Hand repetitive engineering and business work to automation you control
Teams lose hours every week to repetitive work: triaging tickets, writing routine reports, copying data between systems, running the same operational tasks. DoneDeploy automates that work, using plain scripts and pipelines where they are enough and AI where the task involves reading, summarising or deciding between options. People keep approval over anything that matters, and get their time back for the work only they can do.
From design to implementation, and everything after:
What goes wrong without it
Toil that never ends
Engineers spend their days on routine operational tasks instead of improving the product.
Processes held together by people
Business processes depend on someone remembering to copy, check or send something.
Automation nobody trusts
Earlier scripts broke silently or acted without checks, so the team went back to doing it by hand.
What we do
- Automate DevOps tasks: environment set-up, certificate renewal, clean-ups, access requests and routine maintenance
- Use AI to triage tickets, bug reports and alerts and route them to the right person
- Use AI to read documents and emails and fill in your systems, with a person checking before anything is sent
- Generate routine reports and summaries from your data on a schedule
- Connect tools with APIs and pipelines rather than copy and paste
- Log every automated action so it can be reviewed and undone
What you get
- Automation code and workflows in your Git repositories
- A list of what is automated, what needs approval and who approves it
- Logs and a dashboard of automated actions
- Runbooks for when an automation fails
From first call to continuous improvement
Free first consultation
We talk through the tasks that take the most time and which ones could be automated.
Pick the work
We map the chosen processes and decide where plain automation is enough and where AI adds value.
Fixed-price proposal
We agree the automations, the approval points and the price.
Build and trial
Engineers build the automations and run them alongside the manual process until they are trusted.
Improve after launch
We review results and failures and extend automation where it proves useful.
Claude
GPT
LangChain
GitHub Actions
Ansible
Kubernetes
PostgreSQLRelated articles
From Idea to AI Feature in Six WeeksHow an AI project runs end to end in six weeks: discovery, design, prototype, infrastructure, rollout, then measuring and improving.
Automating Business Workflows with AI, SafelyWhere AI fits in business process automation, and how human review, guardrails and audit trails keep automated workflows safe.
RAG in Production: The Boring Parts That MatterIngestion, chunking, refresh, evaluation and access control: the unglamorous work that decides whether a RAG system can be trusted.Often done together
Frequently asked
Will AI act without anyone checking?
Only where you decide it may, for low-risk steps. Anything that reaches customers, money or production waits for a person to approve it.
Do we always need AI for automation?
No. Many tasks are best done with a plain script or pipeline; we use AI only where a task involves reading, summarising or judgement.
What happens when an automation goes wrong?
Every action is logged, failures raise an alert, and each automation has a runbook and a way to switch it off.
Tell us what you run and what you want to change. We reply personally, and the first consultation is free.
Let's talk »
