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Freshworks Proved AI Impact at Scale. 1,500 Engineers.
One Source of Truth.
How an $800M ARR, NASDAQ-listed software company finally got visibility into what AI was actually doing to engineering performance across a globally distributed team.
10%
Improvement in Time-to-market
50%
Reduction in Rework and Maintenance
10%
Reduction in Review Time
" The biggest benefit I see with Hivel is a single pane of glass. We are able to see how much cycle time is improving, how much code is being reworked — all of that across 1,500 engineers working across multiple products and multiple time zones, in one place. "
Murali Swaminathan
CTO, Freshworks
The Challenge
Limited AI Impact Visibility
Freshworks is not a simple engineering organization. It is a NASDAQ-listed, multi-product company with more than 1,500 engineers distributed globally, each product running its own tech stack, its own team structure, and its own way of working. Some of those teams came through acquisitions, bringing their own tools and processes with them.
The result was an engineering organization that had no standard way to measure productivity, no consistent view of bottlenecks, and no single place to understand how work was actually moving.
"Due to the nature of the tech stack, we ended up with multiple different processes across all these product lines," said Murali Swaminathan, CTO at Freshworks.
The visibility problem was hard enough on its own. But then came a harder question: AI. Freshworks had leaned heavily on AI tools across the development lifecycle. Engineers were using Copilot and Cursor for code generation. AI was being used to review code. Automation was touching the CI/CD pipeline. But knowing which tools were actually helping, and by how much, was nearly impossible without clear data.
Surveys were the previous answer. They were slow, expensive, and never fully accurate. Freshworks went to market looking for something better. They evaluated comparable tools. None were comprehensive enough. They needed a single platform that could measure developer productivity across the entire organization, cover both AI and non-AI code generation, and surface all of it in one coherent view for engineering leaders.
The result was an engineering organization that had no standard way to measure productivity, no consistent view of bottlenecks, and no single place to understand how work was actually moving.
"Due to the nature of the tech stack, we ended up with multiple different processes across all these product lines," said Murali Swaminathan, CTO at Freshworks.
The visibility problem was hard enough on its own. But then came a harder question: AI. Freshworks had leaned heavily on AI tools across the development lifecycle. Engineers were using Copilot and Cursor for code generation. AI was being used to review code. Automation was touching the CI/CD pipeline. But knowing which tools were actually helping, and by how much, was nearly impossible without clear data.
Surveys were the previous answer. They were slow, expensive, and never fully accurate. Freshworks went to market looking for something better. They evaluated comparable tools. None were comprehensive enough. They needed a single platform that could measure developer productivity across the entire organization, cover both AI and non-AI code generation, and surface all of it in one coherent view for engineering leaders.
"We had looked at other comparable tools and what we found was that nothing was comprehensive enough. We needed a single tool where we could visualize all of that for leaders."
The Solution
A Single Pane of Glass
Freshworks chose Hivel after a proof of concept that made the decision straightforward. They had already invested in an external engineering intelligence vendor, but the visibility that solution provided was incomplete. What Hivel surfaced in just 30 days, clear data on developer performance, cycle time drivers, and real AI impact, was more actionable than anything their previous vendor had delivered across months or even years of use. Soon the full engineering organization, more than 1,500 people, was ready to move.
Implementation was smooth. Connecting repos and Jira took days. The Hivel team handled the heavy lifting: setting up teams, configuring workflows, and ensuring the data flowing in was clean and actionable. Within a couple of weeks, Freshworks had dashboards with insights they could actually use.
Engineering managers and tech leads adopted quickly. The Activity and Dev360 views removed the guesswork from sprint planning and retrospectives. Feedback that had previously been anecdotal was now grounded in patterns everyone could see and act on. At the leadership level, questions that had previously taken hours to answer now had one reliable source.
For Swaminathan, the value was immediate. Cycle time was no longer just a number. Review time wasn't just another chart. It showed which teams were overloaded and where the real bottlenecks lived. By linking Git activity, reviews, and deployments, Freshworks could finally decide what to fix first and know whether fixing it was working.
Critically, Hivel gave Freshworks something no other tool had: visibility into AI adoption and real AI impact across SDLC. Not suggestions accepted in an IDE. Not activity metrics. Real, pull-request-level data on how much of what shipped was written by AI versus written by hand.
