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Evolutionary real-world
case studies

As the industry’s pioneering customer experience automation tool, Onix’s innovation center can adapt to a variety of complex, specific customer needs. Here are some of its real-world customer success stories:

Problem

As a leading global data and analytics company, Equifax needed to modernize its aging legacy systems, but faced the challenges of highly-regulated security constraints and massive organizational complexity. As an off-the-shelf bot would immediately fail security audits, the customer needed a highly secure, repeatable factory for enterprise innovation.

Our approach

 Instead of just building a one-point solution, Onix built an entire framework designed for safe, governed experimentation. By mirroring the "innovation hub" model, Onix created a secure foundation and then rapidly scaled based on concrete evidence of success.

Outcome

The customer successfully scaled from a single use case to over 130+ distinct, active use cases, thus effectively turning a purely defensive security posture into an offensive, agile innovation engine.

Lesson learned

True innovation isn't a one-time, painful migration project, but a repeatable, operational capability that must be carefully tailored to the strict realities of your environment.

Problem

As a global healthcare research platform, Metricwire was unable to manage highly dynamic, constantly shifting clinical research schedules. Manual scheduling by human staff was slow and highly error-prone due to complex variables, including participant availability, strict clinical protocols, and diverse researcher needs.

Our approach

 Human brains cannot efficiently scale this type of multi-variable logic, which is only possible for intelligent agents. We utilized our internal "Raven" persona to build a hyper-specific internal customer solution uniquely suited for their clinical microclimate.

Solution

By implementing a sophisticated internal Agentic AI system, Onix enabled an internal DCW  as an intelligent backend assistant. This assistant could autonomously generate accurate schedules in real-time, after instantly considering and resolving hundreds of complex internal variables simultaneously.

Problem

As a global healthcare research platform, Metricwire was unable to manage highly dynamic, constantly shifting clinical research schedules. Manual scheduling by human staff was slow and highly error-prone due to complex variables, including participant availability, strict clinical protocols, and diverse researcher needs.

Outcome

90% scheduling accuracy with <1 second latency, thus completely eliminating human bottlenecks.

accuracy in scheduling

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less than 1 second latency
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Legal:

"Review this draft Master Services Agreement and flag any clauses that deviate from our standard liability and indemnification requirements."

Problem

A company cannot build a highly responsive external CX system using fragmented internal data. As a leading Canadian telecom company, the client had a massive 14PB of data stored across dozens of “siloed” legacy systems, thus making it impossible to deliver a unified, 360-degree customer experience.

Our approach

 By utilizing our Raven (code conversion) and Pelican (data validation) methodologies, Onix’s innovation center managed to thoroughly cleanse and structure the data, thus creating a secure, AI-ready foundation.

Solution

Onix implemented a massive, highly orchestrated cloud modernization solution that successfully unified over 100 disparate data sources. This scale of critical internal "plumbing" work was the absolute prerequisite before building their advanced virtual agents for customer care.

Outcome

By delivering a self-serve analytics platform, the client launched highly intelligent, context-aware new GECX agents faster, thus leading to a massive 44% increase in first-call resolution (FCR).

Modernizing over 200 data pipelines

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Over 100 record systems

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14PB data

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Lesson learned

Real, transformative innovation always starts deep below the surface. Our internal DCWs fixed the foundational internal data so that external virtual agents could operate on clean data as the “fuel.”

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