When Drew Garner looked at his engineering organization at Accolade, he saw a familiar challenge. Despite building technology that delivered care to millions of patients, it was difficult to gain visibility into Accolade's delivery process and surface opportunities for improvement. And as the company expanded globally, relying on “leader’s intuition” simply wasn’t enough.
That’s the problem software engineering intelligence (SEI) platforms aim to solve. These tools ingest data from your engineering tech stack and give you visibility into your team's alignment to value, velocity, DevOps health, and ways of working.
For many organizations, the visibility provided by an engineering intelligence platform is the first step toward continuous improvement and aligning engineering work to business goals.
In this guide, we’ll walk you through key criteria that matter most when evaluating SEI platforms — especially for large-scale, distributed organizations looking for sustainable improvement.
You might be using a spreadsheet to track developer hours, a Jira dashboard to check team progress, and 1:1 meetings for health checks. As engineering organizations grow and become more complex, those processes no longer provide enough timely visibility to help leaders drive engineering effectiveness across the organization.
Software engineering intelligence tools go beyond basic reporting and provide deep insights into how developers work and whether their activities align with business goals.
Instead of just “collecting data,” SEI platforms help engineering leaders understand the deeper context of their engineering organizations so they can maximize value delivery and developer productivity. That’s why Gartner predicts that SEI platform usage will increase by a factor of 10 by 2027.
Here are a few core capabilities of SEI platforms:
Data integration and visibility: SEI platforms pull in sanitized data from collaboration, code and delivery, incident management, and work management tools to provide a unified view of how work happens.
Data trends and insights: Most SEI platforms go a level deeper than “just another dashboard” by trying to help leaders find the signal in the noise — understanding what changes to prioritize and why. They do it through benchmark reports, metric correlations, and trend reports available within the platform.
Resource allocation: SEI platforms also reveal how engineers spend their time and effort. Instead of relying on self-reported time estimates, these platforms categorize different types of work and show you which activities consume your team’s time and energy.
Delivery metrics: DORA metrics are table stakes for any SEI platform. They tell you everything you need to know about engineering efficiency. In addition to DORA metrics, you’ll likely see deeper dives into throughput and review times so you can identify bottlenecks.
These capabilities are table stakes — your evaluation process shouldn’t stop there. Forward-thinking engineering leaders need to go beyond these features and identify what will truly make a difference to your engineering effectiveness.
Here’s a list of “must-haves” when you’re evaluating SEI platforms as an enterprise:
Understanding what happened yesterday isn’t enough. Engineering leaders need to tackle issues before they impact delivery. That’s why you shouldn’t stop at lagging indicators like DORA — by the time you think about fixing the problem, it’s too late. Metrics like deployment frequency should factor into your analysis, but they don’t give you the full picture.
For instance, if deployment frequency drops, was it because of unclear requirements, junior devs being afraid to push their code, or too many interruptions keeping the team from doing their work? If you don’t capture that data, you’ll never know. That’s why you need to focus on leading indicators instead — such as pull request (PR) complexity and deep work time — to mitigate the downstream effects.
High PR complexity is often a canary in the coal mine for delivery problems. If PRs become too large, the immediate impact is likely lower code quality, longer review cycles, and higher cognitive load.
As a result, your team experiences slower deployment frequency, higher risk of burnout, and an overall reduction in engineering efficiency. Similarly, if your developers are being pulled into too many meetings or unignorable Slack chats, they can’t devote more time to fixing problems.
As engineering organizations grow, teams naturally develop their own ways of working. Some may meticulously label every PR while others take a more minimalistic approach. The solution isn’t to enforce a one-size-fits-all process but to choose a platform that adapts to your team’s needs.
Here’s the reality: the engineering process is messy, and your SEI platform should recognize that.
At Uplevel, we use a machine-learning (ML) allocation model to automatically classify tasks when Jira hygiene is a concern. If a developer creates a PR, the model determines whether it’s for new feature development or technical debt reduction, using contextual clues from various tools (just like an engineering leader would).
You can also configure the platform’s rules engine to match your own naming conventions. Let’s say you want to differentiate feature requests from bug fixes. Just adjust the rules to ensure that titles like “customer request” are categorized as feature requests and “security error” as bug fixes.
Think about the typical day of a software engineer. They start their mornings with a team standup followed by a technical design review. Between coding sessions, they field questions in Slack or join an impromptu call to debug an issue. As a result, only 10% of developers spend more than two hours coding in a day.
If you only look at code commits and ticket updates, you’re missing a huge chunk of the story. This blind spot becomes problematic as you scale — and you may only see its impact when it’s too late. That’s why you should make sure your SEI platform integrates with collaboration tools like Google Calendar and Slack.
For example, Uplevel’s Chat Interruptions and Meeting Classifier models surface how much time your team spends on these interruptions. It’s not about aiming for zero interruptions — but there’s a difference between necessary collaboration and unproductive disruptions. Your team needs time to do deep work and collaboration tools don’t always allow for that to happen. This can end up being a hidden drain on engineering productivity.
If you are on the market for an engineering intelligence platform because you want to drive sustainable improvement — and you're not one of the "FAANG du jour" tech darlings with your own established motion in this area — look for engineering intelligence that will help you bridge the gap between metrics and action.
Why? Because it's incredibly difficult to drive organizational change across a large team. Engineering organizations are complex sociotechnical systems made up of technology and people — and optimizing technology is the easy part. People change is messy and not intuitive. It requires strong sponsorship and buy-in at multiple levels to create lasting improvements.
For most engineering intelligence tools, that's a "you" problem.
For Uplevel, we see this as a critical part of the success of our customers — so much so that we launched the Uplevel Method, a hands-on practice that empowers engineering leaders with complete context and support to build high-performing engineering organizations. Combining quantitative platform insights with qualitative results from surveys and interviews, the Method helps leaders and teams identify and pull the most impactful levers to actually drive change and build the capability for continuous improvement.
The ideal SEI platform analyzes data like chat conversations, calendar time blocks, and individual tickets to understand what’s disrupting efficiency. However, any platform needs to surface this data in a way that doesn’t make your developers feel like you’re breathing down their necks.
That’s why it’s key to remember that engineering metrics are not about singling out individual developer performance. Engineering intelligence is a strategic investment to identify efficiency bottlenecks at team and org level.
Your platform of choice should only surface metrics at a level that can’t be used to evaluate individual performance. If not, you’ll risk team morale and productivity instead of gaining trust and respect. For instance, it could look at how much time a developer spent in deep work or dealing with interruptions — but not how much time individual developers spent on a specific task.
Most platforms need access to source code repositories, issue-tracking systems, and collaboration tools, which is a risk as it is. When you add in your organization’s security restrictions, it gets even harder to stay compliant — especially if you’re in a regulated industry.
The solution? Opt for a platform that respects your privacy as it impacts your compliance obligations and your relationship with your team. An enterprise-grade SEI platform should only require metadata from coding or collaboration tools, such as timestamps and character counts for chat messages or lines added and deleted for code repositories. You should have full, on-prem control for data sanitizing.
Traditional engineering metrics are table stakes. For leaders looking to drive lasting change, your choice of an SEI platform should align with your organization's broader transformation goals. The right solution serves as more than just a measurement tool. It becomes a catalyst for continuous improvement — from leading indicators that predict delivery challenges to collaborative frameworks that make meaning from metrics.
Ready to transform your engineering organization with data-driven insights that respect both performance and people? Explore how Uplevel's engineering intelligence platform and methodology can help you build a more effective, sustainable engineering org.