Engineering
Problems AI visibility monitoring solves for engineering teams
How engineering teams use AI visibility monitoring: answer quality as a signal, competitor citation shifts, accuracy risk, and a stored audit trail.
Observability
Treat AI answer quality as a measurable signal. Each run records the prompt, the raw answer, and the four scores, so a change is a diff rather than a rumour.
Competitive intelligence
Competitor Presence is the share of answers that name a rival. A scheduled run on Pro, Agency, or self-host can alert you when a metric moves past the threshold you set.
Accuracy risk
Accuracy Risk is the share of answers flagged for misdescribing the company. That is the signal for a wrong product fact, a retired name, or a competitor attributed as you.
Audit trail
Raw answers stay with the run. You can re-analyse a stored answer without sending the prompt again. CSV export of that trail is a licence-keyed feature on self-host and a higher cloud tier, not the trial.
What the stack actually is
Cloud is a hosted Next.js app with Postgres, Claude and GPT, and monthly check caps plus prepaid credits. Self-host is the same core on your infrastructure, with your API keys. It is not a local-only desktop app, and it does not call Ollama.