Case 01
Signal-based outbound
Two Clay tables. Companies filtered by city, size and industry. People imported from filtered LinkedIn search, enriched, then a formula confirmed a job change inside 90 days.
AI columns did two jobs: confirm the person was a real buyer — VP Sales, Head of BD, C-suite — and strip out anyone already contacted, on the DNC list, or working at a competing staffing firm. Waterfall enrichment for verified email.
Pushed to Lemlist as an omnichannel sequence: email, connection request, chat message, voice note.
Two clients closed in about a month.
Here's a Loom walking through the exact workflow.
Case 02
Deliverability rescue
I owned the whole sending layer — domains, SPF, DKIM and DMARC, warmup, rotation and cleanup — running through Instantly and Lemlist.
Early on, the domains burned out. Manual setups with every tracking marker switched on: open tracking, click tracking, reply tracking. Every one of them adds weight to a message that reputation systems read.
I switched the tracking off and moved the effort to warming accounts properly instead. Deliverability recovered.
Fixed in production, under live campaign pressure.
Case 03
CRM consolidation
The stack overlapped rather than working as one system. Cost was climbing and nobody trusted the data.
I moved the core into
Airtable
14 active automations behind the rebuilt ATS. and rebuilt the candidate database from scratch —
a few thousand records — shaped around how the team actually worked.
Automations carried stage moves, reminders, status updates and handoffs, so the process ran itself. I owned the reporting layer too, so the founder never had to ask for pipeline numbers.
Monthly spend fell from roughly $3–4k CAD to $1–2k CAD.
Those figures are approximate, from memory, and not audited.
