We did not pitch a single service on stage at AI Transform. We showed what we had built for ourselves. That is the whole strategy, and it is the only one we believe in.
The thinking behind it is not complicated. If you will not run something on your own business, you have no business running it on someone else’s. And if you have run it, you do not need to pitch it. You just show it.
Here are five things currently running inside Green Gorilla Automation, what each one does, and what it honestly took to get there.
1. Voice of the Market
Every sales call, discovery session, and client meeting is full of insight: the exact words people use to describe their problems, the objections they raise, the questions buried inside the things they are not quite asking directly. For years, that insight lived in recordings nobody went back to.
Now, at 6am every morning, an automation sweeps the previous day’s meetings, skips anything it has already processed, and files every pain point, objection, question, and win as its own entry in an insight library. Word for word, linked back to the call it came from.
On Monday at 7am a second automation reads the fresh insights, checks them against every piece of content already published, and suggests the week’s topics. A third builds LinkedIn content from the same material, so both channels say the same thing in the same week. A fourth reads every meeting summary and turns agreed actions into tasks, so nothing said on a call gets lost.
Since January, the system has filed 1,495 insights. 809 came directly from prospects and customers. The top themes: price and budget, systems that do not talk to each other, skills gaps, trust in AI output, and how long results take. Fear of AI came up 14 times. Under 2%.
“Something breaks and then I can’t unbreak it.”
That is a real quote from a real prospect, filed word for word. It is not a question. It is a pain point with a question hiding inside it. The system catches it so the content can answer it.
2. Support Ticket Triage
A support email lands. A card is created on the development board automatically. Then AI agents read the codebase and return the likely affected files, the probable root cause, a suggested fix with a confidence level, and a draft reply to the client.
Ticket handling went from most of a working day to minutes. The system triaged its first real ticket on 11 February. In a live client session it diagnosed an intermittent upload bug that the team had failed to reproduce for weeks.
What it actually took: building it properly required the agents to understand the codebase structure, not just search it. That took iteration. The draft replies also needed calibrating so they were useful starting points rather than something that needed rewriting entirely.
3. Lead Scaffolding and Pre-Call Plans
When a prospect books a call, the full client structure is built in Notion automatically. Before the diary is even opened, AI has already written the pre-call plan against the existing playbook.
After each stage of a client engagement, the system writes the summary and the plan for the next stage. The paper trail that used to get written up retrospectively, if it got written up at all, now exists before the meeting ends.
The honest part: this only works well because the playbook it works from was written carefully. Garbage in, garbage out applies here as much as anywhere. Getting the playbook into a shape the system could actually use took longer than building the automation itself.
4. Multi-Agent Code Review
Four agents review every pull request: one for style, one for security, one for business logic, and a manager agent that reads all three and decides pass or fail. Morning code review went from over an hour to five to ten minutes. Around half of pull requests now merge automatically.
It took three attempts to get here. CodeRabbit first, then Codex, then a custom-built version. Back with CodeRabbit now, for what it is worth.
The lesson from the three attempts: off-the-shelf tools that fit the workflow are usually better than custom builds that do the same job. The temptation to build your own is strong. Resist it until you have a reason the existing tools genuinely cannot meet.
5. Automated Tests on Legacy Code
Fourteen years of software development projects. In a billable-hours model, clients rarely paid for automated tests, and they rarely got them. Test coverage across the codebase sat below 30%.
AI-generated test suites took that from below 30% to 80% coverage across projects. The new tests immediately flagged a bug that had been sitting in production for a year undetected. Bugs are now rare enough that GGA can offer unlimited bug fixes as standard.
What it took: time, mostly. Not glamorous time. The kind that involves reading output, adjusting prompts, and running the tests again. The coverage improvement did not happen in a weekend.
What All Five Have in Common
None of them arrived finished. All of them started small and slightly broken and improved through daily use. One of them still creates every task at Critical priority regardless of what it is told, which is a large part of how the Critical list reached 126 items against a cap of five. That is being fixed.
The pattern across all five: the systems built to solve a real internal problem, quietly, without anyone watching, turned out to be more useful than anything built to impress. That is usually how the best ones get built.
Want to See How Any of This Works?
Our free 45-minute Operational Audit is the place to start. We will show you what we actually run on, rough edges included, and talk through what something similar might look like for your business.
Book yours at greengorillaautomation.co.uk.
