When OFO Collective started working with this marketing agency in early 2026, the team was sending 28,000 outbound emails a month. By hand.
That sounds wrong. It was. The team had a system in the sense that a sequence of manual steps strung together is technically a system. What they had was 140 hours of senior-rep time per month going into list sourcing, copy-paste enrichment, CRM upload, and one-by-one sending. The CRM was 54% deletion-rate dirty. Most of the email traffic landed in spam.
This is the build that fixed it. The result was a 94% reduction in manual hours — 140 hours per month down to 5 — at the same monthly cost, with the capacity to send 100,000 emails per month if needed.
If you run a B2B agency in Australia doing outbound at any volume, the components below will be familiar. The interesting part is how they wire together.
Where the time was actually going
Before the build, the workflow looked like this:
- A senior rep picked an industry and screen location for the month
- They opened Apollo, Google, LinkedIn, and a handful of niche directories
- They pulled lead lists by hand — 1,000 to 2,000 per campaign
- The lists were copy-pasted into Google Sheets for cleaning
- The Sheets were imported into HubSpot
- Each lead was manually enrolled into an email sequence
- The rep watched replies and updated CRM status one record at a time
The work was real. It was also senior-rep work being done at junior-rep pace. The 140-hour estimate was the team’s own — and it was probably understated, because nobody was logging the LinkedIn-tab time honestly.
The other problem was data quality. Manual entry produced typos, format mismatches, duplicate records, and field collisions. Of the 65,000 contacts the agency had accumulated, 54% were eventually deleted because of bad entry. That percentage is what told us the workflow needed a rebuild, not a tune-up.
The build: stack and architecture
The system runs on three tools you have heard of and one layer of AI you have not (because we wrote it).
Apollo is the lead source and enrichment engine. The agency was already paying for it. What we changed was how the platform was used — from manual searches to programmatic queries triggered by a single internal form.
HubSpot is the CRM and sending platform. Again, already paid for. We restructured the lifecycle stages, sequence enrolment logic, and reply-routing so that the rep’s inbox only sees interested replies.
Clay runs the enrichment and waterfall logic between Apollo and HubSpot. It is the connective tissue that lets a list move from sourced to enrolled without a human hand touching it.
Custom AI layer — built on Anthropic Claude — handles two things: personalisation copy and reply triage. The personalisation is the part most outbound tools get badly wrong. We trained the model on the agency’s historical reply data so the openers sound like the agency, not like every other Apollo user’s outbound.
The architecture on a page:
- Rep opens a one-screen interface, picks an industry and a location
- Apollo runs a programmatic search and pushes raw leads into Clay
- Clay enriches, deduplicates, and validates against the existing HubSpot database
- Clean leads write to HubSpot with the right lifecycle stage
- The AI layer generates the personalisation tokens for each lead
- HubSpot enrols the lead into the relevant sequence
- Replies route based on intent — interested replies land in the rep’s inbox, out-of-office and disinterested replies handled automatically
What changed for the team
The 94% time saving is the headline. The shape of the change underneath it matters more.
The senior reps stopped being data-entry clerks. They became closers. Their inbox got smaller and the quality of every email in it went up. Sequences started running on auto. The rep’s job became responding to interested replies and making the call — which is what they were hired for in the first place.
The data quality problem inverted. The new system writes clean records into HubSpot because the cleaning step happens before the write, not after. The 54% deletion rate stopped accumulating from the day the system shipped.
Capacity went up without headcount going up. The agency now has the infrastructure to send 100,000 emails per month — about 4x the pre-build volume — at the same operational cost. They have not used the full capacity yet because they did not need to. The point is that the system scales with the next campaign, not with the next hire.
What did not work and what we learned
A clean case study reads like everything ran on rails. The build did not run on rails.
Two things broke in the first month and are worth naming:
The first reply-triage model over-routed. Our first version of the AI reply-triage was too generous — too many “polite no” replies were being routed to the rep’s inbox as if they were interested. The rep’s inbox got noisier for a week. We retrained on a fresh batch of labelled replies, tightened the threshold, and the noise dropped to expected levels.
Sequence delivery was punished by domain reputation. The agency’s domain reputation was already taking hits from the previous manual workflow. Even with clean data, the first two weeks of sequences saw lower-than-expected inboxing rates. We rotated to a dedicated sending domain, warmed it properly, and the inboxing rates climbed back over the following month. The lesson: a clean stack does not undo a poisoned reputation overnight.
Both fixes were inside the 30-day trial. Neither cost the client a separate engagement.
What this means if you run a similar agency
The build above is replicable. Apollo, HubSpot, and Clay are platforms most agencies either already pay for or could adopt for a small monthly cost. The AI personalisation and reply-triage layer is the part that needs custom build — it is not a SaaS product you buy off the shelf, because the value comes from training it on your agency’s historical data.
The reason the build pays back fast is that the manual workflow it replaces is almost always senior-rep work. Senior reps cost $120,000 to $200,000 a year. Recovering 135 hours of their time per month is a six-figure annual recovery on a 4 to 5 figure trial investment.
If your agency is doing manual outbound at any meaningful volume, the question is not whether to build something like this. It is who builds it and how long it takes to ship.
OFO Collective ships this kind of build on the 30-day trial. The full case study with metrics and quotes is at lead-generation-automation. If you want to talk through what a similar build would look like for your stack, book a call.