💻 CASE STUDY · SOFTWARE DEVELOPMENT
AI Automation for Software Development Companies
Ticket triage, code review queues, release notes, regression testing, documentation, on-call reports — the overhead around shipping, not the shipping itself. We build agents that carry that work so your engineers stay in the code.
Where your engineers’ time actually goes
Senior engineers are the most expensive people in the company to have doing these four jobs.
🎫 Ticket triage and routing
Bugs read, prioritised, labelled and assigned by hand — often hours after they arrive.
🔍 Code review as a bottleneck
Pull requests wait for the one person who understands that module — release trains slip.
📝 Release notes nobody wants to write
Changelogs, customer comms and internal summaries reconstructed from memory the night before a release.
🐞 Regression and reproduction
Manually reproducing the same class of bug over and over, and writing tests for it afterwards.
What we automate for engineering teams
Seven systems we build most often. They connect to GitHub, Jira, Slack and your CI — and they assist your engineers rather than replacing review or judgement.
🎫 Issue triage and routing
New issues labelled, prioritised, assigned to the right owner and de-duplicated against existing tickets — within minutes of arriving.
🔍 Pull request review assist
Every PR arrives with a summary, risk flags and suggested test cases — so a senior review takes five minutes instead of forty.
📝 Release notes and changelogs
Generated from the actual commits and PRs — internal notes, customer changelog and support briefings in one pass.
🧪 Regression tests from bug reports
Reproduction steps drafted and a regression test proposed for every fixed bug — so the same defect never ships twice.
📚 Documentation from code
API docs, integration guides and onboarding notes kept in step with the codebase instead of rotting in a wiki.
🚨 Incident and on-call summaries
Alerts, timelines, impact and follow-up actions assembled automatically — the post-mortem writes itself while the fix is fresh.
📊 Stakeholder and sprint reporting
Velocity, blockers and delivery updates written from the tracker — so nobody spends Friday afternoon formatting status slides.
The engine room
Not an autocomplete in your editor — an agent stack that runs the process around your repository.
🦾 OpenClaw — the orchestration layer
Runs the agents against your toolchain: scheduling, scoped repository access, approvals and hand-offs — engineers keep the final say.
🧠 Hermes engine — the learning layer
Every triage decision and review note is stored as a reusable skill, so the system learns your conventions, labels and house style over time.
🖥️ The control board — one place to watch it work
Every job running, every approval waiting, every hour saved — in one view your tech lead can actually audit.
👥 A team of specialist agents
Triage, review, docs, release and reporting agents — each with one job, coordinated by the orchestration layer.
What changes when it’s running
Typical shifts once the systems are live — the size of the win depends on your team and release cadence.
- Engineers get their afternoons back — triage and admin stop interrupting deep work
- Review queues shrink because the first pass is already done
- Release notes ship with the release, not days later
- Documentation stops drifting away from the code
- Onboarding a new engineer gets faster because the process is written down by the system
How we start
- Free 30-minute audit. We look at your tracker, review flow and release process, and pick the one bottleneck worth automating first.
- Build. Two to four weeks, agents trained on your repo conventions and labels.
- Connect. GitHub, Jira, Slack, your CI — scoped read/write access, nothing broader than it needs.
- Scale. Once the first workflow is proven, the next one builds on it.
Proof, not promises
We run our own engineering on this. Our builds are the demonstration:
- Our Android releases are built, versioned and shipped by an automated pipeline
- Our backups run, verify and publish themselves to GitHub
- Our corporate site was designed, written and deployed by an AI agent team
Questions engineering leads ask us
Does this write code for us?
It assists rather than replaces. The agents handle triage, summaries, documentation, test suggestions and reporting. Your engineers keep design decisions and final review.
Which stacks do you support?
GitHub, GitLab, Jira, Linear, Slack and common CI systems. The automation connects to the tools you already run — no replatforming.
What access does it need to our repo?
Scoped permissions — typically read on code and write on tickets, comments and docs. Credentials live in a protected store rather than in prompts or logs, and access is revocable at any time.
Can it run on our infrastructure?
Yes — that’s the normal setup for teams with source-code restrictions. We scope the deployment to where your code already lives.
How quickly does it pay for itself?
The audit identifies the workflow with the highest hours-to-effort ratio; most teams start with triage or release notes because the saving shows up in the first sprint.
Get your engineers back to engineering
Free 30-minute audit. We’ll map where your team’s hours go and tell you honestly whether AI is the right fix for it.