Background
A fast-growing SaaS provider in the UK supported more than 5000+ enterprise customers and relied heavily on Salesforce as its primary CRM. Over the years, the company had integrated dozens of tools, automated workflows, and operated across multiple regions. But despite their scale—and thousands spent on technology—one fundamental issue never improved: Their data was never clean.
Challenges
Every attempt to launch an AI initiative, build automation, or run accurate forecasting hit the same wall: Broken, inconsistent, and unreliable customer data. During Zenotris’ initial audit, the following problems were uncovered:
- Missing fields across critical customer and deal objects
- Severe duplication in contacts, accounts, and leads
- Sales stages that didn’t reflect the real process
- Inconsistent naming conventions between teams and regions
- Pipeline data that looked promising on paper but fell apart under analysis
Leadership kept repeating the same thing: “Once we clean our data, we’ll finally implement AI.” But months passed. Tools were purchased, consultants were hired, and dashboards were built—yet data quality never got better. The company was stuck in the classic loop: Waiting for clean data before modernising their operations—never realising that clean data was never going to magically happen on its own.
Today’s logistics economy rewards innovators. Technology is no longer considered a luxury; it is the foundation of competitiveness. Those who use tools like process intelligence increase their resilience, visibility, and agility. These characteristics are crucial in marketplaces experiencing change, uncertainty, and increased customer demands.
Firms that fail to innovate risk falling behind. Manual oversight cannot keep up with the complexities of global logistics. While competitors streamline and optimize, laggards lose money, customers, and reputation. In a business where every second counts, inefficiency may soon become the costliest liability.
Technology enables logistics organizations to not only survive, but grow. Businesses achieve long-term growth by combining data and action. others that act early have a competitive advantage over others who wait until inefficiencies destroy value.
Why They Turned to Zenotris
Zenotris brought a fundamentally different approach. Instead of forcing the organisation to fix data manually (a losing battle in large organisations), Zenotris introduced a Process Intelligence–driven Data Knowledge Layer.
This approach allowed the company to:
1. Unify Data Across All Operational Sources
Salesforce, marketing automation tools, billing systems, support platforms, and local market CRMs were consolidated into a single process-aware data structure.
2. Automatically Clean & Normalize Data Using Process Intelligence
Zenotris applied rule-based and behaviour-based cleansing, resolving duplicates, detecting errors, and standardising fields based on real process patterns.
3. Learn From Real Top Performers
Using process mining and AI, Zenotris identified the actual behaviours of the company’s highest-performing sales teams—not the theoretical workflows defined in SOPs.
4. Build a Reusable Knowledge Graph for AI Sales & Marketing Agents
Instead of feeding raw CRM data into AI models, Zenotris provided a pre-processed, context-rich, continuously updated graph, ensuring agents operate on accurate insights.
The Outcome
Before Zenotris, the company believed their messy data prevented AI adoption.
After Zenotris, messy data was no longer a barrier—it became an input for smarter automation.
Conclusion
Most European enterprises struggle with messy data—and waiting for “perfect data” only delays progress.
Zenotris solves this by building scalable, process-aware data foundations that AI can trust—even when CRM data is chaotic.
If your organisation is also stuck in the cycle of:
“We’ll implement AI once we fix our data…”
It’s time to break it.
Zenotris helps enterprises move forward—even with imperfect data.
Turn your messy operations into intelligent, unified, and AI-ready systems.