Upbuild
Written by Uptimal, the team that builds and operates Upbuild and runs marketing analytics for multinational clients across the globe. Everything below reflects how the product actually works, including the parts that are deliberately boring.
"AI analytics" gets pitched as a product when it is really a stack, and stacks are judged by their seams. Here is the architecture we run and sell, layer by layer, with the load-bearing decisions named, so you can steal the shape even if you never buy the product.
Layer one: a read-only tool surface
Everything sits on read-only access to all your ad and analytics accounts, such as Google Ads, Meta or GA4, with per-person sign-in; the same access powers the MCP server that is part of Upbuild’s toolset. Read-only is not a setting: Upbuild never asks for permission to change anything, which is what makes the rest of the stack safe to automate: nothing downstream can touch a campaign, because nothing upstream can. Every number in every layer traces to a logged read of your accounts.
Layer two: conversation
Ad-hoc analysis lives in AI Insights, a collaborative chat on all your marketing data, or in the assistant your team already uses (Claude, ChatGPT, Copilot Studio or Gemini Enterprise) through the MCP server: pacing checks, blended ROAS, anomaly archaeology. This layer answers the questions you did not know to pre-build, which is most of them, and it is where recurring needs reveal themselves; any answer can be added to a dashboard in one prompt.
Layer three: persistence
What proves recurring graduates into dashboards that refresh themselves, shared by private link and safe to hand a client, and get changed by asking Upbuild AI, with numbered versions you can restore. The design rule across this layer: the assistant drafts, a person publishes, and no change may silently shrink what a live page reads.
Dashboards, exports and alerts, built by asking.
Upbuild is an AI platform for marketing data: AI Dashboards, AI Insights, AI Data Exports and AI Alerts, with a read-only MCP server as part of its toolset that brings your accounts into Claude, ChatGPT, Copilot Studio and Gemini Enterprise. Every tool, live data. Free for 14 days on the connector and 7 days on the platform.
Get a demo Start free →Layer four: schedules and guards
Exports defined in chat land ready-to-use tables in your data warehouse, cloud storage or a spreadsheet on a schedule, with backfills of history, and alerts watch cross-platform conditions (blended CPA week over week, zero-conversion spend, a tag gone quiet), checked on every data refresh and delivered by email, Slack or Microsoft Teams. Both run on the same versioned definitions as the dashboards: one description of the data, three consumers, one history.
The governance spine
Company reality is organisations, not users. On your own you see exactly the accounts your logins already reach; as a member of an organisation on Upbuild you also inherit every account the company shares with you. Roles separate viewing from publishing, and every material action is recorded. If you evaluate a competing stack, press hardest here; chart quality is table stakes, custody of access is the product.
Buy the stack or build the shape
You can assemble a version of this from parts: a connector, a BI tool, a scheduler, glue. The argument for one system is the seams: the dashboard, the export and the alert reading the same tested definition through the same read-only access. Get a demo to see the platform live, no sales pitch, or start with the MCP server in the assistant you already use. Prefer to try it yourself? Start free.
Frequently asked questions
What is an AI marketing analytics stack?
Four layers on one data surface: read-only access to your ad and analytics accounts, AI Insights and the assistants for ad-hoc analysis, AI Dashboards that build and refresh themselves, and AI Data Exports plus AI Alerts defined by asking. Upbuild ships all four as one platform, with an MCP server as part of its toolset.
Do I need a data warehouse first?
No. Upbuild reads your platforms directly, so conversation, dashboards and alerts work from day one. A warehouse joins the stack when you need modelled joins with non-marketing data, and scheduled exports into your warehouse are how the two meet.
What should I evaluate in an enterprise AI analytics platform?
Whether access is read-only by construction, whether AI-made changes are versioned, tested and human-published, whether sharing is by private link to named people, and whether it says "I cannot see that" instead of inventing a number.