AI that does the work, not AI that demos well
AI agents wired into the systems you already run.
You will recognise at least one of these
- You have been asked to have an AI strategy and want a real one
- Your team is copying between an AI tool and the CRM by hand
- You tried an AI pilot and it never left the pilot
- You need to know where the data goes before you can approve anything
Most AI pilots stall because the model was never connected to anything. It drafts in a browser tab while the CRM stays untouched, and nobody trusts it enough to let it act. Add thin local data and questions about where records are allowed to sit, and the pilot quietly ends.
What we actually build
We start from a task that costs real hours, not from the model. The agent gets the context it needs, the tools to act on your stack, a confidence threshold, and a human gate on anything expensive to undo. It runs inside n8n or the CRM you already use, so the output lands where the work happens.
The stack
- Claude
- Anthropic API
- n8n
- MCP
- HubSpot
Every system ships documented and built to run without us. The goal is that you do not need Zuun after 90 days.
What changes
- Research and qualification handled before a human opens the record
- Model choice swappable, so cost is a decision rather than a lock in
- Every irreversible action gated by a human by default
Real numbers from real builds are on the case studies. We do not quote figures that did not happen.
AI Implementation, answered
Is this just a chatbot?
No. A chatbot answers questions. What we build does work: reads a document, decides what it means, updates a record, drafts the follow up, escalates when it is unsure. The interesting part is the deciding, not the chatting.
Which model do you use?
Whichever one fits the job and the budget. Claude for most reasoning work, cheaper models for classification and extraction. We build so the model can be swapped, because it will be.
What about data leaving the country?
It is a real question in several African markets, and it is worth asking before anything is built. We scope where data sits, which providers are acceptable, and what has to stay local. Some builds change shape because of it.
What if it gets something wrong?
It will, sometimes. Every agent we ship has a confidence threshold and a human gate on anything that is expensive to undo. Full autonomy on a reversible task, approval on an irreversible one.
The other four
- Outbound Engine: Full-stack cold outbound: ICP scoring to CRM delivery.
- Content Automation: New content in → LinkedIn, newsletter, and threads out.
- Social Listeners: LinkedIn signal monitoring: catch buying intent in real time.
- Bespoke Systems: Custom multi-system workflows for problems with no off-the-shelf answer.
- Ads & Influencer Intel: Segment influencer audiences by purchase intent before you spend.
Ready to build this?
We scope it on a call and start building. No decks, no six week discovery phase.