Anti-FAQ
LinearB may not be for everyone
We’re building the most useful tool we can for DevEx leaders and their teams. But nobody’s perfect.
Is my engineering organization mature enough for an AI productivity platform?
We work with thousands of engineering organizations of every size and maturity level. Teams earlier in their maturity journey use LinearB to build a data-driven foundation for their AI strategies and workflows. In many ways, ensuring that you're making the best decisions possible today creates an outsized impact on your business down the line. It's never too early to standardize best practices across teams and reduce developer toil using cutting-edge workflow automation that lets you do more without increasing headcount. As they say, yesterday is the best time to begin an engineering productivity journey. The second best time is today.
Can I use LinearB if my teams use different development workflows?
Yes! There is no one-size-fits-all in software engineering. We support some of the world's largest enterprises, including multiple business arms, thousands of repos, and hundreds of teams - many of which work in unique and interesting ways. So whether you are Agile or Kanban, On-prem or Cloud or Hybrid, Front-end or Back-end or Mobile, we've got you covered. Our platform is built with high levels of flexibility, not because it's easy, but because it has to be in order to support our users. So don't let a little thing like unique workflow or development pipelines get in the way of improving your team's productivity.
I already get data from my Jira, git, etc. Why would I need another thing to look at?
Every tool in your stack has data you can look at - but that is also part of the problem. When data is siloed and not integrated into the full view of your engineering operations, it becomes either too time-consuming to extract meaningful insights out of or ignored altogether. LinearB correlated the data from across your stack to present not just end-to-end visibility but also insights, recommendations, and automated workflows that actively improve your team's ways of working. In a nutshell, we transform your existing data into actions that reduce daily toil and drive more meaningful conversations.
What is the difference between LinearB and other tools in this space?
There are many differences, but the most important one is philosophy. LinearB is an active platform, meaning we don't just show you metrics and survey results; we also provide you with the solutions to take action to improve them. The truth is, DORA, SPACE, et al. metrics are available in many tools, but what happens when you have them in place? How do you actually improve? More meetings and new initiatives are not the answer. LinearB provides engineering organizations with AI-driven workflow automations where their teams are already working (Chat and Git) that reduce toil, wait times and frustration. No other tool in this space can identify the problem and solve it within the same platform.
I already use Copilot for AI Code Reviews, why do I need LinearB AI Code Reviews?
That's great! It sounds like you already understand the value of these AI use cases. There are a couple of significant differences that LinearB offers in this regard:
Level of Control - Copilot will either run on all PRs or only when requested and only with users who have an active subscription. LinearB provides AI reviews for all developers, and we give granular and centralized control that is super flexible (yaml). When a DevEx team is trying to scale process changes across teams, this level of control enables them to be more effective.
Orchestration - When our AI review provides a LGTM comment, many customers will then allow gitStream to automatically approve the PR if it's small and/or a safe change. This creates direct time savings for the team. On the other hand, you can also trigger additional reviews by the code experts or specific teams.
Suggested Fixes - Copilot only provides suggested fixes for minor errors like typos. LinearB is far more dynamic, not only identifying major bugs but also providing suggestions for correcting the error.
Context Rich - As your data provider, LinearB is already connected to your code, issues, incidents and releases, making our AI code reviews more context rich than copilot's. While we aren't using all of this data at this moment, future enhancements on this feature will allow us to customize reviews and orchestration to your organization.
Cost - Our AI code reviews are free, as they are already bundled into your LinearB subscription. Copilot's "Review Changes" is a premium feature that has a pre-set quota based on your subscription type.
Our process / data isn't clean enough for this to give me a good signal.
LinearB was built to be highly flexible regarding data gathering and categorization, and enhancing these capabilities is a core part of our roadmap every quarter. That's all to say it's very atypical that we are unable to provide you with data insights. And even if you are in the 1-2% of organizations with poor data cleanliness, we can help with that, too. We provide all of our customers with a set of policy-as-code automations that ensures the developers on your team begin using best practices like attaching a Jira ticket to a PR or adding specific naming conventions. We believe data is at the core of strong decision making and will work closely with your teams to ensure you have what you need.
We're only focused on metrics, can I buy LinearB for metrics alone?
Technically speaking, yes, you can. But if we've learned one thing in the last eight years, it's that metrics alone don't improve dev teams. As an engineering productivity platform, our core strength, and why you should purchase LinearB, is what you do after you have all of your metrics and dashboards set up. Our suite of AI and YAML-based workflow automations takes action against your metrics so that you're not only improving your leadership's decision-making and your team's ceremonies with data but are also actively improving your ways of working at the developer level.
