A few of the engagements.

Different industries, similar shape. The same patterns turn up across teams. Each of these is described by industry rather than name. Specifics are available on a call.

Real estate Marketing operations

Paid media monitoring across hundreds of campaigns

A listed real estate developer was running hundreds of campaigns across Meta and Google Ads. Analysts were spending one to two hours a day on manual monitoring. Underperforming campaigns took too long to surface, and reporting was inconsistent enough that decisions slowed down.

We mapped the team's workflow, identified the redundant steps and built a Zapier and API setup across both ad platforms. Campaign-level and metric-level success criteria were defined together with the team. Escalation rules went to the right owners through Slack and email, tuned to avoid alert fatigue.

Reporting time dropped from hours to minutes. Around 20 to 25 hours a week saved across a three-person team. Faster detection of underperforming campaigns improved return on ad spend.

Fintech Content operations

Scaling content output without scaling the team

A financial services firm was capped at around 10 blog posts a week. Research was the bottleneck. Hiring more writers wasn't sustainable, and the editorial standard wasn't something they were willing to drop.

We built a research and drafting pipeline using Google Trends, Browse AI and the OpenAI API, with a human editor stage built in for tone, accuracy and final polish. The handover included Loom walkthroughs and cost-tracking sheets so the team could run it without depending on me.

Output scaled to 10 blogs a day. Around 80 hours a week saved. Quality held. Traffic and lead generation scaled with it.

Edtech Placement operations

Cutting placement evaluation from 20 minutes to under two

An edtech operator's placement team was manually evaluating student applications against job openings. The process was subjective and inconsistent. Candidates often missed relevant opportunities because evaluation was rushed.

We built a Job Fitness Score model that scored candidates against role criteria with weighted logic for experience, skills and qualifications. Results landed in a dashboard that summarised fit, strengths and improvement areas. The platform later extended into AI-driven mock interviews for both text and audio formats, with adaptive difficulty and section-wise feedback.

Per-candidate evaluation dropped from around 20 minutes to under two. Job description processing fell from four hours to one and a half. Assessment setup time cut by 90 percent.

Professional services Operations

Real-time client onboarding tied to payments

A consulting firm was triggering CRM onboarding manually after each payment. Hour-long delays were normal, day-long delays weren't unusual. Clients sat without confirmation, which sometimes meant calls or emails outside working hours that the team couldn't respond to in time.

We mapped the Stripe to CRM to email flow, built a Zapier workflow that linked them and tested it end to end with dummy clients before going live. Full handover with Loom walkthroughs and support docs so the team could self-manage from there.

Manual steps eliminated. Onboarding emails go out within minutes of payment, including outside working hours. Around 5 to 10 ops hours a month saved, and customer trust improved at the moment it matters most.

More engagements behind these. Some are quieter, some live under NDA, some I'd rather walk through on a call than write up. If a specific industry or use case matters to you, that's a good place to start the conversation.

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