Marketing Intelligence that you actually own.
Connect Google Analytics 4 and analytics tools like Adobe Analytics to one AI layer.
Independent web analytics that keep your first-party data yours.
Learn moreAnalytics integrations
Google's current web analytics platform and, for most teams, the system of record for site traffic: connect it to read your GA4 metrics next to your other sources instead of in isolation.
Adobe's enterprise analytics suite, common in larger organisations with their own data teams, connected so its reporting is readable alongside the rest of your marketing data.
Model providers
OpenAI is one of the models available in hurra.ai, reasoning over the systems you connected rather than over general marketing knowledge.
Anthropic's Claude is one of the models available in hurra.ai, useful where you want a second opinion on exactly the same connected context.
Google Gemini is one of the models available in hurra.ai, answering from the systems you connected like every other model in the platform.
DeepSeek is one of the models available in hurra.ai, chosen per task like any other and reading the same connected systems.
Kimi from Moonshot AI is one of the models available in hurra.ai, suited to questions that span a large body of connected reporting in one pass.
Perplexity is an answer engine, used in hurra.ai as a search source for web research and as a surface the GEO skill optimises your content to be cited on.
Microsoft Copilot is where many teams already write. hurra.ai reconciles the marketing data underneath, so the numbers you carry in can be checked.
Of Mistral's models, Codestral is the one available in hurra.ai, chosen per task like every other model in the platform.
You can connect everything. hurra.ai reads the signals from the tools you already run and turns them into one independent view of what works, without locking your data inside another platform.
Put Marketing Intelligence to work on your own stack
On this page
Why first-party web analytics matter
First-party web analytics is measurement you run on your own properties, where the data is collected by you and stays in your possession. It stands against the usual arrangement, in which the numbers live inside a platform you rent and are shaped by that platform’s definitions.
The practical consequence shows up when something changes. A pricing change, a policy change or a contract that ends can take your history with it. When the measurement runs on your side, the record outlives the tool.
There is a second reason, and it is about trust rather than ownership. A platform that sells you media is also the platform reporting on that media. Independent web analytics gives you a figure that was not produced by the party being measured.
Cookieless analytics belongs in the same conversation. As third-party identifiers keep losing coverage, any measurement built on them keeps getting thinner. First-party data tracking does not depend on them, which is why it holds up better over time.
Connecting analytics data to AI
Connecting analytics to AI means giving a language model read access to your own measurement, so it answers from your numbers instead of from general marketing knowledge. Without that access a model can only describe how analytics usually works.
The connection has to be explicit to be worth anything. Which system, which metric, which time range. A model that reads everything is hard to audit, and a model given nothing specific will produce fluent sentences with no basis in your account.
hurra.ai runs on multiple frontier models. You choose the model per task, whether that is ChatGPT, Claude, Gemini or one of the others, or you let the platform choose. Whichever model answers, it answers from the systems you connected.
What changes in daily work is the question you can ask. Instead of exporting a report and reading it yourself, you ask what moved last week and get an answer grounded in the systems you connected.
Data quality as the foundation
Marketing data quality is the degree to which your numbers mean what you think they mean: consistent definitions, complete collection, and a clear record of how each figure was produced. Everything above it depends on that layer holding.
Most disagreements between dashboards are not errors. Google Analytics 4 and an ad platform count a conversion differently because they were built to answer different questions. The trouble starts when one of those numbers is quietly presented as the truth.
An intelligence layer earns its place by keeping the difference visible. It shows the platform figure and your own figure next to each other and says which is which. That is less comfortable than a single number and considerably more useful.
This is also what makes an AI answer worth trusting. A model reasoning over reconciled, well-defined data can be checked against the source. A model reasoning over a merged average cannot.
What a first-party measurement stack includes
First-party measurement stands or falls with how the data is collected. Collection under your own domain, set up through a proxy or load balancer, keeps cookies persistent and removes third-party scripts from the critical path. Server-side tracking moves the event delivery to your own infrastructure, and cookieless tracking keeps conversions measurable through privacy-safe identifiers when cookies are rejected, and it does so without fingerprinting.
Consent and tags belong to the same system. An integrated consent manager records user choices once and passes them through the entire pipeline, and because it is coupled to tag management, a rejected consent stops the tags it covers from firing at all. Managing web and server-side tags from one interface, with preview for web tags, versioning and rollback, is what keeps that enforceable across teams.
Journeys today span devices and screens. Deterministic matching links users through hashed e-mail identifiers, probabilistic models fill the gaps, and connected-TV activity joins the same conversion paths through IP-based linking. Organic visits are classified automatically, including traffic arriving from LLM-based assistants.
On top of a record like that, attribution becomes an instrument rather than a setting: rule-based and data-driven models such as Markov chains and logistic regression can be simulated against each other, MMM-based reporting feeds media-mix weights into the multi-touch attribution, and audiences and attributed conversions flow back to ad platforms such as Google Ads, Meta and DV360. In hurra.ai, that stack is OWA Pro, connected as a preinstalled source, with more than 100 pre-built reports and over 200 KPIs sitting on the record it collects.
Frequently asked questions
What is a marketing intelligence platform?
A marketing intelligence platform is a layer that reads the marketing systems you already run and presents their numbers in one place. It does not replace your analytics, your ad platforms or your CRM. It sits above them, so you can see what each one reports and where they disagree.
How do I connect my analytics data to an AI assistant?
You connect the analytics system as a source, then choose a model such as ChatGPT, Claude or Gemini, and define what that model may read. hurra.ai reads from your systems and shows the numbers side by side, and the assistant works from exactly that. The access is granted by you and can be revoked.
What is first-party data tracking and why does it matter?
First-party data tracking is measurement you collect on your own properties: sessions, conversions and product events. It matters because it does not depend on third-party cookies and it is not shaped by a platform that also sells you media. It is the part of your measurement that stays yours.
What is cookieless tracking?
Cookieless tracking measures conversions and behaviour when cookies are rejected or unavailable, using privacy-safe identifiers instead of fingerprinting. In practice it sits alongside first-party collection under your own domain: the cookie path stays the primary record, and the cookieless path keeps measurement continuous where cookies cannot persist. OWA Pro ships both as part of its tracking layer.
Is there an independent alternative to Google Analytics 4?
Yes. OWA Pro is an independent web analytics system from hurra.com and is available in the hurra.ai layer. Many teams run it next to Google Analytics 4 rather than instead of it, so they hold a second, independent reading of the same traffic.
Missing a system you run? Tell us and we will look into it.