One question that crossed three systems

8D Hubify is one of the largest RevOps agencies in Brazil, an RD Station Diamond partner born from the merger of 8D Pro and Hubify and recently joined by Dialetto. For an agency that runs marketing, sales and revenue operations for its clients, one question kept costing hours: did this ad actually turn into revenue for this client? Answering it meant pulling paid media, the CRM and billing together, often client by client. A Nekt customer since May 2026, the team rebuilt that work on a single governed layer in a little over three months.

What 8D Hubify built on Nekt

• A per-client data architecture. Paid and organic media, the CRM and other sources connected in one place for each client, and in some cases several units of the same operation consolidated into a single view.
• Attribution all the way to revenue. Marketing, CRM and billing are crossed to link a campaign to the money that actually came in, rather than stopping at the lead or at ROAS.
• Conversational data in the same context. Templates, campaigns and events from customer-service platforms become structured data for analysis and decisions.
• A RevOps agency doing RevOps on itself. An MRR waterfall, GRR, NRR, quick ratio and Bowtie built from its own financial and operational data.
• AI agents over MCP. The team uses Nekt's MCP to put all of this context at the disposal of AI agents, which reason over marketing, sales, revenue and RevOps alongside the team in natural language, through Claude and others.

The systems behind it

Twenty-four data sources feed the layer, unified and modeled rather than left as raw exports. Among the categories:

Built by the agency's own team

Twenty-four sources and forty-five transformations, in a little over three months, built entirely by 8D Hubify's own team rather than outsourced. For an agency, that ownership is the point: the same per-client architecture that attributes a client's ads to revenue also runs the agency's own RevOps, and both feed the AI agents that answer in natural language. Nekt is not only their analytics layer; it is the context infrastructure that connects client data, the agency's own data, and the agents that work across all of it. The approach is the worked example in how to build a connected GTM tech stack, applied per client as in how an agency runs AI agents across many clients, with meaning carried alongside the data through context engineering and a governed MCP server for data.