1:1 Agentic Decisioning
Move beyond segments and journeys with a dedicated AI agent for each individual user – learning their preferences, reasoning about what moves them, and personalizing every interaction in real time.
Outcomes From Agentic Decisioning
128%
Improvement in engagement
25%
Increase in incremental purchases
135%
Increase in GMV after onboarding
<8 hrs
To complete integration & testing
Your Users Are Individuals, Your
Campaigns Treat Them As Audiences
THE OLD WAY
Knowledge Resets Every Campaign
Your marketing tools optimize sending, not learning. Everything they figure out about a user disappears when the campaign ends – so the next one starts from scratch.
THE NEW WAY
Learning Compounds for Each User
Every user has unique goals. A dedicated agent persists per user, sharpening its understanding about that person with every interaction – instead of starting from zero.
An Agent for Every User
Each agent learns from individual user behaviour and preferences to fine-tune the
copy, creative, timing and channel of the next message.
What Makes Agentic Decisioning Different?
1:1 Reinforcement
Each agent uses Thompson Sampling and multi-armed bandits to continuously optimize content, timing, frequency, and channel – all at once for a single user.
Causal Learning
Agents distinguish "this happened because I sent a message" from "this happened after I sent a message," so they learn what actually drives action.
Semantic Learning
Agents learn at the level of meanings – tones, themes, value framings – not just specific messages. New campaigns inherit everything the agents have already learned, so nothing cold-starts.
Network Intelligence
Agents share learnings across the platform while staying autonomous per user, which keeps even brand-new users from starting cold.
