Niraj Ranjan, Founder and CEO of Hiver – Interview Series

Niraj Ranjan

, founder and CEO of Hiver, is an experienced entrepreneur and technologist who has built his career at the intersection of software engineering, product development, and customer experience. He founded Hiver in 2017 to reimagine customer service software, drawing on his earlier experience co-founding Mobicules, where he scaled the company from a small team to a 35-person operation while working hands-on as both a programmer and architect. Prior to entrepreneurship, he spent nearly five years at Mentor Graphics developing advanced emulation software for FPGA-based systems, an experience that shaped his approach to building high-performance, scalable products and fostering strong engineering cultures.

Hiver

is a modern AI-powered customer service platform designed to unify communication channels such as email, chat, voice, and messaging into a single workspace. It enables teams to manage shared inboxes, automate workflows, and collaborate in real time while AI handles repetitive tasks like ticket routing, response drafting, and data analysis. The platform is built to replace legacy helpdesk systems with a more intuitive and scalable solution, helping organizations improve response times, track performance metrics, and deliver consistent customer experiences across channels, and is trusted by more than 10,000 teams globally.

Early in your career at Mentor Graphics, you worked on advanced hardware emulation systems used to simulate complex electronic designs before they are physically built. Later, you co-founded and scaled Mobicules from a three-person startup to a 35-person company before launching Hiver. How did those deep technical foundations and early scaling experiences shape your approach to building AI that performs reliably in real-world, high-pressure support environments?

Working on hardware emulation systems shapes how you think about reliability. Those systems exist because complex designs behave differently once they encounter real conditions. Edge cases appear, interactions between components change outcomes, and the clean model breaks down. That mindset carries overectly to customer support environments. Conversations arrive with missing context, emotional urgency, and dependencies across several internal systems.

Scaling a company exposes another layer of complexity. As teams grow, operational friction becomes very visible. Agents spend time piecing together information from different tools and coordinating internally before they can even respond. That experience shaped our thinking at Hiver. We look at the entire support lifecycle, from the moment a request arrives to the point it is resolved, and ask where AI can remove that friction so teams spend more energy solving the problem.

Hiver emphasizes using AI to remove busywork rather than replace human judgment or empathy. Where do you draw the line between helpful automation and over-automation in customer support?

Support work contains a lot of operational effort that never shows up in the final response. Agents categorize requests, search for policies, pull account information, and trace long conversation histories before they can decide what to say. AI handles that groundwork well. When a system can summarize a thread or surface the right knowledge article at the right moment, the agent starts the conversation with a much clearer understanding of the situation.

Judgment enters the picture when the conversation involves emotion, accountability, or ambiguity. A frustrated customer or a service failure requires interpretation and care in how the response is framed. AI can provide context and suggestions in those moments, though the final decision about tone and resolution stays with the person responsible for the customer experience.

Many AI tools look impressive in product demos but struggle in day-to-day production use. What have you learned about the gap between AI that demos well and AI that consistently holds up inside high-volume support inboxes?

A demo captures a clean scenario. The question is predictable, the knowledge base is organized, and the system produces a response. Real support work rarely unfolds that way. Requests arrive with partial information, the conversation stretches across multiple exchanges, and the agent often needs input from other teams or systems before the situation is clear.

One lesson that becomes obvious in production is that the response itself is only one piece of the job. Much of the effort sits around understanding what happened and deciding how the issue should move forward. AI holds up far better when it supports that flow of work. Helping agents understand the context of a conversation quickly makes a meaningful difference when the inbox starts filling up.

Hiver integratesectly into existing communication workflows instead of forcing teams into entirely new systems. How important is this “meet users where they already work” philosophy when deploying AI in fast-moving environments?

It matters a great deal because support teams already operate under pressure. When a new tool asks them to change how they work or jump between systems, the friction shows up immediately. Most support conversations still begin in email, and the work around those conversations involves pulling context from other systems and coordinating internally with colleagues. If AI sits outside that environment, the agent ends up doing extra work just to use the technology.

We have seen that teams move much faster when the intelligence appears inside the workflow they already rely on. An agent opening a long email thread can immediately see a summary of the conversation, the relevant customer context, and suggestions that help them move the issue forward. That small shift reduces the time spent reconstructing what happened and gives the agent more space to focus on solving the problem itself.

