In the press, it seems like the only tech news these days is around the impact AI is going to have on the corporate world. Often, we’re hearing about the amazing capabilities that were fantasy five years ago, but now seem tantalizingly close. Of course, there’s plenty of talk about the downsides and the risks, but we tend to hear about the big-picture items, like what it’s going to do to the labor market.

When we’re out listening to people in the corporate world, people tend to be optimistic about the business impact, while somehow still stressing out, not about losing their jobs, but about how they’re possibly going to manage their way through this, to get to the business impact everyone expects, without it blowing up in their faces.

Many people worry that they’ll play around with every vendor, wasting a lot of money without making any progress, or that they’ll go all-in too quickly, only to find it’s not cost-effective for them, after they couldn’t possibly hit an “undo” button. 

It seems like it should be easy for corporate leaders to get their arms around the adoption, costs, and risks of what is undoubtedly the hottest tech trend ever. Unfortunately, the industry has never done a good job of managing its internal technology adoption.

Why Legacy Tech Still Haunts Modern Enterprises and the Hidden Costs of Invisibility

Large companies have lots of scars here, such as purchasing expensive product A to replace expensive product F, only to then spend years paying both vendors, because it was too hard to find out all the places where F was really being used, to be sure nothing breaks.

Most medium to large companies aren’t good at keeping tabs on the software they already have. We’ve seen small companies pull in dozens of developers for a production outage, just to figure out who owns the thing. Often, that’s without knowing if it’s even important, because it’s just as hard to look at a cloud console and know if you’re really looking at a customer-facing production workload, unless you’ve got angry customers calling.

The truth is, people don’t have a real-time view of what software they use where, so that they can be strategic about, say, consolidating to one AI vendor for cost efficiencies. 

The problem goes way beyond cost; It makes risk management and security almost impossible. Alerting tools never have enough context by themselves to remove significant noise. While you’d expect their data lake investments to connect the dots and eliminate said noise, the whole industry has spent over twenty years in firefighting mode, with nothing making a dent. Even AI doesn’t bring the magical missing context to connect those data silos. Every day we watch investigations dying on questions that feel like they should be automatable, but in practice require engineers to take time away from their primary responsibilities (which they can’t afford to do), such as:

• “What version of the code is this actually?”
• “What other software is running with it?”  
• “Does it use this third-party component we’re worried about?”
• “Is this deployment a production instance? And is it directly communicating over the internet?”
• “Is this service handling sensitive data?”

AI’s not going to be immune to these problems. In fact, it seems likely to make it much, much worse. 

Let’s be real. The AI wave isn’t coming. It’s here. Code is being generated by LLMs, refactored by bots, and deployed by scripts. That’s amazing for velocity, but terrifying for traceability. Who’s watching what’s going out the door? What’s the provenance of that Python library? Was that container built from scratch, or pulled from GitHub with a curl command?

Pretty soon, we expect most organizations to deploy even more software, because, with vibe coding, you don’t have to be a developer to get something useful built and deployed. The amount of software people are deploying is expected to explode, and as labor-intensive as it is to get basic strategic questions answered, people have the right to be nervous.

How to Solve Shadow Engineering With Code-to-Cloud Visibility

A couple of years ago, we went on a journey to learn all the details about how companies struggle to deal with these so-called “shadow engineering” problems; meaning, problems where they should be able to have easy answers about their tech stack, but rarely seem to be able to find the right answers quickly, when it’s important.

We were shocked to see that even companies in the tech industry, which develop everything in-house and mandate the use of their tooling for all projects, were also dealing with the problem. I remember one session with the CTO of a company that sells such tooling. I was almost embarrassed to ask how long it generally takes to determine when there’s an outage, and who owns it. I figured that information was automatically updated, and I said so. 

He said, “Are you kidding? Just last week, there was an issue with a staged rollout of a new version of something, and it took people three hours of asking around to find out who owned it. We are not immune to that problem.”

We quickly learned that, while software is at the strategic heart of most businesses, the majority of companies, regardless of their size, will not be able to keep up with the inevitable complexity. Nobody seemed to have figured out how to automate keeping all the dots connected, until now.

At Crash Override, we worked hard to make it incredibly easy for companies to get code-to-cloud visibility of software at scale, in a way that’s easy to deploy across a broad organization, and follow software from creation, to build, and all the way into deployment, with a cohesive view of how all all the pieces relate. In most cases, this can all be done transparently and automatically.

The First and Only Deep Build Inspection Technology

While developers are used to being able to compare two versions of the code, we’re able to look at an actual deployment and help people answer “what’s different?” at a much deeper level. This is because we observe every part of building and deploying software at an unprecedented level, automatically and continuously monitoring and observing all the way into production.

