Verysell Group Applied AI Lab

If confusion can sound, you can hear from business the loudest. AI is everywhere. It’s a big buzzword. It’s a confusing noise. We recently asked David - Director of International Partnerships at VeryPay: Why’s there so much noise around AI and how can businesses get thru the hype? Just like the blockchain days, companies were pouring money into hiring blockchain teams and attending conferences. But no one really knew what they were doing. The buzz was huge, but the ROI? Not so much. Now, AI is following suit. Companies are jumping in fast, and the loud noise leaves many businesses scratching their heads. AI can bring huge value, but for many SME biz, the real question is: Is AI going to help solve my challenges? Because if it’s not, you’re just jumping on the hype train. So before diving in, you need someone to guide you through the clutter. AI isn’t a magic box that fixes everything. It’s a tool, but only if it solves real problems. Take a step back. Make sure AI fits before you invest!

What's next for AI in software testing? For the past few months, our geeks (Yurii Lozinskyi, Dao Huu Hung) have joined forces with Viacheslav Kostin 🔜 WEB Summit - CEO from WislaCode Solutions to find the answers. In this article, we provide insights into how AI can enhance productivity, quality, and business outcomes in software testing. Looking to improve your software testing process? Read on.

Matching random pitches with the right journalists at scale was a headache for our client SNP - A digital PR marketplace. But then we started working together. Before working with us, SNP team was: - struggled to aggregate the data (PR pitches) and make decisions (match it with the right journalists) - flooded with the huge amount of incoming data (pitches...) So we proposed a solution that allows the team to sit back, while AI will handle the matchmaking smoothly. - update UX/UI for better client experience: check - instantly analyze pitches and personalize content recommendations to journalist: check - build a codebase to integrate more AI functions and capabilities: check How do we go? ---We'll leave this for you to check below--- (a bit cheeky right? you are welcome) Think you have a problem that AI can solve? Let us help with the diagnosis. Drop us a DM or Yurii Lozinskyi and let's schedule a call.

Our Applied AI Lab team had an incredible time at #GITEX2024! It was a fantastic opportunity to connect with our partners, explore cutting-edge AI solutions, and uncover new avenues for collaboration. The energy at the event was truly inspiring, and we’re excited to build on the conversations we had. A special thank you to Captain Hoff (Steven Hoffman) for the inspiring and thought-provoking speech—your insights are paving the way for exciting advancements in the industry! Looking forward to the future of AI and tech partnerships as we continue driving innovation together! 💡✨ #AI #Innovation #TechFuture #GITEX #AppliedAILab

Data chaos is a real problem. So many sources. So little time. How do we make sense of it all? This week we discussed with David around the potential of AI to reshape how we analyze data. In the past, desktop research meant sifting through endless articles, databases, and documents. Now, with tools like ChatGPT and Perplexity, that process is easier. But there’s still a catch. What about browsing thru 101 documents on your SharePoint and a ton of other internal databases? These tools don’t update themselves, and AI solutions like ChatGPT are external. That’s exactly why VeryChat comes into play. We merge internal and external data sources in one secure environment. Then we ask AI to pull documents from SharePoint, cross-check them with external data, and give insights. That's how conversational AI works. A simple dashboard that lets you ask questions, pull relevant data, and deliver insights quickly, securely, and conversationally. No need to consult data researchers or data scientists. Just you, AI, and the answers you need, whenever and wherever you need them. Save time, money, and resources. That’s a happy day for your team!

In the 2010s, call centers were flooded with these 5 problems. Good news! In the 2020s, AI comes to the rescue. Here’s how AI-powered Customer Support is saving Verypay's team from dying inside. 1. High Call Volume & Long Wait Times: Managing large call volumes = long wait times, frustrating customers, and overburdening agents. → VeryPay’s Solution: An AI-powered system handles routine queries, automating the first line of support through text or voice interactions. By taking care of common issues instantly, the system significantly reduces the volume of calls that require human attention, cutting down wait times and improving overall customer satisfaction. 2. Agent Burnout: Repetitive tasks and constant pressure can lead to high levels of burnout and employee turnover. → VeryPay’s AI automates repetitive queries, allowing customer support agents to focus on more complex and meaningful interactions. This shift increases productivity and reduces the risk of burnout, helping agents work on higher-value tasks that keep them engaged and motivated. 3. Inconsistent Customer Experience: Delivering a consistent, high-quality customer experience can be challenging, especially with a large team of agents. → VeryPay’s AI Solution: The AI system provides standardized responses to routine questions, ensuring consistency across all customer interactions. With seamless integration, this can have access to up-to-date information and resources, ensuring a consistent and reliable support experience across the board. 4. Long Training and Onboarding Times: Training new agents to handle a wide range of customer queries is time-consuming and costly. → VeryPay’s Solution: By automating simple customer interactions, VeryPay’s AI system reduces the workload for new agents, allowing them to focus on more complex issues. The system also provides agents with comprehensive historical data, enabling quicker onboarding and improving agent performance from day one. 5. Data Overload: Call centers generate large amounts of data that can be difficult to track and analyze manually. → VeryPay’s Solution: VeryPay’s AI engine integrates data sources like Jira and Confluence, transforming them into a centralized knowledge management system. This allows call center managers to easily access performance metrics, customer data, and ticket histories, providing real-time insights for better decision-making and service improvements. What bottleneck in Customer Service Operations are you seeing? Share with us below 👇🏻.

