Why Steadybase?

The GTM Execution Gap

Every GTM team runs the same playbook: research accounts, score leads, prep for calls, generate content, monitor health, forecast revenue. The problem isn't strategy — it's consistent execution at scale.

:::note The average AE spends only 28% of their time actually selling. The rest goes to admin work that an AI agent could handle. :::

What Breaks Today

1. Stateless AI tools forget everything. ChatGPT can draft an email, but it doesn't remember your last 50 interactions with that account, your team's messaging framework, or the deal context from last quarter.

2. Point solutions create silos. Call scoring in one tool, CRM in another, content generation in a third. No shared memory, no coordinated execution.

3. Workflow automation is brittle. Zapier-style automations break silently. There's no retry logic, no state management, no human-approval gates.

4. Single-model lock-in limits capability. Different tasks need different models. Research needs Claude's analytical depth. Content generation benefits from GPT-4o's creativity. Quick classification works best with Gemini's speed.

How Steadybase is Different

Durable Execution (Temporal Cloud)

Every Steadybase workflow runs on Temporal Cloud. This means:

• Automatic retries — if an LLM call fails, it retries with exponential backoff
• State persistence — a workflow can pause for human approval and resume days later
• Crash recovery — if the server restarts mid-workflow, it picks up exactly where it left off
• Visibility — every workflow execution is fully observable with step-by-step history

Hierarchical Memory

Steadybase workers don't start from scratch every conversation. They maintain durable memory across four scopes:

Organization Memory
  └── Team Memory
       └── Worker Memory
            └── Session Memory

An Account Executive worker remembers your org's ICP, the team's messaging framework, its own past research, and the current conversation context — all layered together.

Multi-Agent Coordination

The Drew Coordinator workflow orchestrates multiple specialized agents in a single request:

Drew plans the request Breaks a natural language request into subtasks using Claude

Lisa researches Queries Gong transcripts, cross-references Salesforce, enriches contacts

Brian creates content Drafts personalized outreach using GPT-4o with research context

Human approves Signal-based gate pauses execution until an AE approves the outreach

Marketing validates Nexus cross-namespace call checks ABM program alignment

Transparency-First Security

We don't claim to be SOC 2 certified (yet). Instead, we publish exactly what's implemented, what's in progress, and what's planned. See our Security Overview for the full posture.