The AI-era engineering performance platform | Pensero [Let's talk](../book-demo) [Login](/auth/login/) [Login](/auth/login/) [Let's talk](../book-demo) [Login](/auth/login/) Customers / Stord ## From visibility to efficiency How Stord uses Pensero to standardize engineering performance,
measure AI impact, and increase engineering velocity by 25% ![](https://i.ytimg.com/vi_webp/maIe2HJRjTw/maxresdefault.webp) ##### For Nick Traylor, Head of Engineering at **Stord**, scaling a high-performing engineering organization required a standardized and objective way to understand engineering performance across the organization. Stord operates at the intersection of logistics and software, building everything from warehouse management systems and transportation infrastructure to order orchestration and commerce tooling. With hundreds of engineers across multiple highly specialized teams, engineering performance plays a direct role in how quickly the business can execute and scale. But as the organization grew, so did the complexity of managing performance consistently across teams and managers. Before Pensero, engineering leaders relied on a combination of Jira dashboards, GitHub activity, spreadsheets, and manual reporting processes to understand how teams were operating. Managers spent hours every week aggregating data themselves, often **interpreting performance differently** depending on the team or workflow. > *“There’s a lot of subjective data in there. We could be holding individuals accountable differently. We weren’t setting a standard across the group.”* At the same time, AI adoption was accelerating rapidly across the organization. Engineers were increasingly using tools like Cursor, Claude, and Copilot to move faster, but leadership wanted to understand whether AI was actually accelerating development without compromising quality. That combination of **scale, subjectivity, and AI adoption pushed Stord to look for a different approach.** ## The challenge ### When engineering visibility becomes fragmented Before Pensero, visibility into engineering work existed, but it was fragmented. Like many engineering organizations, Stord had access to large amounts of operational data across Jira, GitHub, and internal tooling. But **turning that information into something standardized, objective, and actionable remained difficult and time consuming.** Each manager approached performance differently. Reporting workflows varied across teams. And engineering leadership spent significant time manually aggregating information into spreadsheets and documents just to create a usable view of performance. The challenge wasn’t simply measuring output, but **creating a shared and objective framework that could scale consistently across the organization.** > *“Automation, standardization, and time savings was our primary goal.”* This became even more important as the company invested more heavily in AI-enabled engineering workflows: Stord wanted visibility into whether AI was being adopted consistently across the organization, whether it was helping engineers move faster, and whether that speed was being achieved without sacrificing quality. > *“We’re interested in AI to accelerate us as a team... are we using AI, is it accelerating us and are we still delivering quality code?”* ### Why Pensero changed the conversation #### 1. Objective performance, shared across the organization Pensero introduced a fundamentally different way of understanding engineering performance at Stord. Instead of relying on fragmented dashboards and manually aggregated reports, Pensero standardized engineering visibility across the entire organization, providing managers and leadership teams with a shared system for understanding contribution, delivery, quality, and AI adoption. This immediately **reduced operational overhead while creating more consistent and objective conversations around performance.** > *“I can go to Pensero and tie all this together in a great format.”* That visibility also changed how Stord approaches accountability and performance development. Rather than simply identifying underperformance, managers can now understand the complete picture of each engineer's contribution and have more constructive conversations based on objective information. > *“It captures practically everything... technical design documents, conversations, code, code reviews, AI... and it aggregates it up to a single number.”* #### 2. Engineering in the age of AI The platform also became critical for understanding the impact of AI across the engineering organization. Stord can now measure AI adoption across teams, understand whether engineers are accelerating, and verify that higher delivery speed isn't coming at the expense of software quality. > *“Are we using AI, is it accelerating us and are we still delivering quality code?”* Rather than treating AI adoption as an end goal, Pensero helps engineering leadership at Stord understand **whether AI is creating measurable improvements and what successful practices should be replicated across the organization.** #### 3. Continuous improvement through objective measurement As Stord has grown, introducing new processes has become inevitable. But implementing change is only half the challenge. The other half is knowing whether those changes are actually improving engineering performance. Pensero gives the team an objective feedback loop. Rather than relying on intuition, engineering leadership can introduce new processes, measure their impact on engineering velocity, and decide whether those changes should be rolled out more broadly. > *“When you put processes in place, it's important to measure yourself to make sure you're headed in the right direction... If they're positive, then we continue down that area and roll it out to the rest of the team.”* #### 4. R&D capitalization without the manual overhead Beyond engineering performance, Pensero also introduced a new level of efficiency in R&D capitalization. Before Pensero, this process required engineering managers to manually gather data, maintain spreadsheets, and build scripts to approximate engineering allocation and capitalization reporting. Even then, many of the calculations still relied heavily on estimation. > *“We were putting everything into Excel and calculating it manually. Sometimes we were just guessing.”* Pensero changed this by **leveraging the same engineering data already being used to measure contribution, delivery, and team performance.** Instead of creating separate manual workflows for finance reporting, Stord can generate capitalization insights directly from engineering activity. The results closely match the company's internal calculations while significantly reducing manual effort. ## The results ### Higher velocity, less overhead, better alignment The operational impact of Pensero at Stord has been both measurable and immediate. - Over the last three months, Stord increased engineering velocity per individual contributor by **25%**, even while significantly expanding headcount across the organization. > *“Each individual coming on is adding twenty-five percent more value.”* - For leadership, this provides objective evidence that productivity continues improving as the engineering organization scales. - At the same time, Pensero has significantly reduced operational overhead for engineering managers. Before adopting the platform, managers spent hours every week manually compiling reports, and engineering leadership spent additional time consolidating that information across teams. > *“It probably saves each manager at least three hours a week... and myself probably double that.”* - Across the engineering organization, this translates into **more than 120 hours saved every month**. - Performance conversations have also become more productive. Because every engineer has access to the same information leadership sees, discussions are grounded in shared, objective data rather than subjective opinions. ### Fast time to value and close partnership One of the things that stood out most to Stord was how quickly Pensero became operationally valuable. Once the engineering teams were connected, useful signals appeared almost immediately, with meaningful trends emerging within the first few weeks. > *“Once we got the teams in there... you can start seeing signals pretty much instantly. Within the first month, we could certainly start seeing trends and start acting on those trends.”* Nick also highlighted the implementation experience as a key differentiator. > *“Pensero was great to work with... we got things set up very quickly... we had a Slack channel where we could communicate very effectively, very quickly.”* For Nick, the value of Pensero ultimately comes down to one idea: > ***“Efficiency multiplier for my engineering organization.”*** Stord [stord.com/ai](https://www.stord.com/ai) ![](https://framerusercontent.com/images/44xfmHnvxBpZtHL2IIqNTQCPrIQ.png?width=2231&height=477) Stord is an AI-powered commerce platform for e-commerce brands. It helps teams manage fulfillment, inventory, delivery, and customer experience with greater visibility and control. “Efficiency multiplier for my engineering organization.” ENGINEERING VELOCITY ### +25% ### +25% ### +25% increase per engineer TIME SAVED ### 120+ ### 120+ ### 120+ hours saved per month across engineering managers # Get months of engineering performance data now Stop deciding on gut feel. Get 90 days of objective data in minutes. [Let's talk](../book-demo) Get months of engineering performance data now Stop deciding on gut feel. Get 90 days of objective data in minutes. [Let's talk](../book-demo) Get months of engineering performance data now Stop deciding on gut feel. Get 90 days of objective data in minutes. 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