Altman's gentle singularity. Hassabis's golden era of digital biology. Scientists are already 2-3x more productive, research timelines collapsing from years to months.
AlphaFold predicted 200M+ structures. $89B in AI funding (2025). But who synthesizes all this knowledge? The discovery rate is exponential. The synthesis capacity is linear.
There's a hidden bottleneck beneath all this acceleration.
Scientific literature grows exponentially while human capacity to read and synthesize remains linear. The gap between what exists and what any individual or team can process widens every year.
Scientists are already 2-3x more productive, but literature review still takes months. The gap isn't closing—it's accelerating.
The most important insights are buried in literature volumes no one can fully comprehend.
Systematic literature reviews are the rigorous, auditable way to synthesize evidence. They're required for academic research, regulatory submissions, health technology assessment, and AI training. But they take 12-16 months on average and cost $141,000+ per review.
DeepMind's AlphaFold required decades of prior protein research synthesized. Every breakthrough depends on this bottleneck.
The gold standard is also the chokepoint.
Processing 10,000 papers manually takes 1-2 years with a 10.76% error rate. This forces researchers to narrow their scope to what's humanly manageable and costs biopharma up to $500,000 per day in delayed market access.
Research timelines are collapsing: years → months. $89B in AI funding (2025) means exponentially more papers. Manual synthesis can't scale.
Scale constraints force everyone to ask smaller questions than they should.
Large language models cannot yet reliably handle the complex methodological judgments that systematic reviews require. Consumer-grade AI tools lack audit trails, transparency, and expert validation.
Where credibility is currency, generic AI risks the trust these domains depend on.
Built by two core contributors to DSPy—our system achieves 98% accuracy on complex screening and data extraction tasks. As little as 2 weeks (vs. 12-16 months) from search to analysis-ready data, with complete audit trails and expert validation from academic researchers who publish on this work.
Research-grade rigor at AI speed.
Researchers can write the magnum opus reviews they've always envisioned. Biopharma can compress regulatory timelines by 13 weeks and preserve billions in patent life. AI labs can train on expert-validated scientific reasoning at scale.
Enable Hassabis's "golden era of digital biology". Support AI co-scientists. Accelerate toward Altman's predictions.
Previously impossible work becomes possible.
The more we do, the harder we become to catch.
Three billion-dollar markets converge on one capability.
We solved academic literature synthesis because it's the universal bottleneck across the highest-value applications we could find. Now we're scaling it.