Agentic AI
AI agents are only as good as the data and measurement underneath them. Everyone can rent the same model; almost nobody has the foundations that make an agent trustworthy.
We build custom MCPs, agentic workflows and agent-ready websites on clean, measured, consent-safe data. Engineering first, hype never. And we apply it to ourselves first, on our own site and on our product, Upbuild.
What we do
Custom MCP Development
The Model Context Protocol is the standard way to plug your data into Claude, ChatGPT, Copilot and Gemini. We design and build MCP servers over your warehouse, CRM and internal APIs, so your team can ask in plain language and get live answers from their own systems. Our product Upbuild is exactly this, in production, across ten ad platforms.
Design & build
A good MCP is not a mirror of your API: it is a short menu of well-described tools an assistant picks correctly. We define the tools, the schemas and the limits, and ship it into the assistants your team already uses.
Auth done properly
The hard part is not the tool code, it is making sure each user only sees their own data. Per-user OAuth, expiring tokens, read-only scopes by default and no shared keys.
Agentic Workflows
Agents that do marketing ops work with guardrails: the weekly report drafted from live data, campaign QA, budget-pacing alerts with proposed reallocations. The agent drafts and executes; your team approves. The centaur model, applied with judgement.
Draft, then approve
Nothing ships without human review. Agents take the work to the last step and stop right before sending, publishing or moving budget.
Guardrails first
Every workflow starts with explicit limits: what it can touch, how much it can spend, who it alerts and what gets logged. Autonomy is earned with track record, not granted on day one.
Upbuild: ask your ad stack
Upbuild plugs Google Ads, Google Analytics, GTM, CM360, DV360, Meta, TikTok, Snapchat and Adobe straight into Claude, ChatGPT, Copilot Studio and Gemini Enterprise. Fifty-five tools, every one read-only, answering with live data from your own accounts. 14-day free trial at upbuild.app.
In your assistant, in two minutes
No SDKs, no service accounts, no IT ticket. Paste one URL, sign in with Google and ask. You only ever see the accounts your logins already have access to.
Read-only by design
Upbuild can see your data but never touch your campaigns. It is the proof of how we build: minimal access, per-user permissions and no surprises.
Agentic Media Operations
Platform agents (Performance Max, Advantage+) optimise toward the platform’s own numbers. We put agents above them: agent-assisted campaign builds from the brief, with naming, UTMs and tracking verified before a euro is spent, and an independent measurement layer refereeing what the platforms claim.
Brief to build
The agent assembles the structure, applies the naming convention, maps UTMs and checks the tracking. The team reviews and launches. Zero unverified launches.
Referee the platforms
Autonomy without independent measurement is faith. With sGTM, MMM and incrementality underneath, automated optimisation gets a referee with no stake in the result.
Agent-Ready Web & GEO
A third audience has sat down at the table: AI agents reading, citing and operating your website on your customers’ behalf. We make your site legible and usable to them: llms.txt, structured data, WebMCP tools on your highest-value flows and visibility in AI answers. We did it to our own site first, piece by piece, with the guides published on the blog.
Readable and usable
From signposting (llms.txt, schema.org) to action (WebMCP tools agents can call on your page). The goal is not just being in the answers: it is an agent completing the task.
Measured, not guessed
Agent traffic hides inside Direct today. We instrument agent calls into your analytics, consent-gated, so you can see what agents actually do.
Conversational Analytics
Assistants over GA4 and your warehouse that the whole team actually uses. The difference between a demo and a tool is the semantic layer: shared metric definitions so the answer is right, every time. We build both, and train the team so adoption survives past week one.
Warehouse answers
From BigQuery to the plain-language question, with the right tools underneath: MCP, definitions and permissions. No exports, no SQL for the people who do not need it.
Semantic layer first
If “active customer” means three things across three teams, the assistant will answer three things. We settle the definitions before switching anything on.
Agent Measurement & Governance
Everyone sells agents; almost nobody sells knowing whether they work. We measure agent traffic and agent-driven conversions, evaluate output quality, and build the consent-compliant instrumentation and guardrails that putting AI in front of real customers demands.
See agent traffic
Agent events in GA4, segmented and consent-gated: which tools get called, on which pages, ahead of which conversions.
Guardrails and compliance
Output-quality evaluation, decision logging and explicit limits, aligned with the EU AI Act and your own data policy.
The Agentic Readiness Audit
Two weeks, fixed fee. Three scores: data readiness, workflow readiness and the agent-readiness of your web. A prioritised roadmap you can execute with us or without us.
Book the audit ↓Questions? A project in mind? Talk to us.
- Analytics, tracking or data questions: real humans answer, fast
- Enterprise: audits, sGTM, GA4 360, BigQuery and MMM engagements
- Tell us your stack and goals, we reply within one business day