◆ Evergrowth · Explainer

How Evergrowth works

An agentic GTM workspace, explained for the whole team. What it is, how it works, what it costs, and the business case, in one page you can read on screen or forward as a PDF.

Prepared for your team
12specialist agents + Eva
5-minread
Nosign-up · built to forward
Why this matters

The old GTM playbook is broken

69%
reps missed quota (2024)
20+
siloed tools per team
70%
of a rep's week not selling

Data is commoditized: everyone buys the same lists and runs the same enrichment. Playbooks stay trapped across Docs and spreadsheets, and reps burn hours building lists instead of talking to buyers.

Bolting AI onto siloed tools just amplifies the silo. It doesn't fix it.
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The shift · today

AI bolted onto silos

How most teams run GTM today: 20+ tools with AI bolted on, a GTM-engineer bottleneck, and reps who spend most of the week not selling.

AI just builds more silos
no agents
Leadership
no agents
Sales
no agents
Marketing
no agents
RevOps
20-40+
tools in your stack
70%
of rep time not selling
12+ hrs
manual work a week
GTM engineerRuns the heavy AI. Everyone else waits.
CRMs, sequencers, dialersUnactionable data · data ≠ context
More silosAI bolted everywhere0 agents
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The shift · with Evergrowth

Agents and humans, working together

The same team on Evergrowth: an orchestration layer of agents and a shared intelligence layer sit between your people and your tools.

70% selling · 0 hrs manual · 10x productivity
70%+
selling
0 hrs
manual work
10x
productivity
agents
Leadership
agents
Sales
agents
Marketing
agents
RevOps
The orchestration layer
Research
Account qualificationAccount researchContact finderContact qualificationContact researchEmail and phone waterfall
Personalization
Account planningPlay copywriting
Coaching
Voice roleplay and coach
The intelligence layer · Agent Training Center
ICP verticalsPersona cardsValue proposition
Unlimited agentsNo more silos
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Chapter 01

What Evergrowth is

An agentic GTM workspace. You talk to Eva, an AI colleague, who runs 12 specialist agents that research accounts, qualify buyers, find contacts and draft outreach, grounded in your data, not the open internet.

It is
  • Digital colleagues trained on your GTM
  • Agents that work autonomously and together
  • One workspace the whole team shares
  • Grounded in your data, with a full audit trail
It is not
  • A chat box plus an LLM
  • A shared GPT or prompt library
  • Another data vendor
  • Open-internet guessing
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Chapter 02

How it works: one workspace

RevOps trains it, Eva runs the agents, reps get the output. Here's the workspace working a single account:

Agent Training Center
Value props
Ecosystems & verticals
Persona cards
ACME, INC
ICP: YesScore 65%
Account qualification✓
Account research13
Jane Doe
IBP: Yes
Contact finder4
Contact qualification✓
Sales rep output
Play copywriting
Context-driven outreach from the research
Voice roleplay
Practice the call against an AI buyer
RevOps
RevOps
Trains
 Eva
Eva
Runs the agents
Agents12
Sales rep
Sales rep
Sells
1 · Research Qualify, research, find and verify contacts across 20+ vendors.
2 · Interactions Account plans + email, LinkedIn and call scripts per contact.
3 · Coaching Practice against AI personas, scored on your playbook.
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Chapter 03

A workflow, end to end

The Newbiz playbook turns a list of company names into ready-to-send outreach, before a rep ever logs in:

1
Account Qualification

Scores each company against your ICP using LinkedIn, Crunchbase and external data.

↓
2
Account Research

Deep research on qualified accounts: signals, tech stack, hiring, funding, news.

↓
3
Contact Finder

Finds persona-fit contacts at each account via waterfall vendor access.

↓
4
Contact Qualification

Verifies employment and builds a DISC communication profile.

↓
5
Play Copywriting

Writes cold email, call script and LinkedIn message per contact, from research, not templates.

→ The rep opens a ready brief with verified email and phone, synced to the CRM, in minutes, not days.
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Chapter 04

Why the output is different

Data is commoditized: the only advantage left is what you do with it. Context isn't a field you insert; it's a chain of specialist agents, each adding a layer the next builds on.

Old model · data-driven

20+ tools, no context

Data variables dropped into templates
Spray-and-pray volume; prospects feel scraped
GTM engineer as the bottleneck
→
Today · context-driven

One workspace, deep context

Openers and questions calibrated to research
Signal-based quality; prospects feel understood
RevOps and reps share the same agents
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Chapter 05

What it means for your role

RevOps
GTM Architect

RevOps

You build the playbook. Agents execute it at scale. Define ICPs and personas, schedule workflows, all in plain language.

 Sales rep
The seller

Sales reps

Research done. Plays written. Inbox ready. Reps review and send, and spend their time selling.

 CRO
The owner

CRO

Pipeline visibility, rep productivity and GTM efficiency in one workspace.

