Nexora looks at this topic through the business process: what is happening now, where the loss occurs, how the solution is designed, and what result should be measured after implementation.
The topic
The more a company grows, the more applications it has. The problem is that the number of systems grows faster than their connectivity. In its Connectivity Benchmark 2025, MuleSoft wrote that an organization uses about 897 applications on average, and only 2% of companies had integrated more than half of their landscape.
In 2026 the average number of applications in the MuleSoft research grew to 957, with only 27% connected on average. 82% of IT leaders name data integration as one of the biggest problems for AI, and 86% believe that without proper integration AI agents can add more complexity than value.
This is not only an enterprise problem. Even a mid-size business can have a CRM, accounting, Google Sheets, email, a website, Telegram, advertising platforms, and a task manager that know nothing about each other.
How Nexora fits in
Integration is the core of Nexora. We determine which system is the source of truth for each entity: customer, deal, order, payment, project, document.
Then we build the data flow so that one system does not duplicate another. Make can be the orchestration layer, an API the system channel for exchange, Telegram the notification interface, and the CRM the commercial loop.
Our task is not to connect everything to everything but to create the minimum necessary architecture with clear data owners.
How we see it
Bad integration shows up not as a technical error but as a business symptom: an employee copies data, reports diverge, a customer gets two emails, a payment is seen late, and the CRM status does not match reality.
Point-to-point integrations can become a problem too. If every service is connected directly to five others, any change triggers a chain reaction. That is why interfaces, rules, and error handling have to be designed.
AI raises the requirements: an agent needs reliable access to data and actions. If the architecture is chaotic, it gets incomplete context or calls the wrong system.
How we solve it
- We build a map of systems and data flows.
- We define a source of truth for each entity.
- We choose a native integration, Make, a direct API, a webhook, or another method.
- We remove repeated entry and extra copies of data.
- We add retry, monitoring, idempotency, and an error log.
- We define ownership of integrations and the change process.
- We prepare the API and the data for future use by AI agents.
What improvement to expect
After implementation we measure the number of manual transfers, the frequency of desynchronization, data update time, the number of integration errors, and the time to fix them.
MuleSoft shows the scale of the problem: hundreds of applications per company and low connectivity. In such conditions even partial rationalization can have more effect than adding one more new system.
For Nexora, successful integration looks like this: an employee enters information once, the system passes it on by itself, and the manager sees the same state regardless of the interface.
