Enterprise

AI visibility your security team can inspect.

Measure how your brand appears in ChatGPT, Gemini, and Claude with a workspace-scoped evidence trail. Ready for procurement, security review, and the teams who act on the result.

  • Bring your own API keys
  • Encrypted at rest
  • No LLM-as-judge scoring
Trust

A defensible path from prompt to proof.

The important boundaries are visible: credentials, evidence, and execution each have one accountable owner.

Keep the credential boundary clear

Your provider account remains yours from setup through execution.

  • Provider keys are encrypted at rest and resolved only at execution time
  • Keys are never returned in API responses or logged in clear text
  • Same-origin API proxying keeps backend topology out of the browser bundle

Trace every reported number

Security, analytics, and growth teams can inspect the same evidence.

  • Raw responses are persisted before derived metrics are calculated
  • Deterministic rules and versioned analysis explain how a result was produced
  • Coverage, unavailable, and observed-zero states stay distinct

Operate with durable runs

Long-running crawls and audits have an explicit execution trail.

  • PostgreSQL provides durable state and the work queue
  • Leases, heartbeats, retries, and terminal states are recorded
  • Runtime Zod and Pydantic contracts validate the browser/API boundary

How a request travels

Managed cloud · same-origin boundary
  1. 01

    Browser

    Authenticated HTTPS

  2. 02

    Same-origin app

    Relative API requests

  3. 03

    API boundary

    Schema + workspace auth

  4. 04

    PostgreSQL

    Evidence + durable queue

  5. 05

    Workers

    Leased execution

  6. 06

    Answer engines

    Your configured keys

Designed for review

A clear fit for high-trust teams.

Enterprise is the right conversation when your measurement program needs more operating context than a self-serve plan provides.

Security-led evaluation

Give reviewers a concise map of credential handling, workspace authorization, evidence retention, and the managed-cloud boundary.

Multiple teams or brands

Keep projects, prompts, provider connections, and audit history scoped to the workspace that owns them.

A measurement program

Define a prompt portfolio, run comparable audits, and give each reported observation the context needed for review.

Sizing

Scope the agreement around the work.

Enterprise sizing follows your measurement program: its volume, coverage, teams, history, provider setup, and support model.

Enterprise agreement

Six inputs. One operating plan.

Quoted to fit

Monthly audit runs

Volume

prompt × engine × repetition

Sized to concurrent evaluation across your active brand topics.

Monitored URLs

Coverage

total monitored URL set

Brand, product and competitor pages included in your site-health scope.

Projects & seats

Teams

per enterprise workspace

Each project keeps its own prompts, competitors, engines and trails.

Evidence retention

History

set by agreement

Retention terms for raw responses, artifacts and derived metrics.

Engine connections

Providers

OpenAI, Google, Anthropic

The supported direct transports, each using workspace BYOK credentials.

Support & SLA

Response

set by agreement

Response commitments and support channels defined in the contract.

Audit trail included

Your proposal can start with the evidence boundary: deterministic rules, immutable artifacts, and provenance on each derived metric.

Request custom quote

Give your AI visibility program a reviewable operating model.

Tell us about your volumes, constraints, provider setup, and review process. We will map the conversation to the evidence your team needs.

  • Bring your own API keys
  • Encrypted at rest
  • No LLM-as-judge scoring