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.
- GGround. The problem you were solving, plus the receipt that proved it was real (the number, the failure, the cost) and what doing nothing was costing.
- IIdentify. What you had to figure out before you could build: what data you needed and where it lives, where a human had to stay, what good even looks like.
- AAssign. The steps in order, the actual tools by name, and who runs each one: AI, human, or both. Say where control passes.
- NNormalize. How it spread past you: the pilot, the write-up, the moment it became the default, how new people inherit it.
- TTie back. What it produces and the numbers it moved. Even a rough number beats none.
A good walkthrough, and the board it makes.
2 or 3 minutes, hitting all five GIANT beats with real tools and a real number. Talk like this and the board builds itself.
So the problem was our AI outbound all sounded the same. Generic openers, no real reason to reach out, replies stuck around 2%. That is a lot of sends for silence. The issue was the input, not the model.
Before building I had to answer 3 things: which signals actually predict a reply, where the account context lives, and where a human had to stay in the loop so nothing generic ships.
The build: Deepline pulls buying signals across the target list, hiring, funding, tech-stack changes. Claude scores each account on fit and timing. For the ones that pass, Deepline waterfalls the work email, FullEnrich first, then Findymail, then LeadMagic, first hit wins. Octave builds a context pack, 3 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.
On adoption: 2 reps piloted it for 3 weeks, we wrote the one-pager, made it the team default at kickoff, and every new rep runs it in week one. We review misses every Friday.
The result: reply rate went from 2% to 9% on the same list, and reps got back about 6 hours a week. We know because every reply is tagged to its angle on the Monday dashboard.
- Give the problem a number. "Replies stuck around 2%" is a Ground card. "Outbound was rough" is not.
- Name the real tools, in true order. "FullEnrich first, then Findymail, then LeadMagic" becomes a live waterfall run on the board. A vague "an enrichment tool" becomes nothing.
- Say who runs each step. Where AI stops and a person steps in is the most interesting part of the whole thing.
- Tell the motion. The pilot, the write-up, the moment it became the default. A workflow without a motion is dead, and the board will say so.
- Close with how you know. "2% to 9%, tagged on the Monday dashboard" ties the result back to the build.
Context-grounded outbound
AI outbound that all sounds the same. Generic openers, no real reason to reach out, and the model was never the problem. The input was.
They fixed the context going in, not the words coming out.
Replies stuck at 2%.
That is a lot of sends for silence.
Hours of manual research per rep, per day.
Account digging and email hunting before every send.
Which signals actually predict a reply?
Hiring, funding, and tech-stack changes. Everything else was noise.
Where does account context live, and how does it reach the draft?
Public moves, distilled by Octave into 3 usable sentences per account.
Where must a human stay in the loop?
The rep adds the angle the data misses, and cuts anything generic before send.
What breaks at volume?
Never answered. This is a question for the interview.
The signals, the scoring, the email waterfall, the context pack, and the first draft.
The one angle the data misses, and the cut on every send before it goes out.
Details
Details
Details
Details
Details
Details
Two-rep pilot
2 reps ran it for 3 weeks while the rest of the team kept the old motion.
Write the one-pager
The play, the tools, and the review rules, written down once the pilot numbers landed.
Make it the default
Rolled to the whole team at kickoff; the old sequence list was retired the same day.
Bake into onboarding
Every new rep runs it in week one, paired with a rep who has shipped it.
Friday review
15 minutes on the misses every Friday keeps the scoring honest.
9% reply rate
On the same list. Up from 2%.
~6 hours saved
Per rep, per week.
More pipeline
From the same team, same list.
A ranked account list
Scored on fit and timing.
A context pack per account
Three usable sentences plus the rep's angle.
A verified email and opener
Ready for the rep to cut and send.
Deepline waterfalls each provider in order until one returns a verified email.
{ "account": "acme.com", "fit_score": 84, "signal": "hiring 4 SDRs", "contact": "Jane Rivera, VP Sales" }
FullEnrich -> work email: miss Findymail -> jane@acme.com (hit) LeadMagic -> skipped, first hit wins ContactOut -> skipped, first hit wins
{ "account": "acme.com", "contact": "Jane Rivera, VP Sales", + "work_email": "jane@acme.com", + "verified": true }
Deepline runs the vendors in order and stops on the first verified hit.
One idea, grounded in the context pack. No filler.
{ "contact": "Jane Rivera, VP Sales", "context": [ "hiring 4 SDRs this quarter", "just moved off a legacy dialer", "rep angle: spoke at Pavilion on ramp" ] }
Subject: 4 new SDRs and a 90-day clock Jane, saw the 4 SDR openings. Most VPs tell us ramp is where the quarter goes to die. Your Pavilion talk said the same. We cut ramp to about half at teams your size. Worth 15 minutes?
The actual artifact, not metadata. The rep reads exactly this and cuts anything generic.