 Marketing
The partner

Marketing

Same intelligence, same playbook, finally aligned with Sales, no lead-quality friction.
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Chapter 06 · for the person signing off

The business case

Old GTM model
Team8 SDRs + 8 AEs
Quota attainment31%
ARR achieved$2.2M
Total GTM cost$1.85M/yr
$0.84
Cost per $1 of revenue
AI-augmented model
Team2 SDRs + 4 AEs + Eva
Quota attainment90%
ARR achieved$3.5M
Total GTM cost$878K/yr
$0.25
Cost per $1 of revenue
3.4×
GTM efficiency
−60%
headcount
−53%
GTM cost
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Chapter 07

Proof

"Now with agentic AI, the platform does the work for you 24/7. That's the game changer. You need to jump on it now to stay ahead of the trends."
Sven Roeleven
Sven Roeleven
SVP Solution Management, ARIS
research: hours → seconds
"I'm basically buying the knowledge and the research you did. Accurate data is much more important than a lot of data which is not qualitative."
Jonathan Wuurman
Jonathan Wuurman
VP Growth, Luzmo
89 → 37 ICP-fit accounts
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Chapter 08

What it costs

Credits, not seat licenses. Every plan includes unlimited users, Eva and all 12 agents, and all features. You only pay for what the agents actually do.

Starter
€747
3,300 credits / mo
Pro
€1,957
15,400 credits / mo
Pro+
€4,587
41,700 credits / mo

Account Research 0.5-1.5 cr · Contact Finder 1-3 cr · Play Copywriting 1 cr · Email waterfall €0.04 per verified hit · yearly plans 10% off · Enterprise custom. Full breakdown at evergrowth.com/pricing.

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Chapter 09

How rollout works

1
Setup · weeks 1-3 (Evergrowth-led)
Workspace deployed, CRM connected, Agent Training Center configured.
2
Tune · weeks 4-6 (reverse onboarding)
Agent output validated and fine-tuned against your sales process.
3
Rollout · weeks 7+ (client-led)
Reps run AI-augmented plays; supported by office hours and weekly check-ins.
→ Time to value is 1-3 weeks, with roughly 5 hours of your team's time during setup.
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Chapter 10 · "can't we just build this?"

Why not build it internally

Building your GTM engine in-house is 10 systems, 5 disciplines and 12+ months of work, and the build is never done, because models, data vendors and APIs keep shifting under you. You would own and maintain all of it:

⚙Orchestration
⚙Scale & speed
⚙Scheduling
⚙Trust & quality
⚙Research pipeline
⚙Contact data (20+ vendors)
⚙CRM sync
⚙Model routing & cost
⚙Governance
⚙Compliance (GDPR / SOC 2 / ISO)
→ Evergrowth already built and maintains all ten as one workspace. You keep only your GTM logic, ICP, personas, plays, and your team never touches the plumbing.
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Chapter 11 · "why not Clay / ZoomInfo / ChatGPT?"

How Evergrowth compares

ChatGPT & ClaudeData vendorsClayEvergrowth
What it isChat + LLMStatic listsSpreadsheet + APIAgentic GTM workspace
Contact findingNo database accessJob-title filtersManual BooleanPersona-matched, autonomous
Research & signalsYou browseFirmographicsManual recipesCustom signal-scoring agents
OutreachGeneric draftsTemplate variablesTemplate variablesContext-driven plays
Who runs itEach individualEach repOne GTM engineerRevOps sets up · reps self-serve
Team & scaleIsolated chatsManual, per repOne-operator bottleneckShared workspace, parallel agents

Clay consolidates tools; Evergrowth augments the team. Data vendors give you lists; Evergrowth gives you context. ChatGPT and Claude run the same models, the difference is what wraps around them.

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Chapter 12

Common questions

Isn't this just ChatGPT or Claude?
Same frontier models, but wrapped in trained agents with your ICP, personas and value props, a chain of specialists, CRM sync and a shared workspace. The model isn't the edge; the context and orchestration around it are.
Is our data safe?
Eva works from your own data and permissions, not the open internet, with a full audit trail. Evergrowth is GDPR-compliant, SOC 2 and ISO 27001, and never trains on your data.
Will it replace our reps?
No, it removes the grunt work (research, list-building, drafting) so reps spend their time selling. Reps stay in control and review every output; a smaller team does far more.
Do we need engineers to run it?
No. RevOps configures the Agent Training Center once; reps consume the outputs. No code, no GTM-engineer bottleneck.
How fast do we see value?
Most teams see qualified accounts and ready contacts in the CRM within 1-3 weeks; reps run AI-augmented plays by week 4.
Does it work with our CRM?
Two-way sync with HubSpot, Salesforce and Pipedrive; it works alongside your sequencer and replaces siloed data and research tools.
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Before you decide

How agentic is your GTM team?

Score your team across the 6 dimensions of an agentic GTM team in five minutes, and see exactly where Evergrowth would help.

Take the 5-min assessment

See it on your own accounts

Bring five target accounts and we'll show you the research, contacts and plays Evergrowth generates for them.

Request a demo