Automating the Yocto Build Process
Stonetusker — Case Studies

Real Projects.
Real Numbers.

We picked three client projects across very different industries. We kept records of what the situation was, what we did, and what actually changed afterwards.
We understand you are looking for real client names. We safeguard client names in case studies under strict NDAs. Delighted customers are ready to provide references upon request.

Embedded Systems  ·  IoT  ·  Yocto

Yocto Build Automation for a Global Audio Manufacturer

Their engineers were spending up to six hours building each firmware image by hand. Every release. No version control, no visibility into what was happening, and a product portfolio that kept growing. We rebuilt the whole process. The same build now runs in 45 minutes on its own, while the team works on something else.

8x Faster builds. Was 4 to 6 hours. Now 45 minutes.
Zero Manual steps needed to run a build
On-demand Releases. They used to ship monthly and on a fixed schedule.
Near zero Error rate. Previously high on every release.

Background

A global manufacturer of audio devices came to us with a build problem they’d been living with for a while. Every new firmware release meant an engineer sitting down and running the Yocto build by hand. One image took between four and six hours. That was just the build itself, before any testing or release work.

The team had no central repository, no real way to collaborate across engineers, and no record of what had changed between builds. If something broke, figuring out why took time they didn’t have. And their product line was growing, which meant the problem was only going to get worse.

What They Were Dealing With

  • No version control at all. No central repository, no collaboration tools, no record of what changed between builds.
  • Every build was done by hand. Errors crept in regularly and were hard to trace.
  • Leadership had no visibility into build status. Release managers were chasing engineers for updates.
  • Six-hour builds meant the engineering team was stuck waiting, not shipping. With a growing product portfolio, this wasn’t sustainable.

What We Did

Moved the codebase to GitHub and set up proper version control, branch policies, and team workflows. Everything the team should’ve had to begin with.
Built automated Yocto build pipelines using GitHub Actions. Every code push now triggers a full image build automatically. Nobody has to start it.
Added proper logging and monitoring so engineers and leadership can see exactly what’s happening during a build and debug failures quickly when they occur.
Set up cloud artifact storage so every build output is versioned and accessible. Release management across multiple product lines became straightforward.

Before and After

Metric Before After
Build duration 4 to 6 hours 45 minutes
Process type Manual, done by hand each time Fully automated
Error rate High, on every release Near zero
Release frequency Monthly, fixed schedule On-demand, whenever they need
Build traceability None Full, versioned cloud storage

What the client said

Stonetusker helped us move from a manual Yocto build process to a fully automated, cloud-based pipeline. Our engineering team now releases faster, with complete visibility and far less manual effort.

Vice President of Engineering Global Audio Device Manufacturer

Running a similar build process? Talk to Us About Embedded DevOps
Cybersecurity  ·  SaaS  ·  AWS & Azure

CI/CD Automation and Marketplace Onboarding for a Network Security Company

A growing network security company with slow, manual deployments, weeks-long compliance update cycles, and a complicated process just to list their product on AWS and Azure. We automated the pipeline, the compliance workflows, and the marketplace registration. They now ship features 50% more often and spend 75% less time on marketplace admin.

50% More deployments. Features get to customers faster now.
60% Faster SOC compliance updates. Used to take weeks.
75% Less manual work for AWS and Azure marketplace onboarding
Zero Downtime during database updates since automation went live

Background

A fast-growing company building network security and fraud mitigation products needed to ship software faster, keep up with changing SOC compliance requirements, and get their product properly listed in AWS and Azure marketplaces. They were doing most of this by hand, and it was eating up time their small team didn’t have.

What They Were Dealing With

  • Deployments were slow and done manually. Errors came up regularly and delayed releases for customers.
  • SOC compliance updates were taking weeks each time. The requirements kept changing and the review process was entirely manual.
  • Listing and maintaining their product in both AWS and Azure required a lot of manual work that had to be repeated every time something changed.
  • Database updates during deployments were done by hand, which meant real downtime risk on every release.
  • Their app was built with Golang, which needed someone who knew how to automate Golang deployments reliably without creating new problems.

