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

Your competitors buy from the same vendors, scrape the same sources, and run the same enrichment. Data is commoditized. The only advantage left is what you do with it.

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

Context isn't a feature. It's 10 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.

Domain finder
Domain found — Domain Finder locates and verifies the company website so every other agent has something to browse.
ICP fit validated — Account Qualification reads the site and scores it against your verticals, not a static industry filter.
Account signals scored — Account Research surfaces hiring patterns, tech stack, funding, leadership changes — real buying signals.
Strategic plan generated — Account Planning builds a POV: why this account, what angles, which stakeholders, what timing.
Persona-fit contacts found — Contact Finder matches people by responsibilities and seniority, not job title keywords.
Employment verified + DISC profiled — Contact Qualification confirms they're still there and builds a communication style profile.
LinkedIn activity analyzed — Contact Research scans recent posts, shares, and engagement to find conversation starters.
Email & phone verified — Waterfall agents cascade through 20+ vendors until a verified match is found.
Personalized outreach written — Play Copywriting generates emails, LinkedIn messages, and call scripts calibrated to everything above.
Strategy refined — Digital Twin simulates objections and adjusts messaging using your trained value props and DISC insights.

That's 10 layers of research before a rep ever sees the contact. That's context.

From systems of record to systems of context

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

Data-driven GTM

20+ tools, no context

Data variables in templates — "Hi {first_name}"
GTM engineer as bottleneck — AI locked in one person's spreadsheet
Spray and pray at scale — more volume, same message
78% of reps missed quota in 2025
Prospects feel scraped, not understood
Context-driven GTM

One workspace, deep context

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

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
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 (90% attainment)
$0
Cost per $1 of revenue

Calculate your ROI