Analytics Overview

Observo AI is engineered to help DevOps and Security teams tackle their most pressing telemetry data challenges using advanced machine learning and deep learning techniques. By transforming traditional, static, rule-based pipelines into dynamic, self-learning systems, Observo AI dramatically reduces the time and cost associated with incident detection and resolution.

Purpose

Data Optimization & Noise Reduction: Observo AI employs intelligent algorithms that optimize data streams, reducing noise by up to 80% and cutting data insight costs by 50% or more. Its machine learning models continuously adapt to evolving data, eliminating the need for manual rule adjustments.

Accelerated Incident Resolution: Enriched with sentiment analysis, the platform accelerates incident resolution by over 40%. By tagging log data with positive or negative sentiments, it prioritizes alerts for rapid troubleshooting while minimizing alert fatigue.

Dynamic Pattern Extraction & Anomaly Detection: The system uses memory-efficient pattern mining to group similar log entries and create baselines of normal behavior. This capability enables robust anomaly detection by highlighting deviations from established patterns in real time.

Comprehensive Security & Compliance: Observo AI automates security compliance by detecting sensitive data through advanced pattern recognition. It can mask or hash private information, ensuring adherence to regulations such as GDPR, CCPA, and PCI without the limitations of static tools.

Transform Optimization: With dedicated modules for various log types—such as VPC Flow logs, Firewall, OTEL, OS, CDN, and Application logs—the platform delivers tailored optimizations that enhance downstream analytics and significantly reduce compute loads.

Seamless Integration: The enriched data, complete with contextual insights and sentiment analysis, can be integrated with popular alerting and ticketing systems like ServiceNow, PagerDuty, and Jira. This integration facilitates real-time incident management and streamlines the overall workflow.

Data Pipeline Analytics

Understanding how data flows through Observo AI's processing pipeline is crucial for appreciating the platform's optimization capabilities, particularly in transforming chaotic, heterogeneous telemetry data from diverse enterprise sources into standardized, actionable formats. Our event processing pipeline follows a structured, AI-driven approach that maximizes efficiency while maintaining data integrity, addressing key challenges like inconsistent log formats such as mixed RFC 3164 and RFC 5424 standards, unstructured fields, and mismatched timestamps such as RFC1123, ISO 8601, Unix, and Syslog from over 500 sources including syslog servers, AWS CloudTrail, network devices, firewalls, and applications like sshd, snmpd, and sudo. This pipeline eliminates silos in multi-cloud and hybrid environments, enabling scalable ingestion of tens to hundreds of terabytes daily without the fragility of manual Grok rules or regex scripts that break on vendor updates.

Our data pipeline analytics is powered by a flexible three-tier model—Essential (Tier 1: out-of-the-box for 80% of common sources), AI-Assisted (Tier 2: machine learning and Orion AI Agent for pattern recognition and anomaly detection), and Custom (Tier 3: Lua scripting and bespoke schema support for proprietary formats)—orchestrated optionally by the intelligent Orion AI Agent. This ensures instant value with low-touch setup while allowing deep customization, reducing manual effort by up to 85% and achieving 98%+ field accuracy for reliable downstream analytics.

In the diagram, red is required, blue is optional, green indicates AI-Assisted and orange indicates Custom.

Tier 1 - Essential:

• Sources: Observo AI ingests raw log data from over 500+ sources using flexible methods like file uploads or streaming via the Observo Edge Collector, ensuring comprehensive data collection across diverse infrastructure and security platforms.

• Functions: Transform data in transit from sources to sinks, converting structured or unstructured data (logs, metrics, traces) into structured, actionable information. They apply a variety of data transformations—such as modifying, enriching, filtering, or aggregating fields—in real time, ensuring consistency, enhancing usability, and streamlining downstream analytics.

• Sentiment Analyzer: Applies Sentiment Analysis to score log patterns as positive, neutral, or negative based on severity, error type, and context. By monitoring sentiment trends across any ingested data, it enables teams to quickly detect anomalies or negative shifts and proactively address critical issues.

• Serializers: Supports major security formats like CEF, Cisco, Fortinet, Palo Alto, and Windows, converting normalized logs into vendor-specific schemas. Extracted and enriched fields are aligned to standard models like Splunk CIM or OCSF, ensuring seamless integration with analytics and SIEM tools.

• Optimizers: Leverage advanced aggregation, sampling, and filtering techniques to reduce data volume, drop unnecessary fields or events, and summarize information for faster processing. By intelligently routing and compressing data, they can achieve up to 74% storage cost reduction while improving overall pipeline efficiency.

