Nitesh Nandy

Hiver is an AI-powered customer service platform that centralizes support across email, chat, voice, WhatsApp, and social, giving teams one place to manage conversations, coordinate ownership, enforce SLAs, and automate workflows so support operations stay consistent as they scale.

As the co-founder and CTO, I’m involved in building the platform from the ground up - from early architecture to scaling both the product and the engineering organization. A lot of my work has focused on getting the fundamentals right so the system stays reliable and easy to use as it grows in complexity.

More recently, my focus has been on how AI fits into these systems in a practical way. In support environments, workflows are already defined through how teams assign ownership, prioritize requests, and trigger actions. We build systems that learn from those patterns and apply them consistently, so teams see value without needing to change how they operate.

I spend most of my time on how the system defines and executes workflows end to end, from how incoming requests are interpreted to how ownership, SLAs, and downstream actions are enforced across the platform.

At Hiver, these flows run across thousands of conversations every day, so the focus is on making sure the system behaves consistently across teams that structure their workflows differently.

In practice, small inconsistencies early in the workflow can send a request down the wrong path, and those issues usually surface much later in the system as delays or missed SLAs.

The core challenge is coordination. As teams grow, more time goes into managing how work moves through the system than actually resolving issues. That includes reassignments, fixing workflows, and handling breakdowns between steps.

The constraint is that teams are not starting from scratch. They already have established ways of working. If a system forces them to redesign those workflows, adoption slows down immediately.

So the approach has been to work with existing behavior and make it more consistent, rather than introducing new processes that teams have to adapt to.

One thing that becomes clear in production is that systems drift unless they are grounded in real usage. If an AI system is not continuously learning from how teams actually work, it starts making decisions that look correct in isolation but don’t hold up operationally.

We’ve seen this when workflows evolve but the system doesn’t keep up. Something that worked well initially starts making slightly off decisions as teams change how they operate, and those errors only become visible later in routing or missed SLAs.

Another is that most issues are not obvious. A system can perform well on average but still create downstream problems through small inconsistencies. These show up over time, not in testing.

The key is to evaluate AI as part of the full workflow.

The most visible impact has been in reducing the amount of coordination required before work begins. When the system handles early-stage decisions more consistently, fewer conversations need to be reassigned or corrected later.

For example, we’ve seen cases where a request is routed incorrectly early on and then moves through multiple steps before anyone catches it. By the time it’s fixed, the delay has already impacted response time and SLAs. When those early decisions become consistent, that entire chain of corrections disappears.

In day-to-day terms, teams spend less time triaging queues, fewer workflows fail silently, and there is less back-and-forth before a request is picked up. We’ve seen this with teams like Rush Order, where improving how work is structured and routed led to 20% faster response times, a 10% reduction in ticket volume, and a measurable increase in customer satisfaction.

Where expectations fall short is around autonomy. Real workflows have a lot of variation, and small differences in inputs or team processes create edge cases quickly. Systems that try to handle everything tend to break in ways that are hard to detect. The more reliable approach has been to automate repeatable patterns and keep humans involved where variability is high.

As these systems scale across teams, more effort will go into keeping them aligned with how work actually flows through the product.

Decisions made early in the system influence ownership, response handling, and downstream execution. As usage grows across teams with different processes, maintaining consistency becomes an ongoing operational challenge.

The focus will be on making these systems adapt to real usage and remain stable over time, rather than expanding surface-level capabilities.