A propensity model is a machine learning model that predicts the likelihood of a specific behavior — purchasing, churning, upgrading — based on historical data patterns. More sophisticated than simple lead scoring, propensity models find non-obvious correlations (companies that have a specific tech stack, hired recently, and visited your site 3+ times are 8x more likely to close). They're increasingly accessible through tools like MadKudu and 6sense.
For example, a propensity model trained on your closed-won deals might identify that companies using Salesforce + Gong with a >100 employee sales team and recent SDR job postings have 40% higher win rates — letting you prioritize those accounts over generic ICP fits.
For clients with sufficient historical deal data, we build propensity models that dramatically improve outbound prioritization — focusing human SDR time on accounts most likely to close.
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Agentic AI refers to AI systems that can plan, take actions, use tools, and complete multi-step tasks autonomously — going beyond generating text to actually doing work.
AI Agent
An AI agent is an LLM-powered system that can autonomously use tools, access data, and complete tasks — as opposed to a simple chatbot that only responds to single prompts.
Autonomous Workflow
An autonomous workflow is a multi-step automated process that runs without human intervention — trigger, conditions, actions, branches, and loops all executing on schedule or in response to events.
Human-in-the-Loop (HITL)
Human-in-the-loop describes AI automation workflows that include a human review or approval step before consequential actions are taken — particularly sending outreach, making calls, or publishing content.
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An LLM is the AI model underlying most modern AI tools — GPT-4, Claude, Gemini, Llama.
Prompt Engineering
Prompt engineering is the practice of designing inputs to AI models to get better, more consistent outputs.