• Destinations: Intelligently routes normalized and enriched data to downstream tools like Splunk or cost-efficient storage such as AWS S3, enabling priority-based routing and multi-destination delivery for optimal performance and cost management.

Tier 2 - AI-Assisted:

• Orion AI Agent: Orion is your AI Data Engineer—automatically analyzing logs, generating regex patterns, and transforming data from 500+ sources into standardized models like Splunk CIM and OCSF. With simple natural-language commands, it streamlines pipeline creation, management, and optimization.

• AI-Powered Grok Transform: Utilizes the Orion AI Agent to automatically generate regex patterns for grouping logs by format and detect log structures, extracting critical fields such as timestamps, hostnames, usernames, IPs, and protocols from unstructured logs without manual Grok rule creation.

• Data Insights: Delivers real-time analytics, cardinality analysis, and optimization suggestions by analyzing keys, percentiles, and top values within telemetry streams. Helps identify noisy patterns, redundant fields, and inefficient data structures to optimize pipelines for performance and cost efficiency.

• Pattern Extractor: Uses memory-efficient algorithms to process enriched log streams in real time, identifying recurring patterns, grouping similar events, and establishing baselines for normal behavior. Reduces data noise by condensing similar events while enabling robust anomaly detection capabilities.

• Sentiment Analysis: The integration of sentiment analysis with Pattern Extractor adds context, helping teams zero in on negative or anomalous patterns—such as potential issues—for faster diagnosis and proactive incident management.

Tier 3 - Custom:

• Custom: Supports complex business rules, unique transformations, and specialized parsing through advanced techniques such as Lua scripting to address bespoke data sources or industry-specific requirements. Enables strategic customization for evolving needs while maintaining normalized core functionality for Parsers, Functions and Destinations.

Processing Stages

Input Data Collection: The pipeline receives raw events from multiple sources including S3 buckets, Kafka streams, and network listeners. These events arrive in various formats such as log lines, JSON lines, and syslog packets, providing flexibility for diverse data ingestion scenarios. The Sources component handles data from over 500 sources, collecting up to 100 PB per month, with support for custom log sampling, timestamped event generation, and onboarding via file upload, API, agent, collector, or streaming pipeline to establish a live data flow. This stage prioritizes high-value sources like CloudTrail and core network telemetry for early ROI.

Metadata Enrichment: Upon receipt, the system automatically adds internal metadata fields to every event. These fields include source information, precise timestamps, routing metadata, and trace context, enabling enhanced control, intelligent routing, and comprehensive observability throughout the processing lifecycle. Integrated with the Functions component, this stage normalizes heterogeneous log data for efficiency, including AI-driven timestamp synchronization to harmonize diverse formats such as RFC1123, ISO 8601, Unix, Syslog and preserve compliance-critical metadata like data lineage, preventing issues such as ingestion delays and audit non-compliance.

Intelligent Parsing: The platform parses incoming events using appropriate decoders such as JSON parsing, syslog parsing, and regex extraction to extract individual fields for downstream processing. While this stage initially expands the event size significantly—as key-value pairs are extracted and stored in structured formats, creating a more detailed representation of the raw data—it lays the foundation for substantial long-term optimization. By enabling efficient analytics, noise reduction, and targeted downstream processing, it ultimately shrinks payloads and enhances overall data value. Leveraging the Parsers component, the stage ensures 80% of data sources work out-of-the-box with pre-built parsers and AI assistance to minimize manual configurations. The AI-Powered Grok Transform exemplifies this automation by dynamically generating patterns and detecting formats, thereby extracting buried fields—such as hostnames, usernames, IPs, and protocols—from noisy, unstructured text without requiring manual regex scripting. The three-tier model further enhances flexibility: Tier 1 provides Essential parsing for core functionality; Tier 2 offers AI-assisted enhancements via the Orion AI Agent for anomaly detection and adaptation to evolving structures; and Tier 3 enables Lua scripting for complex JSON or proprietary formats. Overall, this resolves malformed entries and conflicting formats, reducing parsing errors while supporting up to 2,000+ log entries per second without breaking on new vendor updates. The result is more streamlined, optimized data flows that deliver 74%+ storage cost reductions and 65–80% faster query performance in the long run.

User-Driven Optimization: Teams can implement various optimization strategies including field removal, data aggregation, content summarization, and sensitive data redaction. These optimizations dramatically reduce payload sizes before sending data to final destinations, resulting in substantial cost savings and improved performance. The Optimizers component achieves 74%+ storage cost reduction by dropping unnecessary data, while the Pattern Extractor uses event clustering and anomaly detection to reduce noise and improve visibility. Data Insights provide real-time analytics, cardinality analysis, and optimization suggestions, with Tier 2 AI assistance detecting subtle anomalies. This stage streamlines normalization, boosts performance and security, and minimizes alert fatigue by creating up to 90% fewer false positives through standardized fields and enriched context.