What We Did

Built a full CI/CD pipeline for the Golang application, covering build, test, and deploy. Deployment time came down by 40% and manual errors stopped happening.
Automated the SOC compliance update process with full audit trails built in. What used to take weeks now takes days, and the team doesn’t have to babysit it.
Scripted and standardised the AWS and Azure marketplace registration and update process. Manual effort dropped by 70% and the product reaches new customers faster.
Automated database migrations so they happen during deployments without any service interruption. No more planned downtime windows for a database update.

Before and After

Metric Before After
Deployment frequency Slow, manual, irregular 50% more frequent
SOC compliance cycle Several weeks per update 60% faster, days not weeks
Marketplace onboarding High manual effort, slow turnaround 75% less manual work
Database update downtime Real risk on every deploy Zero downtime

What the client said

Stonetusker has been helping my company with CI/CD for our AWS software workflows and has really excelled at improving our Git, GitHub, security scanning, issue tracking, and product testing capability and velocity.

Chief Executive Officer Network Security Solutions Product Company

Dealing with compliance overhead or marketplace complexity? Talk to Us About Security DevOps
AI & MLOps  ·  Healthcare  ·  HIPAA & GDPR

MLOps Pipeline and Compliance Readiness for a US AI Cancer Detection Startup

An AI startup building cancer detection software. No automated MLOps pipeline, manual deployments across environments, and a hard requirement to achieve HIPAA and GDPR compliance before they could launch. We built the infrastructure they needed. Model updates now deploy 60% faster, production has had zero downtime, and they launched fully compliant.

60% Faster AI model deployment. Faster iterations mean faster accuracy improvements.
Zero Production downtime since automated deployments went live
Full HIPAA and GDPR compliance achieved before market launch
One pipeline Across Python, TensorFlow, PostgreSQL and ReactJS. All coordinated.

Background

A US-based startup was building AI-powered cancer detection software using medical imaging. The technology was promising, but their engineering setup hadn’t kept up with the pace of development. Model training and deployment were both done manually, their stack was spread across four different frameworks that didn’t talk to each other cleanly, and they couldn’t launch until they were fully HIPAA and GDPR compliant.

They needed all three problems solved before they could go to market. The clock was running.

What They Were Dealing With

  • No MLOps pipeline. Model training, testing, and deployment were all manual. Every update to the model required manual work to get it into production.
  • Deployments to development and production were done by hand. There was no automated promotion process, no approval gates, and no rollback capability if something went wrong.
  • Their tech stack used Python, PostgreSQL, TensorFlow, and ReactJS. These needed to be coordinated in a single deployment workflow, which they weren’t.
  • HIPAA and GDPR compliance wasn’t just a nice-to-have. They couldn’t launch without it. Patient data handling, audit logging, and access controls all needed to pass regulatory review.

What We Did

Built a fully automated MLOps pipeline. Model training, validation, testing, and deployment are all automated and version-controlled. The team no longer pushes models to production by hand.
Set up GitHub Actions workflows for both development and production environments, with proper approval gates and rollback built in. Promoting a model from dev to production is now a controlled, repeatable process.
Unified the deployment pipeline across all four stack components. Python backend, TensorFlow models, PostgreSQL migrations, and the ReactJS frontend all deploy together in a single coordinated workflow.
Worked through the HIPAA and GDPR compliance process with them. Audit logging, data encryption, access controls, and all the required policy documentation were completed and signed off before launch.
Stayed involved after handover to support the team through the actual launch period and the compliance questions that came up along the way.

Before and After

Metric Before After
AI model deployment speed Manual, slow, fragile 60% faster, automated
Production deployment Manual, real downtime risk Zero downtime, automated
HIPAA and GDPR readiness Not compliant, blocked from launch Fully compliant, audit-ready
Team working practices Data science, dev and ops all working separately One shared pipeline, shared visibility

What the client said

Stonetusker helped us build pipelines that let us deploy AI models quickly and securely, which is critical in healthcare. Their guidance on HIPAA and GDPR compliance made us confident as we prepared for launch.

Founder US-Based AI Cancer Detection Startup

Building AI products in a regulated industry? Talk to Us About AI and Healthcare DevOps

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