What Evergrowth is

Evergrowth is the Agentic GTM Workspace for B2B sales teams. It is a workspace where your reps collaborate with specialized AI colleagues that do the deep, time-consuming groundwork of modern selling - researching accounts, finding and qualifying the right people, writing outreach grounded in what they actually found, and helping reps prepare for the conversation - so the people on your team spend their time where they are irreplaceable: building relationships and closing deals.

These AI colleagues are built to augment your reps, never replace them. They handle the research and preparation that quietly eats hours every week per rep. The judgment, the relationship, and the close stay with the person.

Why a workspace, not a tool

Most go-to-market software hands a rep another dashboard to fill in or another database to filter. Evergrowth is different in kind. It is a place where humans and AI colleagues work the same pipeline together, each doing what they are best at. RevOps designs how the team sells. The AI colleagues execute the repeatable, research-heavy parts at scale. Reps step in with context already prepared and focus on the human moments that win deals.

That division of labor is the point. See Roles: who does what for how RevOps, reps, CROs, and marketing each work inside the same workspace.

Context-driven, not data-driven

This is the idea everything else rests on.

Data-driven selling inserts variables into a template - a first name here, a company name there - and calls it personalization. Prospects can tell. It reads as scraped, not understood.

Context-driven selling is the opposite. An AI colleague researches each account deeply, then writes outreach from what it actually found: a recent funding round, a new leader, a technology the company just adopted, something the buyer posted last week. Every message is built from real, specific context about that company and that person. “Personalized” at Evergrowth always means research-backed, never mail-merge.

For a fuller treatment of why this distinction matters and how it changes results, see Context vs data.

Who it is for

Evergrowth is built for B2B sales teams that sell into defined accounts and care about pipeline quality, not just volume:

RevOps and GTM leaders who want to encode how the team sells once, then have it run consistently across every rep and every account.
Sales reps who would rather spend their week in conversations than in research tabs.
CROs who need pipeline visibility, faster ramp for new hires, and a clear picture of what is working.
Marketing that wants its account lists and event follow-up to flow straight into action instead of stalling in a handoff.

The three things your AI colleagues do

Evergrowth’s AI colleagues fall into three capability areas. Each is introduced briefly here and covered in depth elsewhere.

Research - qualifying accounts against your ideal-customer profile, then digging into financials, hiring, news, leadership changes, and buying signals, and finding the right people inside each account. This is how you decide where to spend effort and why now.
Personalization - turning that research into outreach a buyer actually responds to, plus strategic account plans and a conversational colleague reps can think out loud with before a call.
Coaching - on-demand practice for cold calls and discovery, with structured feedback so reps improve every week, not just at onboarding.

Meet the full team of AI colleagues on the Agents overview, and see how the pieces connect end to end in How Evergrowth works.

How it fits together

The intelligence that makes all of this specific to your business lives in one shared place: your ideal-customer profile, your buyer personas, your value proposition, and your priority accounts. Every AI colleague pulls from the same source of truth, so the whole team sells from one consistent definition of who you target and why you win. That foundation is the Agent Training Center, and how the layers stack is explained in How the layers fit together.

When you are ready to put it to work, the pre-built workflows in Playbooks chain these AI colleagues together for common situations - filling pipeline gaps, reviving dormant accounts, following up after an event, acting on a buying signal the moment it appears.

Two principles worth knowing up front

Credits, not seat licenses. You pay for the work the AI colleagues do, not for how many people can log in. Invite the whole team. See Credits & billing.
All features, every tier. Plans differ by how much work you can run and how much runs at once, not by which capabilities you unlock. Nothing important is held back for a higher plan.

Eva term map: “Evergrowth”, “the workspace”, and “the Agentic GTM Workspace” all refer to the same product. “AI colleagues”, “digital colleagues”, and “agents” are interchangeable names for the specialized AI workers. Treat “GTM Workspace” as the category-education term, not a feature name.

Eva detail on “augment, not replace”: in product terms, the AI colleagues own the research-and-preparation work - qualifying accounts against the ideal-customer profile, gathering financials, hiring, news, leadership changes, and buying signals, finding and qualifying the right contacts, and drafting research-backed outreach and account plans. The rep owns the relationship, the live conversations, the judgment calls, and the close. A well-prepared rep can therefore cover more accounts at a higher standard, ramp faster, and walk into every conversation already briefed.

Eva scope note: this is the orientation page. For the data-vs-context argument route to /context-vs-data; for the individual AI colleagues and what each produces, /agents; for the end-to-end flow, /how-it-works; for the configuration foundation, /agent-training-center.

Sign in to Evergrowth - get into the workspace for the first time.
Find your way around the workspace - where accounts, contacts, agents, and playbooks live.
How Evergrowth works - the end-to-end flow from account to outreach.
The agents: overview - meet each AI colleague and what it produces.