When something breaks, the company’s code often hasn’t changed at all. More often, something in the build or deployment processes, or one of the many third-party dependencies upon which software is built, has. Without developer involvement, we make it easy to see all the hidden pieces and steps that make up software, and track how they change over time.

Easily deployed per-pipeline or build system without developer involvement, we capture high-fidelity metadata during every software build, and help you understand and mitigate the wider blast radius before it happens.

Our GPS-like capabilities make it easy to answer hard questions like, “Who owns it?” But our visibility is also deep, giving us an unprecedented ability to inspect and compare software. 

We monitor every aspect of a build, including the processes that run, the third-party code that gets downloaded and executed as a silent part of the build, the files that get modified, the executables and containers that are built and pushed, and the beaconing that vendors use to track their own usage. Additionally, we track SCM activity. What this all reveals is far more than scattered breadcrumbs; it provides a complete catalog of your software supply chain, showing how everything fits together from source to artifact. This is the starting point for traceability, accountability, and efficiency across the entire engineering lifecycle. And instead of just cataloging what’s on a file system, we can track what actually was there to assist with the build, and what makes it all the way into the executables that end up running in production.

Today, our early customers have been using us at scale to address problems they could never address well before:

1. Incident Response. By keeping a real-time code-to-cloud inventory of software, whenever something breaks, or there’s a critical security alert, we save hours providing instant answers to questions that often required a lot of asking around, such as, “What is it?” “Who owns it?” and “What’s changed since yesterday?”
2. Software component and image lifecycle management. We make it possible to track adoption of software, or software technologies, whether it’s to make sure you get value from a purchase, or to remove security risk, by finding the biggest, most relevant deployments of legacy or poorly written components.
3. Risk Management. We provide all the missing pieces to make other security products better, to bring the context that is needed to determine what’s noise, and what should really be investigated. We also illuminate risk management problems that nobody has had a window into before, such as seeing where new cryptography comes into the organization, so that you never have to deal with a surprise outage because of cryptographic certificates that nobody realized needed to be renewed, because it wasn’t being managed.

Finally, a Strategic Approach to Software Development

We’ve talked about Engineering Relationship Management (ERM) before, but if you’re new to Crash Override, here’s the TL;DR: Our vision of enabling visibility, prioritization, and strategic fixes for everyone in DevOps, that enables a more effective, efficient and cheaper way for teams to work is called ERM. It’s like Salesforce for software engineering teams, merging silos of data, so that everyone can automatically get the visibility they need to do their jobs better.

Crash Override is the first ERM platform, connecting every DevOps dot into a single source of truth that shows what you have, how it is deployed, and how it is changing in real time. Build inspection is its beating heart.

With ERM, you don’t just respond faster, you decide smarter. While most systems today are optimized for alerting or compliance, ERM brings a different advantage by focusing on true clarity and action. It uncovers not just what’s broken or vulnerable, but what actually matters, creating a live catalog of your engineering environment that tracks every repository, every build, and every change as they happen.

It’s the difference between guessing and knowing, reacting and leading.

To help us fuel the future of ERM, we’ve raised $28 million in seed funding from GV, SYN Ventures, Blackstone, and Bessemer. 

Our investors have backed our belief that engineering leaders don’t need another dashboard, but a true, reliable source of truth. This funding accelerates our product roadmap, grows our partner ecosystem, and helps us deliver ERM to more organizations that need it now.

As part of the transaction, Blackstone contributed an internally developed, modular scanning and orchestration framework they’ve been using at scale. This framework will form the basis of Ocular. Soon, we’ll open source a significant portion of the codebase with the aim to democratize access to robust security scanning capabilities for enterprises and individual security researchers alike. The lead developer behind the framework has joined Crash Override, and their work will help us sharpen our analysis of AI-generated code and deliver smarter software intelligence that actually helps teams move faster.

We’re using it to dissect GenAI code patterns, track high-entropy function usage, and detect unfamiliar dependencies back to known sources. In the hands of forward-leaning engineering teams, this becomes a scalpel, not a sledgehammer.

What You Can Do Today

Operating at scale means you’re likely overwhelmed by alerts and buried in dashboards, each one competing for attention but offering little clarity. You don’t need another noisy feed, you need a foundation built on visibility.

That starts with understanding exactly what’s being built, how it’s being built, and who is behind it. From there, you can track artifacts back to their origins, identify unapproved tools and workflows, and surface outliers that don’t belong. With this foundation in place, you can begin shaping smarter governance models, more reliable incident response playbooks, and a product strategy informed by facts instead of assumptions.

Crash Override helps you do all of this, and we make it dead simple to get started.

Don’t Just Take Our Word For It. See It For Yourself.

We’re not going to bend your ear or twist your arm to sell you a solution you don’t need. Instead, we’ll take 30 minutes to demonstrate what Crash Override can do and how it solves DevOps difficulties. Book a walkthrough and see how visibility turns into velocity.