Case Study

Finding the right technology partner: CVector's path from industrial data concept to paying customers

Fast-growing energy tech startup CVector faces a challenge common to many early-stage companies: building enterprise-grade infrastructure from scratch while racing to capture market opportunities, creating inherent complexities that must be navigated carefully. Working with an early proof-of-concept that utilized multiple third-party integrations, CVector's founding team recognized the opportunity to bring in senior technical expertise to transform their initial prototype into a production-ready platform.

-95%
in data analysis time
Engineering team
ready in 2 weeks
Human error
completely eliminated
01
100% commitment delivery on time
02
Reduced onboarding time by 50%
03
Expertise in Delivery Management

About LIFTE H2

/  Global fast growing hydrogen  infrastructure development company
/  Team of 40 people
/  Building projects globally

About

CVector

CVector is a data analytics platform built specifically for industrial operators in the energy sector. Founded by a team of energy industry insiders, CVector addresses the critical challenge of outdated and fragmented data systems that energy teams face on a daily basis.

Challenges

CVector needed to aggregate complex industrial sensor data from SCADA systems and transform it into actionable insights for energy companies. The technical requirements were substantial: processing massive volumes of data that sometimes arrived in microseconds rather than milliseconds, particularly for critical electricity monitoring systems where timing precision is essential for operational success.

Their existing proof-of-concept utilized multiple third-party integrations, which naturally introduced complexity and presented opportunities for architectural optimization.

As a focused startup team, they were navigating the dynamic nature of early-stage development - where priorities shift rapidly based on market feedback and client needs. The challenge was maintaining technical excellence while adapting to evolving requirements, building robust data separation capabilities, and preparing for real-time processing demands that future clients would require.

A key success factor was finding a technology partner who could seamlessly integrate with CVector's startup culture, embracing the fast-paced, iterative approach while providing the senior technical expertise needed to transform their vision into a production-ready platform.

Solutions

Semantive brought senior development expertise and strategic architectural guidance to support CVector's scaling objectives and infrastructure evolution.

Together, we implemented a scalable data processing pipeline using industrial protocols, with efficient data serialization and queue management. Our collaboration focused on optimizing the database architecture for time-series data and integrating analytical models into the web application. We built a comprehensive web interface from the ground up, featuring real-time dashboards for complex industrial data visualization, hierarchical data management for organizing sensor inputs across multiple sites, and robust user management systems designed for enterprise clients.

We also facilitated a strategic migration from existing third-party services to AWS infrastructure, including the transition to AWS services and the implementation of comprehensive multi-tenant data separation to support client onboarding.

Outcomes

The collaboration enabled CVector to successfully deliver an MVP that achieved ISO certification and onboard their first clients with real-time data streaming capabilities - a significant milestone for industrial applications.

The platform now efficiently processes high-frequency industrial data and presents it through web-based dashboards with integrated market data overlays. The architectural improvements positioned CVector for scalable growth, with a clear roadmap for physical IoT controllers and expanded client capabilities.

The strategic migration to AWS infrastructure streamlined operational complexity and optimized costs while maintaining the flexibility essential for their startup environment and evolving client requirements.

What’s next?

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