[ BUILD BETTER ]

Build your GTM workflow.

Paste a transcript of you talking through one workflow you actually built: the problem, the stack, and what you gave to AI versus kept human. You get a branded board you can download and post, and you can submit it to come on build better and walk it live.

01
Talk it through
Open Wispr Flow (or a voice memo) and walk through one workflow you actually built: the problem, the signals and context you pulled, what you gave to AI, where a human stayed in the loop, and the outcome. Then paste the transcript. Two or three minutes is plenty.
02
Generate the board
Paste it in and hit generate. You get a branded Solve, Stack, Split board, with the tools, the agent-and-human split, and the outcome, laid out like the ones on the show.
03
Save, post, pitch
Copy the link or save the board as an image, drop it on LinkedIn, and submit it to come on build better and walk it live in front of the room.
Talk through these five, in order:
  1. 1 The problem you were solving.
  2. 2 The signals, enrichment, and context you pulled.
  3. 3 What you handed to AI.
  4. 4 Where a human stayed in the loop.
  5. 5 The outcome, and the results it drove.
Easiest way: open Wispr Flow, talk it out loud, and paste the transcript below. No Wispr Flow? Record a voice memo, transcribe it, and paste. Or just write it out.
We use this to put your name on the board and send you the occasional build better build. That is it.
WHAT GOOD LOOKS LIKE

A good walkthrough, and the board it makes.

About ninety seconds, hitting the five beats with real tools and a real number. Talk like this and we can see the whole thing.

THE WALKTHROUGH

So the problem was our AI outbound all sounded the same. Generic openers, no real reason to reach out, replies stuck around 2 percent. The issue was the input, not the model, so I built a context layer first.

Deepline pulls buying signals across the target list: hiring, funding, tech-stack changes. Claude scores each account on fit and timing so reps only see the top. For the ones that pass, Deepline waterfalls the work email across FullEnrich, Findymail, and LeadMagic until one verifies.

Then Octave builds a context pack, three usable sentences from the account's public moves, and a rep adds the one angle the data misses. Claude drafts the opener grounded in that pack, the rep cuts anything generic and sends through Outreach, and replies get tagged back into scoring.

The result: reply rate went from 2 to 9 percent on the same list, and reps got back about six hours a week.

WHAT MAKES IT EASY TO VISUALIZE
  • Name the real tools. Deepline or Gong beats a vague "an enrichment tool." The favicons come straight from what you name.
  • Say the number it moved. Even a rough one, 2 to 9 percent, six hours a week, anchors the outcome.
  • Be clear on the handoff. Where AI stops and a person steps in is the most interesting part of the whole thing.
  • One workflow, start to finish. Not three tips. One real thing you actually ran.
AND IT PRODUCES THIS
[ BUILT GTM ]

Context-grounded outbound

Maya Chen 路 VP Sales, Northbeam
Solvethe problems worth solving
Problem 路 01Signal

AI outbound that all sounds the same.

Generic openers, no real reason to reach out, replies stuck at 2%.

Problem 路 02Drag

Reps burn hours on research.

Manual account digging and email hunting before every send.

Stackthe workflow, box by box
Signal
Deepline01
Pull buying signals
Hiring, funding, and tech-stack changes on target accounts.
Powers up with
AI
Claude02
Score fit and timing
Rank each account so reps only see the top of the list.
AI
Enrich
Find the work email03
Orchestrated byDeepline
Waterfall, first hit wins
FullEnrichtry 1
Findymailtry 2
LeadMagictry 3
ContactOuttry 4
Deepline waterfalls each provider in order until one returns a verified email.
AI
Context
Octave04
Build the context pack
Turn the account's public moves into three usable sentences.
Connect to analyze
AI
The rep05
Add the human read
The one angle the data misses: a real connection or a sharper reason.
Human
Write and send
Claude06
Draft the opener
One idea, grounded in the context pack. No filler.
AI
Outreach07
Cut and send
The rep cuts anything that smells generic, and sends.
Human
Learn
Deepline08
Feed replies back
Tag what got a reply and feed the winning angles back into scoring.
AI + human
Splithow control passes: agents branch left, humans right
HUMAN + AI, IN THE LOOP
How control passes.
AgentsHuman
Agent01
Pull buying signals
Deepline runs it.
Agent02
Score fit and timing
Claude runs it.
Agent03
Find the work email
Providers run in order.
Agent04
Build the context pack
Octave runs it.
Human05
Add the human read
A person owns this.
Agent06
Draft the opener
Claude runs it.
Human07
Cut and send
A person owns this.
AI + human08
Feed replies back
AI drafts, a human checks.
Control passes back and forth. Agents run the volume, a human owns every call that needs judgment.
Outputwhat the workflow produces
OUTPUT 路 01

A ranked account list

Scored on fit and timing.

OUTPUT 路 02

A context pack per account

Three usable sentences plus the rep's angle.

OUTPUT 路 03

A verified email and opener

Ready for the rep to cut and send.

Outcomethe result it drives
OUTCOME 路 01

9% reply rate

On the same list. Up from 2%.

OUTCOME 路 02

~6 hours saved

Per rep, per week.

OUTCOME 路 03

More pipeline

From the same team, same list.

Built GTM