Serialization & Schema Mapping: Following optimization, the Serializers component prepares data for analytics platforms with vendor-specific formatting, multi-platform compatibility, and a template library. The Schema Mapping component ensures seamless compatibility with platforms like Splunk by automatically mapping data schemas to standards such as Splunk’s Common Information Model (CIM) and the Open Cybersecurity Schema Framework (OCSF). Leveraging AI assistance and the Orion AI Agent, it aligns extracted fields with Tier 1 essential mappings, Tier 2 intelligent adaptations, and Tier 3 Lua scripting for specialized needs, unifying >500 source types without manual mapping and enabling 65–80% faster query performance in SIEMs and data lakes.

Intelligent Routing to Destinations: The Destinations component intelligently delivers normalized data to appropriate endpoints—high-performance analytics like Splunk for real-time insights or cost-efficient storage like AWS S3 for long-term retention—with simultaneous multi-endpoint delivery and API extensions for custom routing. This supports a phased expansion approach, routing critical logs to SIEM while sending routine logs to low-cost storage without losing fidelity, further contributing to 74%+ overall cost reductions and compliance by avoiding fragmented silos.

Data Transformation Example

To illustrate the pipeline's efficiency, consider this typical data flow progression:

Processing Stage	Event Size	Transformation Details
Raw Event	100 bytes	Original input message in minimal format
Metadata Addition	120 bytes	Internal fields added for tracking and routing
Parsing & Extraction	400 bytes	Complete field extraction creating structured data
User Optimization	180 bytes	Targeted field reduction and payload optimization

Optimization Calculation:

Optimization % = ((Parsed Size - Final Size) / Parsed Size) × 100
Example: ((400 - 180) / 400) × 100 = 55% size reduction

This metric accurately reflects the real user-driven optimization effort after parsing, demonstrating significant efficiency gains.

Real-World Processing Example

Original S3 Input Event:

"{\"user\":\"alice\",\"action\":\"login\",\"status\":\"success\"}"

Raw Event Size: 58 bytes

After Metadata Enhancement:

{
  "_ob": {
    "bucket": "abc",
    "timestamp": "2025-08-26T10:30:00Z",
    "stream_id": "stream_001"
  },
  "message": "{\"user\":\"alice\",\"action\":\"login\",\"status\":\"success\"}"
}

Size After Metadata: 160 bytes

After Intelligent Parsing:

{
  "_ob": {
    "bucket": "abc",
    "timestamp": "2025-08-26T10:30:00Z",
    "stream_id": "stream_001"
  },
  "user": "alice",
  "action": "login",
  "status": "success"
}

Size After Parsing: 240 bytes

After User Optimization (removing status field, keeping essential data):

{
  "user": "alice",
  "action": "login"
}

Final Optimized Size: 50 bytes

This example demonstrates a remarkable 79% size reduction from the parsed state, showcasing the platform's ability to maintain data value while dramatically reducing storage and transmission costs.

Pipeline Visualization & Monitoring

The Observo AI platform provides comprehensive visual monitoring of the entire data processing pipeline through an intuitive interface that displays real-time metrics and optimization performance. This visualization layer transforms complex data flows into actionable insights for operations teams.

Real-Time Pipeline Metrics: The pipeline view maintains critical metrics at the top level, including overall Optimization percentage, Total Input volume, and Total Output volume. These metrics represent the cumulative optimization achieved across all transforms from source ingestion through to destination delivery. The overall Optimization metric reflects the combined efficiency gains from all processing stages, providing teams with immediate visibility into pipeline performance.

Transform-Level Analytics: Each individual transform within the pipeline maintains detailed input and output metrics accessible through single-click interactions. Teams can drill down into specific transformation steps to understand bottlenecks, monitor processing volumes, and identify optimization opportunities. The visual connections (edges) between sources, transforms, and destinations display real-time data volume transitions, making it easy to trace data flow and identify processing inefficiencies.

Enhanced Metric Discovery: Interactive elements provide layered access to deeper analytics. Single-clicking on Optimization percentages or Total Input metrics reveals additional details including parsed optimization ratios and parsed data input volumes. Double-clicking these metrics provides direct navigation to the comprehensive Analytics Dashboard for detailed analysis and historical trending.

Log Preview & Transform Insights: The integrated Log Preview panel offers immediate access to transformation details without requiring navigation to separate windows. The Info tab within this panel presents essential Event Count metrics (Inputs, Outputs) alongside Event Size analytics (Inputs, Outputs, Optimizations), enabling rapid troubleshooting and performance assessment directly within the pipeline view.