We’ve been rambling about how AI reshapes Customer Support Ops. But here’s the real analysis of the AI-powered CS system we built for client VeryPay. …sharing so you can see a real-world transformation (see, we don’t just talk) 1. First Response Time (FRT): AI significantly reduces the time it takes to respond to a customer’s query. VeryPay’s AI system brings the FRT down to just 5-6 seconds—compared to the payment industry average of about a minute for chat and up to 2 minutes for voice calls. 2. Average Resolution Time (ART): AI helps resolve simple issues 2-3 times faster than the typical 24-hour industry standard. This means customers get their problems solved quicker, improving satisfaction. 3. Cost Per Ticket (CPT): AI-driven customer support lowers costs by reducing the need for human intervention. For VeryPay, CPT is at $1 per ticket, compared to $2-$5 for email or chat support and $8-$15 for voice interactions in a traditional system. 4. Ticket Volume and Backlog: AI reduces the ticket volume that human agents need to handle. While there may be an initial increase in unresolved tickets due to AI’s learning curve, the system is designed to keep this backlog under 5-6%, significantly lower than the industry’s benchmark. 5. Agent Productivity: By automating routine queries, AI frees up customer support agents to focus on more complex cases, potentially increasing their daily ticket resolutions from 20-30 to 40 tickets. Adopting AI doesn’t just change how support teams work. It optimizes performance, speeds up service, and lowers operational costs. The data doesn’t lie: AI is reshaping how customer service works. If saving operations costs and improving efficiency sounds like what your business needs, DM us “CS” and let’s chat.

Since 2015, conversational AI’s been researched by businesses. After OpenAI debuted ChatGPT in 2022, the world’s gone a bit frenzy. But here’s what our experts believe to be the future of Conversational AI. Business applications include custom AI agents such as virtual assistants, chatbots, and digital workers. Commercial applications can be packaged as AI SaaS (software as a service) or PLAAS (platform as a service). Both will share these directions: 📌 Hyper-Realistic Interactions: Emotional Intelligence: AI is getting more adept at understanding and responding to human emotions, making conversations feel more natural and empathetic. Multimodal Inputs: Conversational AI can now process information from various sources, such as text, voice, and visual cues, leading to more comprehensive and contextually relevant responses. 📌 Personalized Experiences: Tailored Conversations: AI can learn and adapt to individual preferences, providing highly personalized recommendations and experiences. This is nothing new right? Proactive Assistance: Conversational AI systems will anticipate users' needs and offer assistance before it's requested, creating a more seamless and intuitive interaction. 📌 Ethical Considerations: Bias Mitigation: As AI systems become more sophisticated, it will be crucial to address issues of bias and ensure that they are fair and equitable. Privacy and Security: Protecting user data will remain a top priority as conversational AI becomes more integrated into our daily lives. But we do see the future is filled with exciting possibilities. As technology evolves, we expect even more sophisticated and human-like AI systems that will change the interaction between with machines and humans. What are your thoughts on AI getting more emotionally intelligent?

Here’s an imaginably interesting future of AI (yep, imaginably): More tools will be built to handle boring tasks. What we see in the market now is: → Businesses build AI to assist their operational teams goodbye to those repetitive, mundane tasks. Employees in any position are empowered to make their jobs more fun and creative. They utilized AI to have all daily habits and boring tasks done on auto-pilot or semi-auto. A mundane job in the future is not gonna kill you inside anymore - it’s the play of being more effective.

A new report by Google Cloud shows that Generative AI is driving significant ROI across various business functions, And the results are eye-opening. From customer & field service to manufacturing & production processes, many sectors are already seeing immediate benefits, while others will start experiencing significant ROI within the next year or further down the line. The key takeaway? If you're in an industry like customer service or productivity, AI can deliver immediate results. But even if your sector is projected to benefit in a year or two, it's crucial to start planning accordingly now. Interested in learning how to harness AI for your business? Comment below and explore how we can help you stay ahead👇🏻.