In today’s dynamic business landscape, software adoption is moving at lightning speed, like never before. Thanks to incredible advancements and Moore’s law, computation and storage costs have plummeted. Writing software is easier and faster than ever with open source, libraries, frameworks, and cloud-based platforms to deploy. We witness these advances in our daily life with consumer services such as WhatsApp, Amazon, streaming services, travel booking websites, and more.
However, when it comes to Enterprise B2B business applications rollouts, delays and budget overruns seem to be the norm. Even during the production phase, the solution frequently falls short of anticipated outcomes. The problem is compounded by the long waiting periods that often span months. And by the final rollout takes place, the user needs and requirements become obsolete.
The culprit behind this mess is the way B2B applications are developed and implemented. Customers have distinct business requirements and workflows, that need to be supported with their unique technology stack, data quality, and catalog availability. Bridging the user requirements with the reality of their environment to deliver high-value impact is never easy. To make matters worse, the development and implementation teams have limited domain or technical expertise. This challenge is much more profound in the case of Artificial Intelligence (AI) and Machine Learning (ML) due to their inherent instability and the probabilistic nature of algorithms. The AI and ML approach involves a trial-and-error process to fine-tune the models and achieve the desired outcomes. Success also depends on ensuring that the data is both relevant and cleansed of possible biases before any results can be delivered. In fact, studies have found that 80% of the models fail to perform and are scrapped before an ML model is deployed.
On the other hand, most businesses encounter similar challenges in areas of demand forecasting, lead generation, customer acquisition, call summarization, and more. In such a scenario, why not extract reusable steps, frameworks, design patterns, and domain knowledge to cut short the time to roll-out and improve success rate? That is exactly the approach we have taken, and the results speak for themselves.
At ElectrifAi, we chose not to reinvent the wheel. In fact, we rewrite the rules of AI and ML implementation by moving from one-off development projects to leveraging proven, reusable, pre-built ML and pre-trained AI models coupled with data abstraction and processing layer specific to the business problem for a particular industry. We start with a specific business problem and address it with smarter, faster, and pre-built ML solutions that drive real results in just 6–8 weeks.
In addition, it is essential for enterprises to have deep domain knowledge to efficiently solve complex business problems. The potential for cross-vertical innovation, i.e., embracing unconventional perspectives and borrowing best practices from diverse domains, paves the way for breakthrough outcomes. Moreover, the shortage of experienced ML engineers and data scientists is a collective concern for global enterprises. With high demand and limited supply, developing robust ML strategies becomes even more challenging.
On the other hand, ElectrifAi’s pre-built, containerized ML solutions reduce the implementation timeline to just 6 to 8 weeks, all at a fraction of the cost. The risk factors are mitigated through rigorous testing and validation, ensuring that what you get is tried, tested, and proven. Our methodology revolves around a crystal-clear approach: you agree to the key performance indicators we deliver against that. This approach not only cuts down the implementation time and cost but also guarantees value that is tangible and trustworthy.
Say goodbye to making heavy investments from scratch with uncertain outcomes. Bring pre-built, containerized solutions to guarantee predictable results without replacing your existing systems. Deploy effortlessly on-premises or as a hosted service while seamlessly supporting your existing platforms.
Look at how we helped one of our global financial client’s contact centers to elevate customer experience and unlock US$20 million in annual savings through call summarization and sentiment analysis:
The client operated a massive travel and loyalty platform. They sought to boost their agent quality scores, aiming to enhance customer satisfaction, net promoter scores, and service level agreements. However, their agent scoring was operated manually. Supervisors could only evaluate a couple of calls per agent each month, leading to scalability issues, inaccuracies, and inconsistent insights. The client wanted a game-changing solution that could also amp up productivity without compromising on KPIs.
Enter ContactCenterAi - our proprietary, pre-built, and containerized ML solution! ContactCenterAi didn't just rev up call scoring and sentiment analysis, but also provided an insightful performance evaluation for agents based on key quality parameters. Now, fast and accurate feedback was at their fingertips. The voice-to-text solution worked wonders, generating valuable agent insights for coaching and training, elevating customer satisfaction, and enhancing the KPIs. Moreover, our call summarization solution automated call summaries, unleashing productivity like never before.
With ContactCenterAi by their side, the client tapped into the full potential of AI and pre-built ML solutions, creating unforgettable customer experiences and seamless operational efficiency. The engagement saved them $20 million annually, all thanks to just 2 minutes saved per call through call summarization. Voice-to-text accuracy shot up from 70% to 90%, while they aced sentiment analysis, correlating over 87% with customer calls.
The future of business lies in the hands of those who embrace innovation fearlessly and make data their greatest ally. Our pre-built ML solutions epitomize efficiency, delivering tangible results at lightning speed – in just 6-8 weeks! Gone are the days of lengthy integration projects; say hello to actionable and smart insights that revolutionize decision-making. It’s time to unlock the true potential of enterprise data and lead the charge towards a data-driven, AI-powered, and future-ready business operations.
Sounds exciting? Contact us today to understand how we can create this value for you in 6-8 weeks!
ElectrifAi: US' leading ML solutions provider
ElectrifAi is one of the US' leading ML solutions providers, with an extensive library of pre-built ML solutions enabling our clients to capture tangible benefits quickly. We work with the C-suite to understand and solve business problems through data and machine learning in diverse industries such as Higher Ed, BFSI, Life Sciences, Hospitality, Retail, Chemicals, Oil & Gas, Telecom, Insurance, etc. The insights generated by our ML solutions, such as ContactCenterAi, ReputationAi, InventoryOptimizationAi, RevCaptureAi and other solutions, have helped our clients realize consequential business outcomes in 6-8 weeks.
Our solution does not require investment in a new platform or infrastructure. Instead, we leverage the data existing in your system to power the ML models to deliver business outcomes.
We are the last-mile solution that sits on the top to solve specific business problems and bring about savings. Contact us to learn more!