Real systems. Real numbers.
Before and after. Every case study includes the problem, the solution, the metrics, and the tools we used.
Cut proposal time by 80% with an AI agent
The team spent 5 hours per proposal — pulling data from Notion, formatting in Google Docs, and emailing manually. 3–4 proposals per week meant 15–20 hours of non-billable work.
An AI agent pulls deal data from the CRM, generates a formatted PDF proposal using a GPT-4 template, and emails it to the prospect — in under 4 minutes.
"We went from dreading proposals to sending them same-day. The pipeline doubled in 6 weeks."
3× reply rate on leads with automated follow-up
Inbound leads from the website were being followed up manually by the sales team — sometimes 2–3 days late. Reply rate was around 10%.
A multi-step AI agent sends a personalised follow-up email within 5 minutes of form submission, followed by a LinkedIn connection request and a second email at day 3.
"Our pipeline filled up in the first two weeks after we deployed. I wish we'd done this 12 months ago."
12 hrs/week saved on reporting and data entry
Every Monday, the ops manager spent half the day pulling data from 4 different tools into a weekly report spreadsheet. Errors were common.
A scheduled automation pulls data from all 4 tools, formats the report, and posts it to Slack every Monday at 8am. Zero manual work.
"The team finally has time to do actual work instead of copy-pasting data."
Content engine producing 20 SEO posts per month
The marketing team was producing 2–3 blog posts per month due to bandwidth. SEO traffic was flat.
An AI content pipeline generates keyword-researched outlines, drafts long-form posts, adds internal links, and publishes to WordPress — with a human review step built in.
"We're now ranking for keywords we couldn't even target before. The ROI on this is insane."
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