Before Working With Us
Juno is an AI-powered platform that helps tax accountants submit returns faster and more accurately. When we met them in March 2025 they were doing ~£20k/month, and growth came entirely from referrals and inbound - powerful, but lumpy and impossible to model.
- Referral- and inbound-dependent - every new deal depended on who they already knew
- No offer built for cold traffic - what worked on warm intros fell flat on strangers
- No repeatable outbound motion; the founder + COO were wearing every hat
- One-off lists and generic messaging that never got off the ground
- No way to turn “we need more pipeline” into a predictable number of meetings
The 3-Month Proof of Concept
We started with a focused 3-month proof of concept: one channel, cold email, done properly. Craft the offer, launch at real volume, then scale the winners.
1. Craft the Offer
We rebuilt the offer specifically for cold traffic - an angle a stranger would say yes to. We tested multiple offers and personas to find the message that actually landed. To go deeper than generic lists, we submitted a FOIA request for public IRS data and built a custom prompt to identify firm domains and decision-makers directly, so every message tied back to each firm’s real context.
2. Launch at Volume
Launched cold email at 50,000/month on dedicated, pre-warmed inboxes landing in the primary inbox, with all data flowing into HubSpot via n8n. LinkedIn automation via HeyReach ran alongside. Within weeks we were booking ~20 meetings/month and generating £200k-£300k in pipeline.
3. Scale the Winners
Once we had winning offer/persona/list combos, we pushed volume higher and doubled down on what converted. Meetings became consistent; outbound was no longer the constraint. That was the moment we knew the POC had proven itself - and it was time to add fuel.
Then We Poured Fuel On What Worked
Only after the POC proved out did we layer on the rest. As pipeline grew, the real bottleneck became conversion - not lead flow.
We ran a full RevOps audit on-site in Philadelphia, then rebuilt the revenue system around conversion and enablement:
- HubSpot pipeline redesign with clear sales-led vs self-serve motions
- Usage-based nurture sequences and automated follow-ups (~15 hrs/week saved)
- Fireflies call transcription auto-logged, AI-generated summaries + pre-call agendas
- Partner quoting tool (no HubSpot access needed) that unlocked hundreds of thousands in partner-driven revenue
- Sales engineer AI agent trained on Juno’s knowledge base - ~30 hrs/week of senior engineering time saved
The result: free trial → paid conversion up ~50% with no new hires.
Outcome
- Revenue scaled from ~£20k/month to a £700k record month
- Sales remained lean while pipeline quality improved
- Founders and engineers were removed from day-to-day deal friction
- Outbound created momentum, but systems unlocked scale
Key Takeaway
Juno didn’t have a demand problem. They had a capture and conversion problem. Outbound helped them start - but revenue only scaled once sales enablement, automation, and RevOps systems were fixed.
The Results
| Metric | Before | After |
|---|---|---|
| Monthly Revenue | ~£20k/month | £700k record month |
| Pipeline Generated | No outbound motion | $6.5M total |
| Meetings Booked | Almost none | 87 qualified meetings |
| Trial-Paid Conversion | Manual, inconsistent | ~50% lift, no new hires |
| Sales Team Capacity | Founders in every deal | ~45 hrs/week saved via AI |
Key Learnings
Signal Beats Volume
Signal-based targeting unlocked hyper-relevant messaging that generic list-builders couldn't match.
Outbound Reveals the Real Bottleneck
Meetings grew fast. The next constraint wasn't pipeline - it was conversion.
On-Site Changes Everything
Sitting with the sales team in Philadelphia surfaced problems no Slack audit ever would.
Systems > Headcount
AI agents and automations scaled revenue 9x without adding a single sales rep.