CapeStart, Inc. has officially announced the launch of a revamped MadeAi™ solution, the company’s award-winning generative AI-based platform, designed to expedite the synthesis of content and data for the life sciences. This the platform does to help time-strapped teams scale faster, enable stronger regulatory submissions, and advance the pace of scientific research.
In case you missed it, MadeAi first launched during November 2024 as MadeAi-LR, a GenAI-enabled software solution capable of streamlining key applications used across new therapy development. The stated applications included systematic literature reviews (SLR), clinical evidence reports (CERs), meta-analysis, targeted literature reviews (TLRs), and other clinical literature assessments.
Thanks to all the given components, the technology was able to cut literature review time in half, while simultaneously centralizing the process with an end-to-end platform. Such a setup would help the technology, all in all, deliver 90% accuracy. Not just that, it would also pave the way for flawless sourcing and attribution along the way to clock verifiable, traceable, and trustworthy results.
The solution’s efficacy in what it does can be further understood once you consider, since its launch, the platform has been recognized for AI excellence in life sciences several times over.
For instance, it received a Stevie® award in the 21st Annual International Business Awards® program, where the platform was recognized as an innovative Artificial Intelligence/Machine Learning Solution in Healthcare within the Business Technology category. Apart from that, the solution was named a PM360 2024 Innovator in the Generative AI Category.
Aiding its case even more would be MadeAi’s win at the 2025 Artificial Intelligence Excellence Awards, presented by the Business Intelligence Group.
Turning our attention towards the updates version of MadeAi, we begin from its promise to support both AI-aided review, as well as a use case where AI plays the role of a reviewer itself.
For the AI-aided Review mode, AI-generated suggestions are shared with two blinded human reviewers to facilitate faster, more informed decisions.
On the flipside, if a user chooses the AI as Reviewer mode, one human reviewer works in parallel with the AI, which serves as the second reviewer. The idea behind this relates to reducing manual workload without compromising review quality.
Next up, the updated version brings forth an expanded and customizable screening model, something which offers you more than a dozen screening criteria including PICOS. Beyond that, user-defined criteria are also supported to accommodate projects with novel screening needs.
Another detail worth a mention is rooted in the potential for more dynamic extraction. This essentially covers Summary-level Extraction and an Arm-level Extraction.
Now, while Summary-level Extraction is understood to be ideal for internal desk research and for accessing a consolidated view of key information from each study, Arm-level Extraction comes in handy to provide a granular breakdown of outcomes across different study groups, thus showing the origin of extracted content and making reviews, along with edits, fast and efficient.
Hold on, we still have a few bits left to unpack; considering we haven’t yet touched upon MadeAi’s ability in the context of performing AI Agent-enhanced extraction. The stated offering, on its part, allows users to seamlessly extract the necessary information from text, tables, graphs, and figures.
Rounding up highlights would be piece of detail relaying quality appraisal facility being built right into MadeAi workflow. This makes it possible for users to assess the risk-of-bias for included articles, and at the same time, visualize the results through intuitive tables and charts.
Among other things, we ought to mention how the platform in question is actually developed on the back of input from 15 different pharmaceutical companies.
“MadeAi-LR customers are completing more literature reviews in less time and seeing immediate ROI, and they’re eager for us to evolve our platform to address their more complex research needs,” said Gaugarin Oliver, founder and CEO of CapeStart. “Our latest release offers enhancements that allow the GenAI to support more dynamic aspects of literature review research—including AI as Reviewer, Arm-level Extraction, and Quality Appraisals, among other improvements—so teams can scale faster and focus on what matters most: advancing science.”
