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Vektor Technologies

The future of RL for enterprise workflows.

Synthetic worlds, exact verifiers, and tasks that make agents reliable where money moves.

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The method

How training data gets exact.

  1. 01

    Generate the world.

    Synthetic enterprises with real policies, systems, and edge cases.

  2. 02

    Compute the answer.

    Every task solved by construction. No LLM judges.

  3. 03

    Verify every step.

    Exact verifiers catch and correct capability errors while the model trains.

The evidence

The research agrees.

51.3%

Best frontier score on tau3-Banking. Policy tasks stay mostly unsolved.

0.82 → 0.71

Adjudication accuracy on insurance claims once an exclusion clause is active.

~25.5%

pass^1 at high reasoning budgets, degrading over repeated trials.

-45 Elo

GPT-6 Astra's drop on GDPval-AA v2 across 44 occupations.