Examples of the systems we build, from real client work.
These reflect work delivered for a UAE-based B2B fintech serving thousands of SMEs across the region. The client name is withheld for now. Specific outcome numbers are being confirmed and run as directional language until then.
One source of truth, and a clear path to AI.
As the company grew past thousands of customers across the region, its tools grew faster than the data layer beneath them. Five customer-facing tools ran in parallel: sales CRM, a retention platform, support, marketing automation, and WhatsApp. Each held its own view of the customer. None agreed. The team could not reliably spot at-risk accounts. Sales could not size accounts at handoff. Marketing could not fire on product behaviour. The question "how is the customer base really doing?" got a different answer depending on who you asked.
The tools were fine. The layer beneath them had never been built. There was no shared definition of a customer, no matched records across tools, no shared record of what customers did, and no controlled way to push useful data back into the tools the team worked in. This is the exact problem that makes AI projects stall. Scattered data that no model can reason over.
Over three years, in step with the company's growth, we built one customer data layer. Segment as the event bus, BigQuery as the warehouse, n8n for orchestration, and Hex for analysis. It joined all five customer tools and the product database into one matched view of the customer, with clean syncs pushing useful data back where teams needed it.
One source of truth for the customer. Real-time health scoring, reliable reporting, and any future AI project could finally run on it. The foundation did not just fix reporting. It removed the blocker between the company and everything it wanted to do with its data next.
Less churn, faster activation.
For the same fast-growing fintech, the work after the sale could not keep up with growth. At-risk customers showed up too late to save. New customers stalled before reaching value. The team spent its time on manual triage instead of the accounts that needed it most.
With the data foundation in place, the signals to act on were finally there. They just were not being turned into a system. Health was a feeling. Activation was not tracked. And there was no automatic path from "this account is slipping" to "someone is doing something about it."
A live health and activation system on top of the data layer. A daily health-score model that combined product usage, support, and payment signals. Churn alerts sent to the right owner while there was still time to act. And a tracked activation flow aimed at the gap between sign-up and first real value.
The company materially cut the revenue lost at renewal. More new customers reached value sooner. And the team moved from manual triage to proactive work on the accounts that mattered, handling more accounts without adding headcount.
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