AI Automation
AI Automation That Removes Busywork, Not People
We automate the repetitive parts of marketing and sales — lead follow-up, reporting, content operations, support triage — and keep a human approving anything customer-facing.
What We Automate
High-volume, rules-based work that already has a clear definition of done.
Lead response & routing
New enquiries get acknowledged in minutes, enriched, scored and routed — with a human taking over for anything unusual.
Reporting & dashboards
Weekly performance summaries written from your own data, so nobody rebuilds the same spreadsheet on Monday.
Content operations
Briefs, first drafts, repurposing and internal review checklists — drafted by AI, edited and approved by your team.
Support triage
Tickets classified, tagged and pre-answered from your documentation, with escalation rules you control.
Integrations
Connecting the tools you already pay for — CRM, forms, ads, accounting, spreadsheets — so data stops being re-typed.
AI search visibility
Structuring content and entity data so your brand is quoted in AI Overviews, ChatGPT and Perplexity answers.
How We Choose What To Automate
Not everything should be automated. We score candidates on three things.
Volume. If it happens twice a week, automation rarely pays for itself. If it happens forty times a day, it usually does.
Cost of a mistake. Low-stakes, easily checked work is a good first pilot. Anything involving pricing, legal or a customer's money starts with a human in the loop.
Definition of done. If two people would do the task differently, the first job is to standardise it. Automating an unclear process just produces bad output faster.
We start with one workflow, measure the baseline (hours and error rate), then decide whether to extend it. No six-month transformation programme before anything works.
Built With Guardrails
Automation that is safe enough to leave running.
- Human approval on anything that reaches a customer, unless you explicitly approve full automation.
- Logging — every automated action is traceable to its input, so failures can be diagnosed.
- Fallbacks — when a model is uncertain or a system is down, work routes to a person instead of failing silently.
- Data discipline — customer data only goes to tools you have approved, with retention rules agreed in writing.
- Measured outcomes — hours saved, response times and error rates tracked before and after.
How An Engagement Runs
One useful pilot beats a roadmap with nothing shipped.
Audit
We map where hours actually go and pick the workflow with the best return.
Pilot
One workflow, connected to real data, running alongside the current process.
Measure
Baseline versus pilot: time saved, error rate, response time.
Harden
Approval steps, logging, fallbacks and documentation for the people who use it.
Expand
Only after the pilot proves itself — usually to the workflow next door.
Review
Quarterly check that the automation still matches how the team works.
FAQs
Still need more detail? Tell us what you're trying to build and we'll answer directly.
Ask a questionIs this replacing our staff?
Which tools do you build with?
How quickly can something be live?
What about data privacy?
Which task eats the most hours every week?
No pitch deck, no 40-slide audit. A 30-minute call about what you need and what it takes.
Find your first automation