In the GenAI era, the winners won’t just build AI—they’ll master it. Competitive advantage lies in how fast you can experiment, optimize, and scale intelligence across the enterprise. But the ability to compare, optimize, and deploy AI model combinations with traceability, speed, and confidence remains elusive for most enterprises.
That’s why a global leader in human capital management partnered with Impetus to build the RAG AI Playground—a next-generation platform to accelerate AI-driven decision-making at scale.
The AI adoption gap: Complex choices, high stakes
CXOs and technical leaders alike are confronting these common pain points:
- Difficulty selecting the right LLMs, embedding models, and vector databases
- Lack of end-to-end traceability for AI decisions
- High costs and unpredictable latency across models
- Fragmented workflows that slow down innovation
The result? Suboptimal AI outcomes and missed business opportunities.
The RAG AI Playground solves this with a unified, enterprise-grade testbed that delivers experimentation + evaluation + observability – all in one platform.
Reimagining AI evaluation at scale for a global payroll leader
When a global payroll giant set out to enhance the accuracy and efficiency of its AI-powered payroll and workforce automation systems, it needed more than just another model. It needed a smarter way to evaluate and evolve its entire AI stack—at scale.
The challenge: Building an experimentation platform to systematically test LLMs, embeddings, and vector databases—while ensuring traceability, cost efficiency, and seamless MLOps integration.
What we delivered
- A user-friendly AI experimentation playground built with Streamlit, enabling low-code/no-code interaction with AI components
- Integrated Mosaic AI Agent Evaluation and MLflow tracking for real-time performance insights, cost governance, and debugging
- Side-by-side AI response comparisons to evaluate quality, latency, and cost trade-offs across models
- Seamless scalability and integration with enterprise infrastructure, including Databricks Vector Search and existing MLOps pipelines
RAG architecture model
Key features of the model
| Capability | Benefit for tech + business leaders |
|---|---|
| Modular testing environment | Plug-and-play LLMs, embeddings, and vector DBs |
| Side-by-side response comparison | Transparent performance benchmarking |
| Mosaic AI agent evaluation | Objective metrics: quality, latency, cost |
| MLflow integration | Full experiment lineage for auditability and governance |
| Streamlit frontend | Low-code/no-code experimentation for cross-functional teams |
| Databricks vector search integration | Seamless MLOps compatibility and scaling |
Business impact: Smarter AI, delivered faster
By adopting the RAG AI Playground, the enterprise achieved:
- Reduced costs and latency, balancing AI performance with operational efficiency
- Accelerated debugging and issue resolution, minimizing disruptions in AI workflows
- Enhanced collaboration, enabling AI teams to document, compare, and refine models efficiently
- Increased scalability and enterprise readiness, supporting AI experimentation from small test cases to full-scale deployments
- Improved AI-driven decision-making, ensuring better accuracy and cost-effectiveness in payroll automation
Why it matters for enterprise AI strategy
For CTOs and CIOs, the RAG AI Playground offers a blueprint for scalable AI governance—ensuring decisions are explainable, auditable, and business-aligned.
For engineering and AI leads, it’s a launchpad for rapid experimentation—with deep visibility into cost-performance trade-offs and model behavior.
Without structured evaluation and real-time feedback loops, even the most powerful AI models can underperform or misfire in production.
Dr. Ravishankar Rao Vallabhajosyula , Head of Data Science, Impetus
As GenAI continues to transform how enterprises operate, the winners will be those who can experiment fast, evaluate rigorously, and deploy with confidence. Whether you’re building intelligent copilots, domain-specific chatbots, or automation-first workflows, success depends on how well your teams can fine-tune models in context—securely, repeatedly, and at scale.
The RAG AI Playground isn’t just a tool. It’s your control tower for AI innovation, bringing structure to chaos, speed to experimentation, and trust to outcomes.
Ready to bring structure to AI chaos?
Join leading enterprises in operationalizing GenAI—where every experiment is measured, every model is optimized, and every deployment creates business impact.
Let’s redefine what’s possible with enterprise-grade AI.
Authors
Atharv Sakalley – Senior Analytics Engineer, Data Science
Rushikesh Kusare – Senior Analytics Engineer, Data Science
Rohit Agrawal – Analytics Engineer, Data Science