Akila Balasubramanian

Semantic Condensation of High-Cardinality Time Series for LLM-Driven Observability

Abstract:

Modern cloud observability platforms generate high-cardinality time series data that is inherently difficult to interpret at scale and poorly aligned with the reasoning constraints of large language models (LLMs). Raw telemetry, often comprising thousands of time series (TSIDs) with dense temporal sampling, leads to token inefficiencies, obscured signals, and contradictory narratives when consumed directly by LLM-based assistants.

This paper introduces Semantic Condensation, a token-aware transformation layer that converts large-scale time series data into structured, semantically consistent digests optimized for LLM-driven troubleshooting. The approach combines vectorized statistical pre-analysis, multi-signal importance scoring, adaptive token-budget-aware tiering, and behavior-aware trend classification to preserve critical operational signals such as anomalies and change points while minimizing representation cost.

Unlike traditional downsampling and aggregation techniques, Semantic Condensation explicitly optimizes for LLM interpretability and narrative coherence, ensuring that derived summaries remain internally consistent. Experimental evaluation demonstrates that the approach scales to 10,000+ time series within strict latency constraints, reduces token footprint by orders of magnitude, and significantly improves downstream LLM reasoning accuracy. This work establishes a new abstraction layer for observability systems: LLM-aligned semantic representations of telemetry data.

Profile:

Akila Balasubramanian is an independent researcher and technical leader specializing in AI-powered Observability and Intelligent Troubleshooting Platforms. Her work focuses on building intelligent systems that help engineering teams detect, investigate, and resolve production issues with greater speed, confidence, and reliability.

Her field of expertise sits at the intersection of distributed systems, observability, applied artificial intelligence, and enterprise software engineering. She has led the design and delivery of platform capabilities spanning AI-directed troubleshooting, automated root cause analysis, real user monitoring for web and mobile applications, session replay, custom telemetry indexing, and full-stack investigation workflows. A consistent theme across her work is transforming complex telemetry into actionable, evidence-backed insights that reduce operational toil and improve decision-making during incidents.

Akila is particularly focused on the responsible application of AI in production operations, including explainability, guardrails, workflow design, and trustworthy reasoning. She is motivated by building durable platform capabilities that scale across teams, customers, and use cases, while balancing technical rigor, usability, reliability, and long-term strategic value.