Implementation was smooth. Connecting repos and Jira took days. The Hivel team handled the heavy lifting: setting up teams, configuring workflows, and ensuring the data flowing in was clean and actionable. Within a couple of weeks, Freshworks had dashboards with insights they could actually use.
Engineering managers and tech leads adopted quickly. The Activity and Dev360 views removed the guesswork from sprint planning and retrospectives. Feedback that had previously been anecdotal was now grounded in patterns everyone could see and act on. At the leadership level, questions that had previously taken hours to answer now had one reliable source.
For Swaminathan, the value was immediate. Cycle time was no longer just a number. Review time wasn't just another chart. It showed which teams were overloaded and where the real bottlenecks lived. By linking Git activity, reviews, and deployments, Freshworks could finally decide what to fix first and know whether fixing it was working.
Critically, Hivel gave Freshworks something no other tool had: visibility into AI adoption and real AI impact across SDLC. Not suggestions accepted in an IDE. Not activity metrics. Real, pull-request-level data on how much of what shipped was written by AI versus written by hand.
"Hivel connected the dots in a much clearer way. Cycle time was no longer just a number. We could see the real factors behind it."
Result
Faster Shipping
The numbers at Freshworks reflect an engineering organization that stopped operating on assumptions and started improving on evidence.
Throughput across the organization increased by 40%, a significant productivity gain achieved while maintaining quality. In some business units, the boost in features released reached 20 to 25%. Rework and maintenance, one of the most costly and demoralizing drains on any engineering team, fell by up to 50%. Cycle time improved by at least 10%. Code review time dropped 8 to 10%, as teams gained better visibility into pull requests, feedback cycles, and AI-assisted code review insights contributing directly to how fast work moves through the SDLC.
One data insight in particular changed behavior across the organization. Hivel showed that review time dropped when the code being pushed was smaller. That single finding drove a deliberate push to keep pull requests tight. Over two quarters, around 55% of all PRs stayed under 50 lines of code. Work moved faster, not because the team grew, but because they could finally see what was slowing them down.
The cultural shift was equally significant. "Data-driven reviews are now a natural part of our cadence," Swaminathan said. "And Hivel is the common source we rely on for those discussions." Teams started celebrating improvements because progress was visible and objective. When something slipped, Hivel helped leadership understand the reason fast, without guesswork.
What's Next for Freshworks
The most important number Hivel surfaced at Freshworks wasn't a cycle time metric. It was proof of what AI was actually contributing to production.
"One thing Hivel made clear is that suggestions accepted in Cursor don't tell the full story of AI adoption," Swaminathan said. "We are actively measuring AI-authored code versus manually written code and what that means for our investments. Hivel helps quantify that by correlating AI code with speed and quality."
"Today, every CFO and CEO wants clear ROI on AI investments," Swaminathan said. "Hivel gives us that in a board-ready way. That's critical to any organization navigating the AI transformation."
Freshworks has a clear path through 2026 and 2027: maximize engineering potential, keep engineers focused on quality work and innovation, and get more to market faster than competitors. The ability to measure what AI is actually doing to engineering performance, verified and board-ready, is how they intend to stay ahead.
RESULTS SNAPSHOT
Freshworks improved Time-to-Market by 10% across the organization. Rework and maintenance dropped by up to 50%. Features released increased 20 to 25% in key business units. Code review time fell 8 to 10%.
Throughput across the organization increased by 40%, a significant productivity gain achieved while maintaining quality. In some business units, the boost in features released reached 20 to 25%. Rework and maintenance, one of the most costly and demoralizing drains on any engineering team, fell by up to 50%. Cycle time improved by at least 10%. Code review time dropped 8 to 10%, as teams gained better visibility into pull requests, feedback cycles, and AI-assisted code review insights contributing directly to how fast work moves through the SDLC.
One data insight in particular changed behavior across the organization. Hivel showed that review time dropped when the code being pushed was smaller. That single finding drove a deliberate push to keep pull requests tight. Over two quarters, around 55% of all PRs stayed under 50 lines of code. Work moved faster, not because the team grew, but because they could finally see what was slowing them down.
The cultural shift was equally significant. "Data-driven reviews are now a natural part of our cadence," Swaminathan said. "And Hivel is the common source we rely on for those discussions." Teams started celebrating improvements because progress was visible and objective. When something slipped, Hivel helped leadership understand the reason fast, without guesswork.
What's Next for Freshworks
The most important number Hivel surfaced at Freshworks wasn't a cycle time metric. It was proof of what AI was actually contributing to production.
