Eagle Eye from Celebal Technologies is an AI-powered, end-to-end data observability platform that safeguards trust in enterprise data by continuously monitoring, profiling, and validating data and data pipelines. Designed for data leaders, engineering teams, and enterprise stakeholders, it reduces data downtime, ensures compliance, and supports confident real-time decision-making. By addressing fragmented silos, delayed dashboards, broken pipelines, and costly manual remediation, Eagle Eye eliminates risks that often lead to revenue loss, operational inefficiency, and declining stakeholder confidence in data.

At its core, Eagle Eye delivers a structured observability flow that spans the entire data lifecycle. It begins with seamless connectivity across cloud and on-prem sources, followed by automated asset loading, detailed profiling, and metadata/log scanning to detect anomalies at both attribute and system levels. Proactive alerts highlight potential risks, while validation and resolution workflows accelerate remediation. With feedback mechanisms improving accuracy over time, enterprises gain transparency, faster root-cause discovery, and a granular view of data health report—reducing resource-heavy interventions and ensuring compliance with service-level agreements.

The Eagle Eye Observability Stack unifies monitoring across multiple layers. Its user interface is customizable and built for continuous feedback. The data and platform layer provides live monitoring, real-time alerting, lineage tracking, and automated quality rule recommendations. Advanced AI and ML capabilities—spanning anomaly detection, forecasting, drift analysis, root-cause analytics, and auto-ticketing—are powered by models like OpenAI and Llama. At the analytics layer, Power BI dashboards deliver insights into anomaly trends, governance audits, and cost observability. Together, these capabilities establish end-to-end data reliability, empowering enterprises to scale operations with confidence.

Core Capabilities:

- Automated data quality checks with anomaly detection.

- GenAI-powered root cause analysis and table health reports.

- Pipeline failure identification without SQL.

- Conversational chatbot for Q&A, RCA, and ticketing.

- Unified dashboards with lineage, trends, and governance insights.

- Personalized RBAC alerts with proactive ticketing.

- Seamless integration with Outlook, Teams, Jira, and Azure DevOps.

- Automated catalogue of reusable data quality checks.

- GenAI–observability convergence for insights and decision support.

Industry Use Cases:

- E-commerce: Resolved inventory glitches during flash sales, improving speed and customer trust.

- Healthcare: Eliminated data mismatches across systems to boost report accuracy.

- Financial Services: Ensured real-time, consistent data for better fraud detection and compliance.

- Energy: Fixed data silos to improve targeting and reduce product returns.

Business Impacts:

- Faster Issue Detection: Reduced MTTD from 6 hours to 5 minutes.

- Accelerated RCA: RCA turnaround improved from 3–4 days to just 1 hour.

- Improved Accuracy: False alerts minimized through feedback-based learning.

- Productivity Boost: ~90% auto-detection enabled, eliminating manual effort.

- Enhanced Trust: High confidence in reports with strong data quality.

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