Perviewsis brings clarity with LLM-augmented cross-system observability.
Modern cloud architectures are heterogeneous – an application might span multiple clouds (AWS, GCP, etc.), using different stacks that produce logs in varied formats. SREs struggle to correlate events across these disparate systems. This feature uses LLMs to interpret and unify unstructured log data from anywhere, enabling semantic correlation of events that traditional keyword or rule-based systems would miss. Essentially, an LLM acts as a super-powered log analyst: it can read messy logs, understand their meaning, and link related events by context even if they come from different sources with no common schema.
Instead of isolated dashboards and cryptic alerts, Perviewsis uses large language models to synthesize telemetry across systems, surfacing human-readable narratives that explain what went wrong, what systems were involved, and what’s likely to happen next. Whether you’re dealing with a cascading failure or a subtle data drift, the platform connects the dots for you—across services, environments, and time.
Apache access logs, Kubernetes cluster events, application exception traces, cloud provider audit logs – each has its own structure. Traditional log analytics requires writing parsers for each format. An LLM can read raw text and infer structure and semantics on the fly (e.g. it can recognize timestamps, error codes, user IDs in the text without an explicit parser).
Correlating logs usually relies on common fields (like a trace ID or IP address). But cross-system issues may not have a shared ID. For instance, a front-end error log might say “timeout calling Order Service,” and a backend log says “DB connection timeout” – a human can guess these are related (the DB caused the frontend timeout), but automated systems can’t unless explicitly programmed. LLMs, with their language understanding, can connect such dots by semantic similarity and reasoning.
Perviewsis continuously adapts to your system’s evolving architecture and terminology, using custom embeddings and domain-specific tuning to provide increasingly relevant interpretations over time.
All logs from various sources feed into a central pipeline (could be an observability pipeline with Fluentd/ FluentBit, Logstash, etc.). The pipeline might do initial lightweight parsing (like extracting timestamps or severities) but doesn’t fully normalize everything (because that’s hard for unknown formats).
As logs arrive, the system generates vector embeddings for each log message (or for batched messages). These embeddings capture the semantic meaning of the text. For example, two error messages with different wording but both about a database timeout would end up with similar embeddings. The platform can maintain a vector index(a vector database) of recent log embeddings for fast similarity search. This allows quick retrieval of “logs that are similar to this one.”
Organizations today run on a tangled mesh of microservices, APIs, cloud-native infrastructure, third-party SaaS integrations, and real-time data systems. Observability platforms gather the signals—traces, metrics, logs—but:
Data is siloed by source or format
Alerts are noisy and non-contextual
RCA (Root Cause Analysis) is time-consuming
Domain knowledge is often tribal and undocumented
These limitations slow down response times, increase MTTR (Mean Time to Recovery), and create risk.
An LLM (or a combination of smaller specialized models) processes log streams. There are a few modes this can work:
The LLM reads logs as they come in and classifies or annotates them. For example, it could assign each log a label like “timeout-error” or “authentication-failure” based on its content. It essentially creates a structured event out of the unstructured log by understanding it. This is akin to log parsing, but using an AI brain rather than regex. Recent research (like the HELP log parser) shows this is feasible by clustering and then using LLMS. The hierarchical embedding approach clusters similar logs first (to reduce cost) and then uses the LLM to generate a template or structured form for the cluster. This helps handle log format changes (log drift) because the model can adapt to ne patterns without explicit reconfiguration
The system uses the vector index to find related events. For instance, when an incident is detected, the platform might take a representative error log and do a similarity search in the vector DB to find other logs (perhaps from other services or clouds) that are semantically related. If a spike of similar “timeout” errors appears across several services around the same timestamp, the platform groups them into one incident
The LLM can be prompted with a set of log entries (from different sources) and asked to find the relationship. For example: “Given these logs from Service A and Service B, do they describe a related failure? Explain.” The LLM might output: “Yes, Service A timed out waiting for Service B, and Service B’s log shows an out-of-memory error – likely causing the timeout.” This goes beyond simple text matching; the LLM actually infers causality from the content
Perviewsis models are augmented with:
The ultimate output is a higherlevel incident or correlated event that ties together the raw logs. The platform might generate an alert or incident report saying, “Multisystem issue detected: Service A timeout errors (AWS) and Database connection errors (GCP) are linked – likely the database outage caused cascade of timeouts.” This is surfaced to the SRE with all the supporting logs attached. LLM-augmented cross-system log analysis pipeline. Logs from diverse sources (different clouds, formats) flow into a unified ingestion pipeline. The system generates semantic embeddings for logs and stores them in a Vector Index, enabling similarity searches across all logs. An LLM Correlation Engine pulls in normalized log data (via the pipeline) and uses the vector store to find related log patterns. The LLM can interpret log messages (extracting their meaning) and cross-link events that share semantic context. The outcome is correlated incident insights that combine multi-source events into a single narrative or alert. This pipeline allows detection of issues that manifest across different systems (e.g. an app error on Cloud A caused by a database failure on Cloud B) which would be hard to catch with siloed log analysis.
Unlike traditional rule-based correlation, which might require explicit “if error X in service Y and error Z in service Q within 5 minutes, then link them,” the LLM approach is flexible. It can handle incidents with no exact signature match by relying on meaning. For example, if one log says “payment timeout” and another says “Stripe API not responding,” an LLM can recognize these describe the same issue (a payment provider outage), even though they don’t share a keyword. This dramatically improves the observability of complex distributed incidents.
All analysis and interpretations respect your data governance policies. LLMs operate in secure, configurable environments tailored to your compliance requirements.
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