> For the complete documentation index, see [llms.txt](https://docs.icustomer.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.icustomer.ai/ireveal/overview.md). # What iReveal is iReveal: identity, measurement, and outcome tracking as a standalone product, one tag, running where your data lives. iReveal is the identity and measurement layer as a standalone product: see who is actually on your site, resolve them to real people and companies, and track outcomes against the decisions that drove them, without adopting the full platform first. ## What it does * **Server-side, first-party capture** from one async tag. Not a third-party cookie play. * **Identity resolution** to immutable IDs, for humans *and* for AI agents visiting your site. * **The trace loop**: events, identity, signals, decisions, outcomes, joined end to end, so results are attributable rather than asserted. * **Consent-aware by design.** See [Consent](/ireveal/consent.md). ## Where it runs In your environment or ours: hosted, or deployed against your own warehouse, including as a Snowflake native app, so capture and resolution happen where your data already lives and nothing needs to leave. iReveal is the proof feed for [Measurement](/measurement-and-outcomes/traces-and-receipts.md): visitor identity, outcomes, and causal proof, reconciled against what each channel reports about itself. In Enterprise deployments, capture lands in your own warehouse through the native app path. Your deployment plan names the exact landing zone. ## What it is not iReveal is not another web-analytics dashboard, and it does not replace your marketing-mix or incrementality tooling, it complements them by supplying the identity-resolved, outcome-joined ground truth those models want as input. Compared to visitor de-anonymization point tools, the difference is the loop: not just *who was here*, but *what came of it*. ## From iReveal to the platform iReveal is a complete product on its own and the natural first step into the full platform: the identities and outcomes it builds are exactly what the loop runs on. See [Upgrade path](/ireveal/upgrade-path.md). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.icustomer.ai/ireveal/overview.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.