As an e-commerce strategist, I’ve seen countless customer experience leaders struggle with a frustrating paradox: they are drowning in data but starving for wisdom. They have dashboards for every channel and tool, yet they can’t connect the dots to see the complete picture of their operation. This is precisely the challenge that Ardie Sameti and Rajeev Singh are tackling with their new venture, Scala, which just launched with $8.5 million in funding. Drawing from their deep operational experience scaling mission-critical service organizations like Accolade and Concur, they’ve built what they call an “operational intelligence platform” to bridge the dangerous gap between data and decisive action, especially as human agents and AI begin to work side-by-side.
We sat down with the founders to discuss how their platform moves beyond descriptive data to provide actionable diagnoses for complex contact center issues. They detailed how a unified intelligence layer can empower CX leaders to act more deliberately, particularly in managing the growing complexities of hybrid human-AI teams. The conversation also touched on how their firsthand experiences in high-stakes service environments directly informed Scala’s design and how their new funding will fuel their mission to redefine CX management for the AI era.
Many CX tools provide isolated data that describes issues but fails to diagnose root causes. Could you share a specific example of this limitation in a modern contact center and then detail how Scala’s operational intelligence approach provides a more actionable diagnosis?
Absolutely. It’s a pain point we know intimately. A classic example is seeing a sudden spike in customer wait times on a static dashboard. The tool tells you what happened—the wait time went up. But it offers zero insight into why. Was it a marketing campaign that just dropped? Is a specific AI bot failing and escalating everything to human agents? Are a group of new agents struggling with a particular issue? Leaders are left scrambling, trying to pull reports from five different systems to piece together a story. It’s frustrating and inefficient. Scala, by contrast, acts as a unified intelligence layer. It connects those dots automatically, showing that the spike in wait time correlates directly with a surge in inquiries about a new product, and that the chatbot’s failure rate on that topic is 80%. It can even pinpoint that the escalations are overwhelming a specific team. Instead of just a red number, you get a clear, connected diagnosis that tells you exactly where to focus your action.
Your platform is designed as a “unified operational intelligence layer.” For a CX leader managing multiple systems and channels, what does this mean in practice, and how does it help them act more deliberately than they could with traditional dashboards? Please provide a step-by-step example.
In practice, it means finally having a single source of truth that reflects how the operation actually behaves as a whole. Imagine a CX leader who sees their customer satisfaction score dip. Step one with traditional tools is a frantic investigation. They’d pull data from their CRM, their call center software, their chatbot analytics, and maybe even their workforce management tool. By the time they have a hypothesis, hours or days have passed. With Scala, that process is transformed. The leader logs in and sees the CSAT dip, but our platform has already connected it to other events. It might show that the dip is concentrated in one channel—say, post-chat surveys. It then links that to a group of agents who recently completed a new training module, and it might even flag that their average handle time has increased because they’re struggling to apply the new process. The leader can then act deliberately, providing targeted coaching to that specific group, rather than issuing a vague, company-wide directive. It’s about moving from reactive fire-fighting to proactive, focused improvement.
As contact centers increasingly blend human agents with AI, what new operational complexities arise? Can you elaborate on how your platform helps leaders understand this interplay and optimize performance across both human and automated systems? We’d love to hear an anecdote illustrating this.
The blend of humans and AI creates a whole new set of hidden complexities. For instance, a company might deploy a new chatbot that successfully deflects 30% of incoming queries. On the surface, that’s a huge win. But what often happens is that the remaining 70% of issues that reach human agents are now the most difficult, emotionally charged problems. We saw this at a previous organization where agent morale plummeted and burnout skyrocketed. The dashboards just showed that agent handle times were going up and their satisfaction scores were dropping, making it look like a performance issue. In reality, the AI was doing its job, but it was fundamentally changing the nature of the human agent’s job without anyone realizing the downstream impact. Scala is built to surface these exact kinds of interdependencies. It would show that while the AI deflection rate is high, the sentiment of escalated conversations is increasingly negative and the complexity of keywords is rising, giving leaders the true context to support their human teams effectively.
Your team’s background includes scaling high-stakes service organizations like Accolade and Concur. How did those firsthand experiences with the “gap between data and action” directly shape Scala’s core features and overall design philosophy? Please share specific insights from your past roles.
Those experiences are the entire reason Scala exists. At Accolade, we were supporting millions of members with incredibly high-stakes healthcare interactions. A delay or a misunderstanding wasn’t just an inconvenience; it could have serious consequences for someone’s health. We had tons of data, but it was trapped in different systems. We could see that a member was calling repeatedly, but connecting that to their recent clinical visit or a confusing benefits document was a manual, painful process. We lived that gap between data and action, and it was a constant source of operational friction. Similarly, scaling Concur to be acquired by SAP for $8.3 billion taught us the immense challenge of maintaining service quality in a hyper-growth environment. You can’t just throw more bodies at the problem. You need an intelligent, holistic view of your operation. This philosophy is baked into Scala’s DNA—it’s not another point solution, but a foundational layer designed to provide the clarity and connectivity we so desperately needed in our past roles.
With $8.5 million in new funding from Madrona and FUSE, what are the most critical priorities for Scala over the next 12-18 months? Could you outline the key product development milestones and go-to-market strategies you plan to execute with this capital infusion?
We’re incredibly focused. This $8.5 million infusion is about execution and acceleration. Our number one priority is product development. We are laser-focused on building out the core platform to deliver on the promise of a truly unified operational intelligence layer. This means deepening our integration capabilities with the complex ecosystem of tools modern contact centers use and enhancing our AI-driven diagnostic engine to provide even more sophisticated insights. On the go-to-market side, our strategy is to partner with forward-thinking CX leaders who are already feeling the pain of managing a hybrid human-AI workforce. They understand the limitations of their fragmented tools and are actively seeking a better way. The strong backing from Madrona and FUSE, who share our vision, gives us the runway to be deliberate and build a truly transformative, holistic service solution.
What is your forecast for the future of AI-human collaboration in customer experience?
Our forecast is that the most successful organizations will be those that view AI not as a replacement for humans, but as a powerful collaborator that elevates them. The future isn’t a sea of chatbots; it’s a world where AI handles the repetitive, predictable tasks, freeing up human agents to focus on the complex, empathetic, and relationship-building interactions that truly drive loyalty. However, this beautiful synergy can’t happen in a vacuum. It requires a new kind of operational backbone—an intelligence layer that can manage the handoffs, understand the performance of the entire hybrid system, and give leaders the insight to coach and support both their human and AI teams. The winners will be the ones who master this collaboration, and we believe that intentional, data-driven operational management will be the key to unlocking its full potential.
