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Use case · Enterprise

Track AI visibility across brands, markets, and teams.

Use tenant-scoped projects, credit tracking, API keys, and priority processing to operationalize AI visibility measurement.

Built for enterprise measuring AI visibility across ChatGPT, Perplexity, Gemini, Copilot, and AI Overviews.

For Enterprise
tailored workflow
Enterprise

Use case · Enterprise

Designed around what enterprise actually need to ship.
Enterprise use cases

Operationalize AI visibility across the org.

01

Tenant-scoped projects per brand

Run separate, isolated AI visibility projects for each brand, region, or business unit — with clean separation of queries, competitors, and history.

02

Credit ledger and metering

Track engine usage, prompt fan-out, and run costs as a transparent credit ledger so finance and procurement can plan AI visibility spend.

03

API keys for internal tooling

Hand off API keys to internal BI, content, or growth platforms so AI visibility data flows into the dashboards your teams already use.

04

Priority processing for large fan-outs

Enterprise queues get priority slots — large multi-engine, multi-prompt runs complete on a predictable cadence instead of waiting in the common pool.

05

Multi-market and multi-language tracking

Compare AI visibility across markets and languages from one workspace, then roll up the numbers per brand, region, or product line.

06

White-label visibility exports

Export visibility reports under your own brand for internal stakeholders, board updates, or investor narratives.

07

Role-based collaboration

Add SEO, content, growth, brand, and PR teammates with the right access — every team sees the visibility signal that matters to them.

Workflow

How it fits into your monthly cycle.

A practical loop your team can repeat — measure visibility, prioritize gaps, ship improvements, then re-measure to confirm movement.

  1. 01Step 1

    Onboard projects

    Stand up tenant-scoped projects per brand, region, or business unit with their own competitors, queries, and engine settings.

  2. 02Step 2

    Set policy and budgets

    Define credit allocations, priority lanes, and access roles. Everyone runs against the same governance, not ad-hoc tooling.

  3. 03Step 3

    Run measurement on a cadence

    Schedule recurring multi-engine fan-outs and repeat runs. Stability and movement become signals the org can plan against.

  4. 04Step 4

    Distribute insight per team

    Route visibility data into BI, content briefs, and internal dashboards through the API. Each team sees what matters to them.

  5. 05Step 5

    Review at the leadership layer

    Roll up AI visibility into a single executive view — by brand, region, and competitor — so leadership can compare and decide.

  6. 06Step 6

    Iterate with confidence

    Re-measure after every initiative to confirm AI visibility movement, then double down on what worked.

Enterprise needs AI visibility that is governed, metered, and roll-up-ready — not a single seat poking at ChatGPT. focadio is built for the operating layer.

Make it count

Turn AI visibility into something your team can act on.

Measure where enterprise appear, where they are missing, and what to improve first.