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fazeZEROApplication Atlas

Demo listing. This page uses illustrative seed metadata to demonstrate the Atlas. Claims shown here are not verified product facts.

PocketLatch

Phone-side context engine that predicts user context in ~11ms and actuates linked IoT devices even when the cloud is unreachable, while…

A phone-side context engine that predicts user context in ~11ms and actuates linked IoT devices even when the cloud is unreachable, while periodically syncing heavier model parameters from a server trainer.

DemoDeployment-Ready FoundationfazeZERO VerifiedProprietaryInternet of Things & Edge

Last verified 15 Sept 2026 · Updated 23 Sept 2026 · Metadata v4

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Configuration + implementation engagement

Problem

Smart-home and building-automation OEMs whose apps already sit on the user’s phone but still round-trip every context decision to the cloud.

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

Split-brain context control. Cloud-only context learning is more accurate but fails real-time IoT actuation under network loss; phone-only models are too weak. PocketLatch productizes the paper’s client/server pair: light on-device inference (mean accuracy up to 97.51%, ~11ms) refreshed by server-learned parameters.

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