PROBLEM 01
The signal arrives too late.
A piece of content breaking out is only useful while it is still breaking out. By the time anyone notices it by scrolling, the window to respond has closed and the idea is everywhere.
CASE STUDY / CONTENT INTELLIGENCE
We built and we run a content intelligence system for a property media business in Australia. It watches a whole content landscape without being asked, works out what is actually breaking out, explains why, and hands the operator a draft in their own voice. It has been running in production since 2026.
CLIENT NOT NAMED AND SOURCES NOT LISTED, BY AGREEMENT. WHAT FOLLOWS IS THE SHAPE OF THE SYSTEM, NOT ITS CONTENTS.
TaskDriver builds and operates production AI systems for businesses that would rather own the machine than rent another dashboard.
TaskDriver.ai is an AI automation and web studio in Auckland, New Zealand. We build workflow automation, chatbots, CRM integrations and websites for small and mid-sized businesses across New Zealand and Australia — fixed scope, no retainers.
THE PROBLEM
PROBLEM 01
A piece of content breaking out is only useful while it is still breaking out. By the time anyone notices it by scrolling, the window to respond has closed and the idea is everywhere.
PROBLEM 02
Keeping an eye on a whole content landscape, across several platforms and two languages, is hours of manual scrolling a day. It is the first task a small team drops, and the one that quietly costs them the most.
PROBLEM 03
Spotting that something worked does not tell you why it worked, or what your version of it should say. That gap between the observation and the draft is where most content teams stall.
HOW IT WORKS
The system runs around the clock on its own infrastructure, pulling public performance data on a fixed cadence. It never sleeps, never forgets a source, and keeps a full history so today can be judged against what normal actually looks like.
Raw numbers lie. A post is measured against its own source's baseline at the same age, so a modest account having an unusually big day is caught, and a large account having an ordinary day is ignored. Platforms that hide view counts are handled on their own terms rather than faked.
Anything that clears the bar goes to Claude for a structured breakdown: the hook, the structure, the emotional lever, the reason this one travelled. The output is a briefing a human can argue with, not a score.
The same pass produces adapted hooks, titles, and a full script, written against the client's own brand guidelines and back catalogue. The operator picks a direction and edits, instead of starting from a blank page.
No new dashboard to remember, no login. The alert lands in WhatsApp, the operator replies in WhatsApp, and the whole pick-a-hook, pick-a-title, approve-the-script flow happens in the thread. A web dashboard exists for the deeper view, but the day-to-day never requires it.
A second system collects published market data as it is released and compiles it into a client-ready monthly report. Research that used to be a full day of chasing PDFs is assembled automatically and reviewed instead of written.
WHAT IT CHANGED
Self-hosted, monitored, with heartbeat checks on every scheduled job.
From a post crossing the bar to a full analysis in the operator's hand.
Including platforms that publish no view counts at all.
The output of a research team, operated from a phone.
THE BUILD
No per-seat SaaS stack, no vendor holding the data. The client owns the system, the history, and the cost curve, and we operate it.
Scheduled workers per platform with per-source history, retry handling, and heartbeat reporting so a silent failure surfaces the same day instead of a month later.
Age-matched baselines per source with hard floors and cold-start handling, tuned so the operator gets a small number of alerts worth reading rather than a feed worth muting.
Model routing by task, cheap models for triage and expensive ones only for deep analysis, with prompt caching on the stable instructions. The cost per analysed post is measured and kept deliberately low.
A WhatsApp bot with access control, claim locks so two people cannot work the same alert, quiet hours, and a state machine that walks an alert from detection to approved script.
The client's own back catalogue, transcripts, and past scripts indexed and searchable, so generated drafts are grounded in what they have actually said before.
Automated collection of published market data plus generation of a formatted monthly report, delivered ready for review.
Watch a stream of public data, decide what is unusual, explain it, and put the answer in front of the one person who can act on it. That pattern fits pricing, hiring, support queues, and compliance just as well. If you have a stream nobody has time to watch, that is the conversation.
The answers people ask for before they book — cost, timeline, ownership, and what happens when something breaks.
Founder & lead architect
Scopes, designs and ships every TaskDriver build — the person on your first call is the person writing the code. No account managers, no handoffs between strategy and delivery.