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In our previous engineering update, we discussed how we cleaned house on our build systems by moving to Bazel and Distroless images to ensure that every line of code we create is secure, reproducible, and free of software clutter.
But building secure software is only half the battle. You also have to deliver it.
Once that secure code leaves our developers’ hands, how does it get to the servers that power your customer support? And how do we ensure those servers stay healthy, secure, and compliant 24/7?
While our last update focused on the Build, this update focuses on the Run. We have re-architected our continuous deployment (CD) system, moving from a traditional manual model to a modern GitOps architecture using Argo CD.
Here is why that matters for the enterprise.
From pushing updates to watching security
In traditional deployment models, engineers manually push updates to servers. It works, but it leaves room for human error and configuration drift (where a server’s settings slowly wander away from the ideal state over time).
With this rearchitecture, we’ve inverted this model.
Instead of pushing, our new system constantly watches our code repository, the single source of truth. The system compares what should be running against what actually is running in our data centers.
Why this matters: The self-healing infrastructure
If a server configuration changes unexpectedly (a noisy neighbor issue or a manual error), our system detects the anomaly immediately and automatically reverts it to the secure, approved state. Which means…
- Consistency: The environment we test is the environment you get.
- Stability: This constant feedback loop eliminates the “unknown errors” that cause downtime.
Faster patches, zero friction
In our previous post, we mentioned how Distroless images allow us to rebuild our software in minutes to patch vulnerabilities. Our new deployment architecture takes this speed a step further by separating Application Code from Configuration.
Before, changing a simple setting (like increasing memory for a high-traffic event) required rebuilding and redeploying the whole package. Now, configuration is treated separately.
Why this matters: Instant response
- Performance Tuning: We can instantly adjust resources (CPU/Memory) to meet demand without the risk of a full code deployment.
- Critical Security: If a vulnerability is found in an open-source library, we can update the configuration to block it or patch it across our entire global fleet in moments, not days.
Global Scale and data residency
As we scale with global banks and healthcare providers, Data Residency has gone from nice-to-have to strict legal requirement. You need to know that your data is processing in (the US/the UK/Canada/the EU) and nowhere else.
Because our new architecture relies on a monorepo (a single, unified library of infrastructure code), we have standardized how we build clusters. Instead of being a labor-intensive manual construction project, spinning up a new environment is now a copy-paste of a perfected blueprint.
Why this matters: Rapid compliance
We can say yes to your geographic data requirements faster. We can deploy secure, compliant, local clusters to support your expansion without reinventing the wheel.
The invisible, invincible upgrade
Infrastructure upgrades like this rarely make headlines because when they work, they are invisible. You don’t see the deployment that didn’t fail. You don’t notice the security vulnerability that was patched before it could be exploited.
But for the enterprise buyer, this invisibility is the goal.
By moving to this GitOps architecture, we are ensuring that the PolyAI platform is smart enough to handle your customer conversations and robust enough to protect them.
Ready to elevate your customer experience? Speak to our team to see how PolyAI’s self-healing, enterprise-grade platform powers the future of AI-driven customer service.