The Shift
For decades, producing analysis was expensive, so more analysis meant better decisions. AI changed that. Teams now generate dossiers, memos, and code at high speed — but the committee workflows haven't changed. When everyone brings a compelling narrative, meetings get steered by whoever owns the room, not by the best evidence.
The bottleneck moved from producing a case to selecting the right option. Most organizations haven't caught up.
The Failure Mode We See Repeatedly
We have seen this pattern firsthand across investment and portfolio contexts: committees are nominally evidence-driven, but decisions can be nudged by whoever owns the room.
Personal exposure amplifies the bias. If careers, budgets, or reputations are tied to a specific candidate, teams naturally fight to keep it funded — even when it is no longer the highest expected-value path. This is not a failure of intelligence. It is a structural problem: the person who owns the analysis also owns the advocacy. Klarblick separates evidence assembly from decision ownership so that the person making the call is not the same person who built the case.
Our Method
Klarblick compresses large evidence volumes into many small, explicit trade-off decisions. Instead of asking for one long narrative judgment, we force repeated pairwise comparisons: is option A better than option B on this property?
Run that process thousands of times, with humans and models where each is strongest, and a robust ranking emerges. It is the same logic behind tournaments and other competitive systems: repeated local comparisons surface global order.
- Explicit decomposition into comparable properties.
- Large-scale pairwise comparisons with traceable rationale.
- Ranking with uncertainty and clear override records.
- Human accountability for final calls.
Why We Start In Biotech
Our philosophy is general, but our first domain is biotech because the pain is acute and the structure is clear. Decisions are expensive, repeated, and slow to validate.
Our blueprints come from two bodies of work: advising leading institutions on portfolio decisions through McKinsey, and leading the engineering of a decision pipeline for a novel biotech company that had to evaluate thousands of drug candidates rapidly. Working end-to-end across the full candidate lifecycle — rather than within a single function — made the underlying structure visible. The methodology is not hypothetical.