The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
25% reply rate - 51 C-Level Meetings in 60 days
Set up the full outbound system in Lemlist from scratch, from reply playbook to Claude cowork skill that can handle replies. ICP targeting, sequence logic, copy, follow-up cadences. Sitting at a 1.5% meeting rate across three markets (Albania, Croatia, Slovenia) with no sales headcount (51 meetings
booked with biggest companies creating a 600K+ pipeline in 60 days)
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Problem: Barbershop taking bookings on Instagram DM, no-shows 40%, no client history, manual payment tracking.
What I built:
→ Client booking website - pick barber, service, time slot, pay deposit
→ Automatic SMS/WhatsApp reminders to reduce no-shows
→ CRM dashboard - client history, favorite cuts, lifetime spend, retention
→ Admin panel - barber schedules, daily sales, payouts
→ Stripe , secure payments & auto receipt.
Result: Bookings 24/7 without DM, no-shows dropped to 5%, owner sees revenue from phone.
Great work 🔥
On a phone call, silence is the bug.
A text chatbot can take three seconds to reply and nobody minds. A voice agent can't. Leave a caller in silence for a beat too long and they say "hello?", start talking over it, or hang up.
That one fact shaped everything we built on Talk-Lee, an AI voice agent that answers business calls for healthcare, real estate and finance teams. Scheduling, support, lead qualification, around the clock.
The goal was a reply in under 500ms. You don't get there with a faster model. You get there by making sure nothing waits for anything else.
→ Speech to text streams while the caller is still talking
→ An LLM and intent layer keeps track of what they actually want
→ Text to speech streams back, so the agent starts talking before the whole answer is ready
→ An orchestrator decides in real time whether to answer, book or hand off to a person
Then it has to do something useful. It books into Calendly, logs the lead in HubSpot, and passes the hard calls to a human with the context already attached.
Where it landed. Under 500ms responses, 1,000+ concurrent calls, 30+ languages, GDPR and TCPA compliant.
Third project in a row with the same lesson. The model is the easy part. The plumbing around it decides whether anyone keeps using it.
Congrats on top 1% Franco! Amazing achievement 👏
Quick question if you don't mind - I'm new on Contra (UGC + 3D product animation niche). My PTS is low right now. What was the 1 thing that helped you the most to increase your score from 0 to 100+?
Would love to learn from your journey!