Data vs Context

Everyone has the same data.
Context is what separates you.

Your competitors buy from the same vendors, so the only advantage left is what you do with the data.

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Same account. Same contact. Different outcome.
Data-driven GTM

More tools, no context

Hi {first name}, {data variable}{data variable}

Spray and pray at scale: more volume, same message

Prospects feel scraped, not understood

Evergrowth

1 workspace, deep context

  1. Domain foundWebsite Finder
  2. ICP fit validatedAccount Qualification
  3. Account signals scoredAccount Research
  4. Strategic plan generatedAccount Planning

Prospects feel understood: peer-to-peer conversations

See the difference

Same account. Same contact. Different outcome.

ACME, INC
ICP: Yes Score: 65
LinkedIn data
# of employees
Industry
Country
Hiring signals
Head count growth
b BuiltWith data
Tech fingerprints
cb Crunchbase data
Funding data
Account qualification
Account research
John Doe
IBP: Yes
LinkedIn data
Job title
Engagement data
Contact qualification
Contact research
DISC profile
LinkedIn insights
Data-driven outreach
Play copywriting

Hi {first name},

_____________ {data variable}_______ ___________.

_____ {data variable}.

_ {data variable}_______?

+ AI?
FAIL
What makes context

Context isn't a feature. It's a chain of specialist agents building on each other.

You can't add "context" to a template engine. It requires deep research at every stage, each agent adding a layer that the next one builds on. Eva is who chains them, grounded in your training center and the research her agents bring back.

Website finder
Domain found: Website Finder locates and verifies the company website so every other agent has something to browse.
ICP fit validated: Account Qualification reads the site and returns a clear fit verdict against your verticals, not a static industry filter.
Account signals scored: Account Research surfaces real buying signals, from hiring patterns and tech stack to funding and leadership changes.
Strategic plan generated: Account Planning builds a POV: why this account, what angles, which stakeholders, what timing.
Persona-fit contacts found: Contact Finder searches by the persona's job titles, then keeps only people whose responsibilities and seniority match the persona card.
Employment verified: Contact Qualification confirms they still fit the persona and still work there.
LinkedIn profile read: Contact Research reads the person's LinkedIn profile and the open web, and every persona-fit contact gets a DISC communication-style profile.
Email & phone found: the Email/Phone Finder cascades through data providers covering 30+ sources until it finds a verified email and a phone number.
Personalized outreach written: Play Copywriting generates emails, LinkedIn messages, and call scripts calibrated to everything above.
Sequence chained by Eva, your AI colleague. You ask her in plain language, she runs the agents in order, grounds every layer in your intelligence, and shows her work.

That's layer on layer of research, chained by Eva, before a rep ever sees the contact. That's context, and you get it by asking Eva.

The shift

From systems of record to systems of context

Your CRM is a data warehouse. Your reps need actionable context.

Data-driven GTM

More tools, no context

Data variables in templates: "Hi {first_name}"
GTM engineer as bottleneck: AI locked in 1 person's spreadsheet
Spray and pray at scale: more volume, same message
78.3% of sellers missed quota in 2025 (Fullcast 2026 GTM Benchmarks)
Prospects feel scraped, not understood
→
Context-driven GTM

1 workspace, deep context

Everyone talks to Eva in plain language, she runs the research chain and shows her work
Deep research in every outreach: account, persona, signals, DISC
RevOps + reps share digital colleagues, no bottleneck
Quality over volume: signal-based, not spray-based
Reps armed with playbook-aligned coaching and roleplay
Prospects feel understood: peer-to-peer conversations
The business case

Data-driven teams spend more to close less

AI-augmented GTM teams generate more revenue with fewer people, fewer tools, and dramatically lower cost per dollar of revenue.

Data-driven model
Team8 SDRs + 8 AEs
Tools cost$72K/yr
Total GTM cost$1.85M/yr
Quota / AE$900K
Achieved$2.2M (31% attainment)
$0.84
Cost per $1 of revenue
Context-driven model
Team2 SDRs + 4 AEs + ∞ agents
Tools cost$48K/yr
Total GTM cost$878K/yr
Quota / AE$990K
Achieved$3.5M (88% attainment)
$0.25
Cost per $1 of revenue

An illustrative model on Evergrowth's assumptions, not a measured result.

Calculate your ROI

Ready to move from data to context?

See how Eva and 12 types of specialized AI agents turn raw data into deep research, qualified leads, and outreach that feels like a peer-to-peer conversation.

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