Production AI for CX: The Three Architectural Requirements
On-Demand
In this 10-minute session, Replicant’s Head of Solutions Engineering, Koen Van Duyse, breaks down how enterprise teams are building AI systems that resolve real customer conversations without sacrificing control, compliance, or consistency. Includes short, real-world demos that show how different AI approaches perform under real conditions.
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In this session, you’ll get both the architectural framework and a look at how these systems behave in practice through short, side-by-side demos.
You’ll learn:
- Why prompt-based systems break in production
And why “happy path” automation doesn’t survive real conversations - How to use enterprise conversation data as your foundation
Transform real interactions into scalable, high-resolution AI - What governance actually requires
How deterministic enforcement ensures compliance, consistency, and control
Why this matters:
Most AI initiatives don’t fail because of vision, they fail because they can’t scale.
- Prompts don’t enforce behavior
- Manual workflows don’t scale beyond a few agents
- Governance gaps introduce risk as automation expands
The result: stalled pilots, rising costs, and lost confidence.
Replicant was built to change that, enabling teams to move from experimentation to production AI that resolves more, scales faster, and gets better with every interaction.
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