Support teams often operate under intense pressure, especially when dealing with frustrated customers or urgent issues. How do you design AI systems that reduce cognitive load rather than add friction in those moments?

Support work places a constant demand on attention. An agent may handle dozens of conversations in parallel, each with its own tone, urgency, and history. Much of the mental effort goes into reconstructing the situation before deciding how to respond.

AI helps most when it reduces that effort. Opening a thread and immediately seeing a clear summary or the relevant knowledge article changes the starting point of the interaction. The agent spends less time piecing together what happened and more time thinking about the best way to resolve the issue.

With more than 10,000 teams using Hiver globally, what patterns have you observed in how AI adoption evolves after the initial rollout? What separates teams that truly integrate AI into daily workflows from those that treat it as an optional add-on?

The teams that see real value from AI usually begin with a few very specific moments in the workflow where agents lose time every day. Conversation summaries are a good example. When an agent opens a long thread and immediately understands what happened, the entire interaction starts differently. The same applies when the system surfaces the exact help article or policy needed to answer the question. When those moments genuinely help, agents start using the AI naturally because it makes their day easier.

The other factor is the quality of the knowledge behind the system. AI suggestions depend heavily on the documentation and processes it draws from. Teams with clear, well-maintained knowledge bases tend to see much stronger adoption because the suggestions remain useful and trustworthy. Over time the AI becomes part of how the team works, simply because it helps them move through conversations with more clarity.

From a product strategy perspective, how do you balance speed of AI innovation with maintaining reliability and trust — particularly in environments where mistakes can damage customer relationships?

Customer support is one of those environments where small errors carry outsized consequences. A reply that misunderstands a billing issue or a frustrated customer can create more work for the team and damage trust quickly. That reality forces a very deliberate approach to where AI takes action and where it supports the human agent. Some tasks, like categorization or summarizing conversations, tolerate a high degree of automation. Decisions that affect revenue, policy interpretation, or customer relationships require a much higher level of certainty.

Product strategy becomes an exercise in matching AI capability with the level of reliability a task demands. New models and techniques appear constantly, though the real test is whether they perform consistently inside day-to-day support operations. The teams building these systems need to stay close to how agents actually work and treat that feedback as the primary signal for what should ship next.

How do you think AI will change the structure of support teams over the next five years? Wills shift toward oversight and judgment, or will entirely new categories of work emerge?

The structure of support teams will likely shift toward fewer people handling repetitive ticket processing and more people focused on resolving complex issues. As AI handles tasks such as summarizing conversations, organizing incoming requests, and helping draft replies, agents will spend more time understanding what actually happened in a situation and coordinating with other teams to fix it. The becomes less about moving tickets through a queue and more about owning the outcome of the customer issue.

Teams will also need people responsible for the systems that make AI useful. AI-assisted support depends heavily on accurate documentation, clear processes, and reliable knowledge sources. Maintaining those systems becomes an ongoing job, so support organizations will likely adds focused on managing knowledge, improving workflows, and making sure the AI continues to provide useful guidance as products and policies evolve.

Hiver operates in a competitive help desk market. What fundamental shifts in customer expectations do you believe legacy platforms have failed to adapt to?

Customers increasingly expect continuity when they reach out for support. They want the organization to remember previous interactions and carry that context through the entire conversation. Repeating information across several exchanges quickly becomes frustrating.

Support issues also extend beyond the support team itself. Product teams, operations teams, and account managers often contribute to the resolution. Platforms that bring communication and operational context into the same workflow make it easier to keep ownership of the issue clear from start to finish.

Looking ahead, what does “great customer support” look like in an AI-first world — and what capabilities will separate companies that thrive from those that fall behind?

Great support in an AI-first world will simply feel easier for the customer. They reach out, the team understands the situation quickly, and the conversation moves forward without a lot of back-and-forth to reconstruct what happened. The technology behind it stays mostly invisible. What customers notice is that their issue is understood and resolved without unnecessary effort.

For the teams running support, that experience comes from having the right context available the moment a conversation begins. AI helps organize information and surface what matters while the agent focuses on understanding the customer and guiding the issue to resolution. The companies that thrive will be the ones that build their support operations around that clarity and continuity in the interaction.

Thank you for the great interview, readers who wish to learn more should visit

Hiver

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