Our platform transforms evidence gathering in pharma and health by utilizing AI to streamline searches through over 250 million publications, enabling efficient access and analysis of critical knowledge.
I once watched a team spend two weeks trying to answer what should have been a straightforward question. We needed a simple summary: which biomarkers had been studied in moderate-to-severe psoriasis over the last five years?
The team ran searches in PubMed, built a spreadsheet of results, screened nearly 400 abstracts, and tried to cluster insights into themes. They were fast, methodical, and experienced. And they still struggled. Not because the evidence wasn’t there, but because it was buried under everything else.
We’ve built systems that reward rigor, but not speed. And when time runs short, the shortcuts creep in. A partial search. A summary from a past deck. A “representative” citation that everyone hopes is close enough.
That’s the real problem. Not the number of publications. The lack of a system to work with them properly.
When it comes to working with published literature, most teams still rely on a mix of Boolean logic, manual screening, and copy-pasted summaries. It’s slow. It’s repetitive. And unless you’ve built the search yourself, it’s often hard to trust what was included or left out.
Boolean queries are brittle. A missed synonym or the wrong field tag can exclude dozens of relevant papers. Abstract screening is tedious, and rarely done in full by anyone except the person who ran the original search. And synthesis, where multiple findings are combined into a single statement, is where quality quietly falls apart.
Even with good people, the process is hard to scale. Especially when you’re building a deck for an internal workshop, not a publication. There’s pressure to move quickly, and no clear standard for how evidence is selected or summarized.
The result? Teams take different approaches each time. Past work gets lost. And no one can see the trail that led to a recommendation.
That’s what led us to build a fully integrated TLR workflow. One space where you define the question, run and refine the search, screen abstracts, extract findings, and generate summaries... without jumping between platforms.
At the heart of our platform is a growing knowledge base of over 250 million indexed publications. That number sounds overwhelming, but it becomes usable through structure. Every paper is tagged with MeSH major headings, mapped to secondary concepts, and linked to disambiguated author profiles and topic clusters.
Instead of building new searches from scratch, you can start from a library of prebuilt, reusable strings. You can tweak them, refine them, and see what’s included with each adjustment. Every query is transparent. Every result is tied to a PMID.
Once your search is set, the entire workflow: screening abstracts, extracting data, summarizing findings, is handled in one continuous flow. No jumping between tools. No spreadsheets. And no ambiguity about what was used or why.
At any point, you can trace a summary back to the sentence it came from. That’s what makes this different. You’re never just reading AI-generated text. You’re seeing the original evidence, in context, ready to share or reuse.
We’re not trying to write strategy decks for you. We’re trying to save you from the repetitive, mechanical steps that get in the way of thoughtful work.
Our system uses AI in a few very specific ways. First, to refine search queries. You write the core of what you need, and the system suggests related terms or MAJR tags you might have missed.
Then, during abstract screening, summaries are automatically generated using grounded AI. That means every line in the summary links directly to the underlying publication text.
Once you’ve screened and selected your evidence, key points are extracted into structured fields, ready to slot into summaries, dashboards, or visual outputs. The platform can suggest charts, tables, or citation blocks, all tied to source materials.
You still decide what goes into the final output. But the system makes it easier to get there with confidence.
The best teams don’t just find evidence. They organize it. They reuse it. They know where it came from, and they trust what it means.
That’s what we’re building: a system that helps you get to the right answer, not just a fast one. A place where evidence builds on itself, instead of being recreated every time. A process that speeds up delivery, without sacrificing traceability or depth.
The number of publications will keep growing. But that’s not the problem. The problem is that most systems treat them all the same.
Ours doesn’t.
It helps you find the ones that matter. And then makes sure you never lose track of them again.