"One thing Hivel made clear is that suggestions accepted in Cursor don't tell the full story of AI adoption," Swaminathan said. "We are actively measuring AI-authored code versus manually written code and what that means for our investments. Hivel helps quantify that by correlating AI code with speed and quality."
"Today, every CFO and CEO wants clear ROI on AI investments," Swaminathan said. "Hivel gives us that in a board-ready way. That's critical to any organization navigating the AI transformation."
Freshworks has a clear path through 2026 and 2027: maximize engineering potential, keep engineers focused on quality work and innovation, and get more to market faster than competitors. The ability to measure what AI is actually doing to engineering performance, verified and board-ready, is how they intend to stay ahead.
RESULTS SNAPSHOT
Freshworks improved Time-to-Market by 10% across the organization. Rework and maintenance dropped by up to 50%. Features released increased 20 to 25% in key business units. Code review time fell 8 to 10%.
"This year our throughput went up by 40%. That's a phenomenal spike in productivity, especially when quality is maintained."
Customer Outcomes Delivered With Hivel
What teams achieved with clear, real-time data and support to act on it
What Engineering Leaders Say
Vaibhav Bansal
V.P. Engineering
"Hivel has been phenomenal in helping us present our progress to leadership. Its insights into leading indicators have empowered our team leads to enhance review efficiency and ship features faster. Now, we are able to show the impact of our process changes, clearly identify areas of opportunity, and bring in a data-driven culture without spending time pulling manual reports."
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Murali Swaminathan
Chief Technology Officer
“We were looking for a unique solution to understand engineering productivity and efficiency, with a single tool that could give leaders complete visibility. We evaluated other comparable tools, but none were comprehensive enough. When we came across Hivel, it checked all the boxes for measuring developer productivity across both AI and non-AI workflows.”
Derek Mackie
Director of Engineering
"Using Hivel shone a light on the non-standard aspects of our work, emphasizing the need for crucial adjustments. The insights from Hivel helped us recognize areas for improvement, particularly in managing cycle times and streamlining development workflows. This guidance has been instrumental in our efforts to enhance operational efficiency and productivity."
Leonardo Pinheiro Ferrari
Chief Technology Officer
"Thanks to Hivel, we've seen a staggering 157% improvement in our deployment frequency. By identifying and addressing inefficiencies, and adopting best practices, we've significantly accelerated our progress. Hivel has been instrumental in our journey to operational excellence."
Chris Callander
Senior Engineering Manager
"Hivel has been instrumental in identifying areas for enhancement and guiding our strategic improvements. It has significantly contributed to our journey toward greater operational efficiency. The Activity and Dev360 features in Hivel have been particularly valuable for providing feedback to engineers during 1-1's, enhancing our communication and development practices."
Shiro Theuri
Chief Technology Officer
"Before Hivel, understanding our engineering team's dynamics was a game of guesswork. Now, with Hivel's clear dashboards and insights, our leaders have the clarity and time-saving tools they need to excel. It's been transformative in streamlining our operations."
Koti Reddy
Chief Technology Officer
"Incorporating Hivel into our daily operations has been a game-changer. It's not just a tool; it's a part of our strategic decision-making process, trusted by our leaders at every level. The impact on our engineering efficiency is undeniable and deeply valued."
Manoj Awasthi
Chief Technology Officer
"It has been great to partner with Hivel to get access to insights that help leaders at all levels gain visibility into bottlenecks and challenges that the teams face, build actionable plan to resolve them and track the progress over time. And thanks for the fantastic support!"
Ameen M
Engineering Manager
"Hivel has made a huge difference to how I manage my teams. Before every one-on-one or appraisal, I rely on it to dive into developer metrics and drive truly meaningful conversations. It helps me spot rework patterns, reward high performers, and benchmark our teams even against our US counterparts. The dashboards are intuitive, insightful, and honestly far more actionable than anything we get from Jira."
Paras Sood
Associate Director
"Hivel's insights have been pivotal in refining our engineering processes, elevating both efficiency and the developer experience. We're now making smarter decisions, faster, thanks to Hivel."
Vaibhav Bansal
V.P. Engineering
"Hivel has been phenomenal in helping us present our progress to leadership. Its insights into leading indicators have empowered our team leads to enhance review efficiency and ship features faster. Now, we are able to show the impact of our process changes, clearly identify areas of opportunity, and bring in a data-driven culture without spending time pulling manual reports."
