Red Label Intelligence
Red Label Intelligence

Discipline

Methodology

Red Label sits in the space between strategy theater and raw data feeds. Scenario decks from large consultancies can be directionally useful yet under-specified on mechanics. Kroll-style due diligence shops produce thick files that defend process more often than they answer the investment question. Generic OSINT vendors deliver volume without a rigorous map from evidence to judgment. Big-four advisory often bundles policy and audit constraints that shape what can be said in the room.

This practice is smaller and assumes a different burden. Every engagement starts from the decision you are trying to make. Collection runs at machine speed across corporate filings, litigation, sanctions and watchlists, registries, and qualified media across a wide jurisdictional footprint. Analysts then do the work software cannot. They reconcile conflicts in the record, calibrate confidence, document gaps, and state where inference begins.

Red Label is not selling a black box. The standards page records how we label sources, separate fact from judgment, and close with a position you can defend internally.

Analytical engine

Every Red Label assessment runs through a proprietary analytical engine called Vektora. The engine structures the actor map, surfaces the assumptions that connect them, and stress-tests hypotheses at scale so reviewers can follow the logic and contest inputs directly. The methodology that governs client deliverables is the same spine described in The Shape of Outcomes (Red Label Press). The public run catalog and technical framing for the engine live at vektora.md.

Research notes

Chubby Tails is the firm's published research stream on Substack. It is separate from client work. It exists to show how we think in public about live problems. Subscription and delivery stay on Substack so correspondence and mailing lists remain portable.

Collection and judgment

AI-accelerated collection widens the evidence surface. It does not replace assessment. Where models summarize, we treat outputs as provisional until Tier 1 or Tier 2 corroboration supports the claim. Where records are thin, we say so and explain why the missing evidence matters to your outcome.

Read the eight delivery standards · Discuss an engagement