Instant Insight: Real-Time RAG and Pinot Power Smarter Workforce Decisions

Session title: Real-Time RAG with Apache Pinot for People Analytics

With three-quarters of employers struggling to find the talent they need, enterprises with distributed workforces spanning hundreds or thousands of locations, shifts, and roles need a clear and current picture of workforce dynamics. Having current information is essential to maximizing retention and quickly filling open positions.

While traditional people analytics systems effectively report historical trends in areas like retention, engagement, and diversity, few can deliver real-time insight. Yesterday’s information is of limited value in an environment like retail, where turnover averages 60%.

“Leaders want decisions to be made at the speed of business,” said Bharath Kumar Varma Sagi, Senior Data Scientist at Starbucks, in a presentation at the Real-Time Analytics Summit hosted by StarTree.

A combination of new technologies – retrieval-augmented generation (RAG), agentic artificial intelligence (AI), and Apache Pinot – offers a potential step-change in capability.

On-the-spot insights

Modern HR leaders increasingly need answers to time-sensitive, high-impact questions like:

• Which teams are at risk of disengagement right now?
• What will staffing needs look like tomorrow based on current scheduling trends?
• How can we optimize recruiting strategies based on live feedback from candidate interviews?
• Which employees are we at the highest risk of losing?

Answering these questions requires systems that are fast, flexible, and context-aware.

Retrieval-augmented generation offers a solution by enriching large language models (LLMs) with access to organizational data. RAG combines a language model with a search component. It retrieves relevant information from external data like documents or databases and uses it to generate more accurate, up-to-date, and context-aware responses.

Sagi said the value of RAG becomes even greater when paired with streaming data. It allows employers to monitor employee interactions, performance data, scheduling events, and even unstructured signals like sentiment from surveys and social media for immediate action.

Apache Pinot is a valuable tool for real-time RAG, Sagi said. Designed for ultra-low-latency analytics at scale, Pinot supports high-concurrency, high-volume querying with sub-second responsiveness. In a real-time RAG architecture, it can serve as both the vector store and the analytical query engine, ingesting batch and streaming data, transforming it into vector embeddings, and supporting fast retrieval when the LLM needs relevant context to answer a question.

Sagi described a typical people analytics use case: A manager wants to know which departments have seen a drop in engagement over the past week. With traditional systems, this might require waiting for daily extract/transform/load pipelines to run, data marts to refresh, and dashboards to update. In a real-time RAG system based on Pinot, the query can be answered on the spot, using data received over the past few minutes.

From there, the organization can leverage agentic AI, which uses tools and takes actions, to suggest next steps, such as initiating a survey or investigating shift scheduling problems.

Gen AI advantage

Generative AI makes these insights available far more broadly than was possible in the past, Sagi said. HR leaders, executives, and team managers can interact through natural language interfaces, asking questions without writing SQL or navigating dashboards. AI handles the complexity of querying Pinot, analyzing results, and delivering narrative responses grounded in the organization’s most current data.

Implementing such systems isn’t trivial, Sagi cautioned. Real-time ingestion pipelines like Apache Kafka must be built to furnish fresh data. Pinot clusters may need to be horizontally scaled to handle many concurrent users. Retrieval models must be tuned to ensure relevance and avoid hallucination, a known risk with LLMs. A robust deployment requires governance guardrails, prompt tuning, and frequent monitoring.

However, the results can be transformative. Instead of static insights, organizations gain the foresight needed to make HR less reactive and more strategic. Leaders can better allocate resources, respond to emerging issues, and create more responsive, equitable workplaces.

“We see a shift from retrieval-based intelligence to context-driven real-time reasoning,” Sagi said.

Whether it’s identifying signs of burnout from real-time interaction logs or customizing interview questions based on live candidate feedback, real-time RAG with Pinot is laying the foundation for a more agile, AI-powered approach to managing human capital.