Measure how your brand appears across AI engines at the scale enterprise teams need — with tenant-scoped data, predictable run pipelines, and the operational primitives that keep multi-team AI visibility programs honest.
Built for brands tracking visibility across multiple categories, regions, and competitor sets in parallel.
Designed to integrate cleanly with internal SEO, growth, brand, and analytics teams.
We only describe capabilities that exist in the platform architecture today. Each card below maps to a real primitive used by the run, scoring, and metering pipeline.
Every project, query, run, and result is scoped to the customer tenant. Multi-team enterprise programs stay isolated by organization.
Enterprise teams can issue API keys for programmatic access to projects, runs, and results — alongside the dashboard JWT flow.
Run jobs are placed on the queue with plan-based priority, so enterprise workloads do not get blocked behind lower-priority traffic.
Each run debits credits on creation. The ledger tracks usage so visibility runs remain predictable and auditable across teams.
Run creation supports idempotency keys, preventing duplicate visibility runs and double-charges during retries or workflow handoffs.
Pipeline stages run as background jobs on BullMQ + Redis workers, so the API stays non-blocking and the system scales with run volume.
Enterprise engagements start with a scoping conversation about your brands, regions, competitor sets, query coverage, and the way your teams need to operate. We will only describe what the platform actually supports today — and what is on the roadmap.
Talk to us about scoping AI visibility measurement across your brands, markets, and teams — with the operational primitives the platform supports today.