAgent workflow. In Stage 1, the corpus is mapped into semantic clusters.
In Stage 2, multiple agents concurrently read and synthesise evidence under real-time user guidance for each cluster.
Finally, in Stage 3, findings of all AI Agents are integrated into a complete data review.
At its core, InsightAgent breaks down a large literature corpus into semantically coherent chunks, allowing for more focused analysis.
Its multi-agent design assigns specialised tasks to different AI components, enhancing the precision of study selection and summary generation.
This approach significantly improves the quality of SRs.
Also, InsightAgent provides intuitive visualisations of the literature corpus and AI Agent activities, enabling researchers to monitor progress in real time and offer feedback based on their expertise.
This human-in-the-loop approach ensures that the AI remains aligned with the nuanced needs of domain experts.
Lastly, I also like the data visualisation component of this tool, it adds a new dimension where relationships between data components can be detected not noticed before. And from here user’s can drill down into the detail.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