This visualization approach ensures that teams can quickly assess pipeline health, identify performance issues, and make data-driven decisions about optimization strategies while maintaining full visibility into the data transformation process.

Analytics In-Practice

Observo AI Analytics is designed to empower organizations by optimizing and deriving actionable insights from large volumes of operational data. It combines multiple specialized capabilities that work together to transform raw telemetry and log data into highly relevant, enriched information. The solution is particularly valuable for security, DevOps, and IT operations teams, who rely on real‑time insights for incident response and system optimization.

Building upon the robust pipeline architecture detailed above, the platform's analytics capabilities provide comprehensive visibility and control over data processing workflows through specialized dashboards and intelligent analysis tools.

Analytics Dashboard

Observo AI Analytics enhances data processing efficiency by providing real-time insights, intelligent optimizations, and streamlined transformation metrics. Key capabilities include:

• Data Processed: Offers a near real-time view of your pipeline by showing the input data, processed output, and filtered-out data for each transform. This clear visualization helps teams quickly spot bottlenecks and optimize performance to ensure only high-value data reaches downstream systems.

• Optimization: By leveraging AI and machine learning algorithms, the system dynamically optimizes data flows. This includes real-time adjustments to ensure that the processing pipelines are operating at peak efficiency and can adapt as data patterns change.

• Transformations Optimization: This aspect focuses on refining the transformation steps needed to convert raw logs and telemetry into structured, analyzable formats. The optimization reduces latency, lowers resource usage, and ensures that subsequent analysis has a consistent, high-quality input.

Data Insights Dashboard

Observo AI Analytics empowers teams with deep visibility into log data by identifying key trends, summarizing critical insights, and uncovering emerging patterns. Its core capabilities include:

• Log Data Summary by Keys: By summarizing log data based on key identifiers (such as user IDs, IP addresses, or transaction types), this feature provides a quick snapshot of the overall data landscape. It helps teams rapidly identify areas of interest or concern.

• Tags Trends for Patterns: This functionality involves tracking and analyzing tag data associated with logs. By monitoring trends in these tags, the system can highlight emerging patterns or shifts that might indicate underlying issues or opportunities for optimization.

• Patterns Trend: Beyond individual tags, this capability aggregates and visualizes recurring patterns in the data over time. Recognizing these patterns allows teams to forecast potential issues, validate system performance, and make data-driven decisions.

Pattern Extractor

Observo AI Pattern Extractor offers a set of advanced capabilities designed to enhance log data processing, analysis, and security. Key features include:

• Log Metadata Enricher: Enriches the raw log data with additional metadata, adding context (like source information, geographical location, or system context) that is crucial for deeper analysis.

• Pattern Extractor: At its core, this capability employs sophisticated algorithms to automatically detect and extract recurring patterns from log data. It isolates signals from noise, enabling security teams to focus on anomalies or significant trends that might indicate security breaches or operational issues.

• Sentiment Analyzer: Integrated into this capability is a sentiment analysis engine. Used on text-based logs, it gauges the "mood" or sentiment behind data entries. This can be particularly useful in various security and observability scenarios, where understanding sentiment trends can lead to proactive service improvements or threat detection.

Sentiment Analyzer

Sentiment Analyzer is embedded within the Pattern Extractor and focuses on extracting sentiment from various structured and unstructured data sources. It allows teams to monitor the overall sentiment trends and quickly identify shifts that may require immediate attention.

Key Benefits

Observo AI Analytics delivers powerful data intelligence by optimizing processing, enhancing security, and providing actionable insights. Here's how it stands out:

• Operational Efficiency: By processing and optimizing only the most valuable data, Observo AI Analytics dramatically reduces storage, compute, and ingestion costs—often by more than 50% compared to traditional approaches.

• Enhanced Security: Through advanced pattern extraction and anomaly detection, the platform aids in identifying potential threats faster, thereby lowering the mean time to resolve (MTTR) critical incidents.

• Actionable Insights: The detailed data insights capability transforms complex log data into clear, actionable intelligence. This empowers both security and DevOps teams to prioritize issues and focus resources where they're most needed.

• Scalability and Flexibility: With its modular design, Observo AI Analytics can scale alongside growing data volumes and integrate with a variety of data sources and destinations—making it adaptable to diverse IT environments.

Overall, Observo AI Analytics represents a forward-thinking approach to modern data management. It leverages AI-native technologies to turn overwhelming amounts of unstructured telemetry into refined, actionable intelligence, enabling organizations to not only cut costs but also enhance both operational resilience and security posture.