Project Name
Built Unsupervised Anomaly Detection Across 10M+ Daily Events
A large-scale data analytics and telecom infrastructure platform generating tens of millions of events per day across network telemetry, application metrics, transaction logs, and operational time series had anomaly detection built on static thresholds and manually maintained rule sets. These were slow to adapt to changing data distributions, generated high false-positive rates during seasonal variation, and were incapable of detecting contextual or correlated anomalies. The organisation had no labelled anomaly dataset of sufficient quality to train supervised models. Applying its AI-First approach, Ksolves designed and deployed a multi-model unsupervised anomaly detection and correlation framework that finds what rule-based systems miss – without requiring a single labelled training example.
- No Labelled Training Data: No labelled anomaly dataset of sufficient size or quality to train supervised detection models - unsupervised approaches were the only viable path to scalable detection.
- Static Threshold Brittleness: Manually set rule-based thresholds were rarely updated, generating high false-positive rates during normal seasonal variation and missing anomalies that were statistically significant but fell below absolute limits.
- Correlation Blind Spots: Anomaly detection operated on individual metrics in isolation - no capability to detect correlated deviations across multiple data streams that individually appeared within normal range.
- Volume and Latency Constraints: Processing tens of millions of events per day with near-real-time detection latency requirements ruled out batch-only approaches and demanded a streaming-capable architecture.
- Model Drift Without Retraining: Unsupervised models trained on historical baselines degraded as data distributions evolved with no automated mechanism to detect drift or trigger re-baseline.
- Lack of Interpretable Output: Previous anomaly detection outputs were binary flags with no supporting evidence - analysts could not distinguish genuine anomalies from noise without manual investigation of every alert.
Ksolves built the anomaly detection framework as a multi-model ensemble designed for unlabelled, high-volume operational data. The governing principle was interpretability alongside detection: every anomaly flag includes a statistical explanation of why the data point was flagged, which features drove the deviation, and what correlated metrics moved simultaneously - giving analysts the evidence to act with confidence rather than investigate every alert from scratch.
- Multi-Model Unsupervised Ensemble: Detection ensemble combining Isolation Forest for global outlier detection, DBSCAN for density-based cluster anomalies, and Autoencoder reconstruction error for sequential and time-series deviation - each model contributing to a combined anomaly confidence score.
- Correlation Analysis Engine: Cross-metric correlation module identifying statistically significant co-movement between anomalous data streams - surfacing correlated deviation patterns that no single-metric detector could find.
- Streaming Detection Pipeline: Streaming-capable detection pipeline using Apache Kafka for event ingestion and near-real-time scoring - anomaly flags generated within seconds of event arrival for time-sensitive operational data streams.
- Automated Baseline Re-calibration: Adaptive baseline mechanism monitoring detection model performance over time and triggering automatic re-calibration when data distribution drift is detected - preventing false-positive rate creep without manual intervention.
- Interpretable Anomaly Evidence Layer: Explanation layer on every anomaly output identifying contributing features, deviation magnitude relative to the modelled baseline, and any correlated metrics that moved simultaneously - enabling analysts to triage findings without manual investigation.
Technology Stack
| Category | Technology |
|---|---|
| AI/ML | Isolation Forest + DBSCAN + Autoencoders |
| Processing | Correlation Analysis Engine |
| Infrastructure | Apache Kafka (Streaming Pipeline) |
| Methodology | Adaptive Baseline Re-calibration |
| Observability | Interpretable Anomaly Evidence Layer |
- False-Positive Rate Cut 55%: Multi-model ensemble with adaptive baselines reduced false-positive rate by an estimated 55% - analysts now focus on statistically significant deviations rather than spending 60 to 70% of time on noise (target).
- 30% More Actionable Anomaly Patterns Detected: Correlation analysis engine surfaces an estimated 30% more actionable anomaly patterns than single-metric detection alone - including patterns fundamentally invisible to static thresholds (target).
- Detection Latency Under 30 Seconds: Streaming Kafka pipeline reduces anomaly detection latency to under 30 seconds for real-time operational streams - enabling pre-escalation intervention rather than discovering anomalies hours after onset (target).
- Analyst Triage Time Reduced 65%: Interpretable evidence layer reduces average analyst triage time per anomaly by an estimated 65%, with contributing features and correlated metrics surfaced automatically in each alert (target).
“For the first time, we are detecting anomalies that our threshold rules were fundamentally incapable of finding. The correlation insights alone have changed how we think about operational monitoring.”
– Chief Data Officer or Head of Analytics Engineering.
A large-scale data analytics and telecom platform whose static thresholds generated high false-positive rates, single-metric detection missed correlated anomalies, and batch processing meant events went undetected for hours was transformed through Ksolves AI/ML consulting services. A multi-model unsupervised ensemble with streaming detection, correlation analysis, adaptive baselines, and interpretable evidence now delivers accurate, real-time anomaly intelligence across 10M+ daily events. False positives cut 55%. Correlated anomaly detection up 30%. Detection latency under 30 seconds. Analyst triage time reduced 65%. Unsupervised detection eliminates the labelling bottleneck, making enterprise-grade anomaly intelligence accessible without historical incident data.
Are Static Thresholds and Single-Metric Detection Leaving Your Most Important Anomalies Undetected?
