AI automation
AI automation that
gives your team hours
back every week
Custom AI and Python workflows for faster reporting, lead handling and content operations—built around the tools your team already uses.
- Custom-built in Python
- Works with your current tools
- Monitoring after launch
Turn repetitive work into reliable workflows
Save meaningful time
Remove repeated manual steps from reporting, lead handling and content work.
Keep human control
Add review and approval points wherever your team needs them.
Build for reliability
Use validation, monitoring and alerts so failures are visible—not silent.
From bottleneck to working system
Audit the workflow
We map the current process, pain points and success criteria.
Build and validate
We build the workflow in Python, add checks and test end-to-end.
Launch and monitor
We go live, monitor performance and iterate as needed.
The approach
How the work actually runs
What this is
Custom automation for the marketing work that eats hours and produces nothing anyone enjoys doing. Reporting pipelines, lead handling, content operations — built specifically around the tools you already use, rather than bolted on from a template.
How I work on it
First a workflow audit, because most of the value is in picking the right target. Plenty of repetitive work is not worth automating: it happens rarely, or the inputs are too inconsistent, or the time saved does not justify the build. I would rather tell you that up front than build something that pays for itself in four years.
Once we have picked a target, the build is deliberately boring: stages that can be run independently, raw inputs cached before anything transforms them, and validation at every step. That is what makes it still work in eight months.
Who it suits
Teams losing meaningful time — days a month, not minutes — to work that follows the same steps every time. It suits you less well if the process changes constantly, in which case the process is the thing to fix first.
What's included
Every engagement carries these
- Workflow audit — finding the repetitive work that is actually worth automating
- Custom Python and AI automations built around your existing tools
- Automated reporting pipelines that pull, transform and deliver on a schedule
- Lead handling — routing, enrichment, follow-up and CRM sync
- Content operations: briefs, drafts and publishing workflows
- Monitoring and alerting, so a broken job is loud rather than silent
Common questions
Before you book the call
What kind of work is worth automating?
Anything repetitive, rule-based, and fed by inputs that arrive in a predictable format. Monthly reporting, lead routing, data pulls between tools. Work that needs real judgement about a specific client stays human — but the machine can do everything up to where the judgement starts.
Will an AI be writing things and sending them without me seeing?
Not unless you want it to. The default is a draft-and-review setup: the automation does the assembly and the blank-page work, you approve. Anything involving numbers is computed by code rather than generated by a model.
What does it run on?
Usually a small server of your own, on a schedule. You own the code and the box. Nothing is locked to a platform you cannot leave.
How do I know it will keep working?
Every job validates its own inputs and fails loudly if something is off. A reporting job that silently produces last month's numbers is worse than one that crashes, so they are built to crash.
See what it costs in packages and pricing, how the phases run in the process, or what it moved on real projects in the case studies.
Ready to cut the manual work?
Let's find the workflows where automation will make the biggest difference.