When AI makes analysis abundant, the bottleneck moves to selection. Klarblick brings structure, ranking, and accountability to recurring decisions where the option pool is large and mistakes are expensive.
We are starting in biotech and actively opening to adjacent domains with the same decision pattern.
Today, portfolio committees spend weeks preparing decks, memos, and dossier updates for each gate, then critical calls are still shaped by hierarchy, persuasion, and meeting dynamics instead of comparable evidence. As AI accelerates document production, this same workflow starts to break under volume.
Klarblick decomposes each option into atomic, comparable properties, then uses agents to assemble evidence for each property at scale. Instead of relying on linear scorecards alone, it runs large pairwise comparisons and converts them into robust rankings with explicit rationale.
The bottleneck shifts from producing analysis to making better calls. Teams evaluate thousands of candidates consistently instead of the handful humans can review in a cycle. Hierarchy and presentation skill stop driving outcomes. Override decisions are recorded with rationale, creating an audit trail that improves calibration over time.
A continuous loop: structure → rank → decide → observe → refine.
Structure
Decompose each option into atomic, comparable properties, then use agents to gather evidence for each property at scale.
Rank
Run pairwise comparisons at scale, convert outcomes into a robust ranking, and track uncertainty with recorded rationale for overrides.
Decide
The decision owner calls from a ranked set, with explicit rights, captured dissent, and any override rationale written to an immutable audit trail.
Observe
Track how predictions match outcomes, then automatically refine information decomposition, ranking logic, and confidence calibration using realized results.
Klarblick's methodology comes from two bodies of work: advising leading institutions on portfolio decisions through McKinsey, and leading the engineering of a full-stack decision pipeline for a novel biotech company that had to evaluate thousands of drug candidates rapidly. Because we operated end-to-end across the entire candidate lifecycle — rather than within a single function — the underlying structure became visible. Combined with experience across institutional investors, private equity, venture capital, and banking, we recognized the same decision pattern recurring across all of them.
If your team repeatedly makes expensive portfolio decisions under uncertainty, we should talk.