Skip to content
fazeZEROApplication Atlas

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

Frecat

Intelligent browser layer that predicts address-bar destinations from frecency signals and recommends next sites by URL category — without…

An intelligent browser layer that predicts address-bar destinations from frecency signals and recommends next sites by URL category — without fetching page content to classify.

DemoDesign onlyfazeZERO VerifiedProprietaryStrategy & Transformation

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

Configure this applicationRequest a demo

Configuration + implementation engagement

Problem

Browser vendors, search toolbars, and enterprise secure-browser teams that already store history (URL, first visit, last visit, visit counts) and want smarter omnibox ranking plus category-wise discovery.

Outcome

A design-complete foundation in the active pipeline — engineering scaffold planned to complete this month.

Who uses it

  • Operations Analyst
  • Solution Architect

Key capabilities

Capabilities are defined during customer configuration.

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

History-native navigation intelligence. Default browsers use explicit frecency heuristics; generic recommenders need page text. Frecat learns frecency with supervised regression over history features, classifies URLs from the URL string alone (Naive Bayes + hyperparameter optimisation in the source), and ranks category-wise recommendations from predicted frecency and category visit mass — reporting ~87.6% classification score class metrics after tuning vs weaker untuned baselines.

  • Fieldproof

    Deployment assurance system for funders and implementing organisations putting AI into high-stakes humanitarian and development work:…

    • Addresses the same problem: Manual Workflow, Fragmented Systems
    • Shared capabilities: Workflow Automation, Document Intelligence
  • Revaloop

    Disposition and residual-value system for durable-goods producers that decides, unit by unit, which next cycle a returned product should…

    • Addresses the same problem: Manual Workflow, Fragmented Systems
    • Shared capabilities: Workflow Automation, Document Intelligence
  • Benchline

    Capability-runway planner for large employers that forecasts skill demand only as far out as the slowest supply lever can respond, then…

    • Addresses the same problem: Manual Workflow, Fragmented Systems
    • Shared capabilities: Workflow Automation, Document Intelligence
  • Centaura

    Decision-rights and override ledger for ai-assisted work that grades each use case's earned autonomy by what it can explain and prove,…

    • Addresses the same problem: Manual Workflow, Fragmented Systems
    • Shared capabilities: Workflow Automation, Document Intelligence
  • Curvebank

    Reference-class forecasting desk for new-technology ramps that forecasts a launch by naming the historical ramp it will resemble,…

    • Addresses the same problem: Manual Workflow, Fragmented Systems
    • Shared capabilities: Workflow Automation, Document Intelligence
  • Embedra

    Representation registry for systematic trading research that treats the signal-compression layer — not the classifier — as the governed,…

    • Addresses the same problem: Manual Workflow, Fragmented Systems
    • Shared capabilities: Workflow Automation, Document Intelligence
Configure this application