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Shapemint

Influence-and-incentive ledger that measures how third parties change an AI model’s behaviour, attributes contribution weight, and settles…

An influence-and-incentive ledger that measures how third parties change an AI model’s behaviour, attributes contribution weight, and settles rewards when influence is real — not merely claimed.

DemoDeployment-Ready FoundationfazeZERO VerifiedProprietaryDigital Assets & Virtual Assets

Last verified 15 Sept 2026 · Updated 23 Sept 2026 · Metadata v1

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Configuration + implementation engagement

Problem

Multi-party AI consortia (healthcare, finance, industrial) where external labs, annotators, or Effect.ai-style human-in-the-loop workers contribute to a shared model and currently cannot prove or price their influence.

Outcome

A deployment-ready foundation that teams can configure into production.

Who uses it

  • Operations Analyst
  • Solution Architect

Key capabilities

  • Core workflow

    AI-assistedIncluded

    Primary operating workflow for this foundation.

  • Document intelligence

    AI-assistedIncluded

    Extract and ground decisions from operating documents.

Typical workflow

Primary operating flow

Trigger: An operator starts the primary use case

  1. Task

    Stage 1: Capture inputs

    Operator

  2. AI-assisted

    Stage 2: Assist with draft

    System

  3. Human checkpoint

    Stage 3: Human approval

    Approver

Task Automated AI-assisted Human checkpoint Decision

About this foundation

Boston DeepCloud thesis product. Chauhan’s Boston deck reframes centralized AI as “closed source of the 1990s” and states four problems — Privacy, Influence, Economic, Transparency — asking whether third parties can contribute in a *quantifiably influential* way and be correctly incentivized without a trusting middleman. Shapemint productises Influence + Economic (+ transparency of contribution activity) — distinct from Cipherquest (sealed weekly contests), Fedbounty (validation-lift bounties), and Rimcast (edge latency).

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