How do I measure the impact of my AI tools (aka Copilot, Cursor)?
Measuring the impact of AI tools within LinearB is a common use case for our customers. We combine three unique methods for measuring AI impact across your SDLC. The first is our out-of-the-box meta data identifiers that allow us to visualize the actions of over 50 AI tools in terms of repos they touch and PRs they contribute to. The second is a YAML rule that automatically tags PRs that use AI-generated or augmented code. The third is our suite of API integrations that transfer and visualize the data directly from the AI tool itself. By combining these methods across your teams, tools and workflows, LinearB provide the most holistic AI tool impact visibility in the market.
How do I experiment with AI First development in a responsible manner?
Perhaps the most important part of any experiment is establishing governance around what is happening to ensure both risk and compliance are kept at acceptable levels. Luckily, we, too, are AI practitioners and have built a rules-based automation toolset that allows you to do just that. So, no matter the tool or experiment, we provide your teams with the capabilities to contain it and box it in so your teams can experiment freely.
How do I optimize & coach my development workforce for this era?
The short answer is data and automation. Data and its availability are requirements for any conversation focused on optimization - workforce or otherwise. Understand what is happening, pinpoint opportunities, and apply automation that improves them. The same can be said for coaching, but even here, we take this effort a step further by allowing teams to visualize data, set goals, and apply policy-setting automations that drive best practice behaviors.
I already have metrics capabilities within my CI/CD toolchain. Isn’t this redundant?
The reality is that all tools in your stack provide some level of data - but they aren't working together. Siloed data is nearly useless due to the amount of time and effort it takes to manually correlate it in such a way that creates actionable insights. This is why our founders, both former VPs of engineering, created LinearB in the first place. Fortunately, we've now evolved far beyond simple data correlation to provide you with solutions to the bottlenecks we identify. Ultimately, the conversation is about data; it's about improving productivity. Our focus is on improving your decision-making while simultaneously saving your developers time on every pull request.
My CEO doesn’t care about engineering metrics, how does this help me deliver more features?
Translating engineering performance into business outcomes is one of the most challenging objectives any team can take on. Fortunately, you've landed in the right place. Not only are we providing you with a full suite of metrics, benchmarks, and automations to improve them, but we also have entire teams of success leaders who work with you to translate these outcomes into developers' hours saved. When you take the time you've saved and compare it to feature cost and time spent, even your CEO will understand why it's time for your next promotion.
I’ve used a free tool that told me I have a Cycle Time of 8 days, why do I need LinearB?
Many of our customers have used other metrics tools before purchasing LinearB, and that is great because data has value. But we always ask the same question - now what? Now that you have a dashboard full of metrics, what is the plan to improve them? The most important difference between LinearB and our competitors isn't our years of experience training engineering teams how to use data, our dashboard flexibility for diving deep into data segments, or even our robust API suite - it's our ability to apply automations that improve your metrics, your team's productivity, autonomously, on every single pull request. So, let's look at your metrics and use them as a starting point for building a productivity program that will deliver actual business results together.
I’m afraid an inexperienced manager would use this to drive bad behavior.
The LinearB platform provides data and metrics at every level of your organization. It's not only our job to do so but also our core belief that data should be at the root of every business decision. As engineers ourselves, we also know that development is a team sport, which is why we've built our platform to present team and organization focused metrics. Even so, yes, it is possible for an inexperienced manager to use data from LinearB or any other tool in your stack or organization to drive bad behavior. This is why we've invested heavily in building robust role-based access settings within your admin profiles. Ultimately, the best engineering organization in the world use data to pursue excellence, and we believe you should too.
I can write scripts and get all of this data myself.
This is about 30% true. You can most definitely invest time and resources to pull and correlate data from across your stack to see a standard set of performance metrics. But even if we forget about our automation library, which actually improves these metrics autonomously, you will still be missing the most useful and robust pieces of our metrics capabilities, such as resource allocation, delivery forecast, planning and capacity accuracy, and automated cost capitalization reporting. Engineering organizations buy software so they can put programs on rails and start saving time...vs. spending more on efforts that don't drive customer value.
Things are working pretty well on my team and this just seems like a nice-to-have. We just don’t have the budget, time, or real need for another tool or process.
I will cautiously answer a question with a question: how do you know things are working pretty well? The reality of our world is that technology evolves quickly, and those that don't evolve with it are left behind. LinearB is an established technology that gives engineering organizations both time and budget back for reinvestment. Generally, we don't feel that the use of data for decision-making or automations for process optimization fall into the category of nice-to-have, but we encourage you to invest a minimal amount of time to demo the platform and make that decision for yourself.