Case study
Find, research, draft — a person sends.
A multi-agent pipeline built for a digital marketing agency.
The problem.
Finding local businesses with weak online presence, auditing each, judging affordability, and writing a tailored assessment and pitch took a person most of a day per lead.
What was built.
Five agents in sequence: a finder that sources candidates; a checker that audits website, search presence and listings and scores fit on two axes; a builder that produces a written assessment and an “old vs new” deck; a reviewer that checks every claim against brand rules; a notifier that pings the owner. One lead at a time, each in a fresh context.
The governance.
The batch stops at drafts. Nothing reaches a client unseen. A runner verifies every claimed file exists on disk before recording a step as done — an agent can't report work it didn't do.
What it costs to run.
Each lead costs a small amount of AI usage — more for deep research, less for a quick check — and nothing else: no subscriptions, no per-seat licences. That figure is stated to the client at their expected volume, in writing, before the pipeline is switched on, the same way we'd state it to you.
What we'd change.
Contact discovery was the gap — the pipeline found companies, not phone numbers. The next version resolves contacts in the research step.
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