Scaling Enterprise GenAI with AgentOps on Databricks
Learn how Aimpoint Digital helped a global CX platform provider streamline their AI operations by leveraging our AgentOps Brickbuilder on Databricks to standardize workflows, accelerate experimentation, and enable scalable deployment. Our team delivered a solution that enables seamless model switching, automated prompt optimization, and consistent, robust multi-region model performance and lifecycle management.
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The Challenge
As a leading cloud contact center platform provider, the company is focused on delivering AI-powered capabilities that enhance customer interactions and agent performance at scale. However, scaling these capabilities introduced significant technical and operational complexity.
Switching between LLM providers created inconsistencies in application behavior and required manual, time-intensive prompt engineering. Evaluation workflows were fragmented, limiting the ability to systematically compare models and optimize performance.
At the same time, deploying fine-tuned models across multiple regions introduced challenges around governance, reproducibility, and consistency. Without a standardized framework, experimentation cycles were slow, deployment processes were manual, and achieving production-grade reliability at scale was not feasible.
Our Approach
Aimpoint Digital implemented our composable, modular AgentOps solution built on Databricks, embedding best practices across the full lifecycle of GenAI applications — from experimentation to deployment.
At the core of the solution was a unified LLM interface that abstracted multiple providers, including Databricks Mosaic AI, OpenAI, and custom models. This enabled seamless model switching without requiring changes to application logic, reducing complexity and eliminating vendor lock-in.
To improve prompt performance and evaluation, we leveraged DSPy to structure prompting workflows and enable automated prompt optimization using LLM-as-a-judge methodologies. MLflow provided centralized experiment tracking, observability, and reproducibility, allowing teams to run structured, side-by-side evaluations.
Finally, we implemented robust CI/CD pipelines using GitLab and Declarative Automation Bundles to standardize deployment and governance. A multi-region architecture powered by Delta Sharing enabled centralized model development with consistent, low-latency regional deployment.
Results
Evaluation cycles were reduced from weeks to hours through automated scoring, structured experimentation, and centralized tracking in MLflow, dramatically accelerating model selection and optimization.
A unified LLM interface decoupled application logic from model providers, enabling seamless switching without code changes and reducing integration effort while avoiding vendor lock-in.
Standardized CI/CD pipelines and Delta Sharing enabled deployment across five global regions, replacing manual processes with a governed, repeatable framework for production-grade AI systems.
Key Takeaways
Aimpoint Digital implemented a composable, modular GenAI architecture that enabled scalable AI adoption by introducing flexibility across models, prompting, evaluation, and deployment—reducing vendor lock-in and accelerating innovation. Automated prompt optimization and evaluation transformed a manual, time-intensive process into a fast, data-driven capability, improving model performance and experimentation speed. Combined with robust AgentOps practices such as versioning and CI/CD, this established a reliable, cost-efficient foundation for deploying and scaling enterprise-grade AI systems globally.
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