Anomaly Detection
Catch What Dashboards Miss
When your data volumes grow, manual monitoring fails. GRAVITI builds anomaly detection systems that surface unexpected patterns in transactions, operations, and infrastructure before they become costly incidents.
Microsoft Azure
Amazon Web Services
Google Cloud
IBM Cloud
Oracle Cloud- Full flexibility in deployment options. We are not commercial partners of software vendors
Who Is It For
Anomaly detection is critical for organizations managing high-volume data streams where manual review is impractical.
- Finance teams responsible for fraud detection and transaction monitoring
- IT and DevOps teams managing infrastructure performance and uptime SLAs
- Operations leaders tracking manufacturing quality or logistics deviations
- Compliance officers who need automated surveillance of regulatory thresholds
Our Approach to Anomaly Detection
GRAVITI designs anomaly detection pipelines that go beyond static threshold alerts. We implement statistical and machine learning methods—including isolation forests, autoencoders, and time-series decomposition—to identify patterns that deviate from expected behavior across your operational data.
Our engineers work with your domain experts to define what "normal" looks like for each data stream, then build models that adapt as your baselines shift. This means fewer false positives and faster identification of genuine issues, whether that is an unusual spike in transaction refunds, a sensor reading outside tolerance, or an API response time degradation.
Every detection system we build includes an alerting layer integrated with your existing incident management tools—Slack, PagerDuty, ServiceNow, or email—so the right people are notified immediately with contextual information to act on.
Connecting to systems already in your organization
Our solutions include integration with popular market systems, as well as any additional system as needed
How We Deliver
- Data Profiling: Map data streams and establish statistical baselines for normal behavior
- Model Selection: Choose detection algorithms matched to your data characteristics—batch vs. streaming, univariate vs. multivariate
- Threshold Calibration: Tune sensitivity to balance detection rates against false-positive tolerance
- Alert Integration: Connect detection outputs to your incident management and notification systems
- Feedback Loop: Incorporate analyst feedback to continuously improve detection precision
Expected Outcomes
- Early detection of fraud, errors, and operational deviations—hours or days ahead of manual discovery
- 60-80% reduction in false-positive alerts compared to static threshold systems
- Automated root-cause context delivered with each alert for faster triage
- Continuous model improvement through analyst feedback integration
Service Model
- Discovery: 2-week data stream audit and baseline analysis
- Build: 6-8 week model development, calibration, and alert integration
- Managed: Ongoing monitoring, model retraining, and false-positive review
Frequently Asked Questions
What types of anomalies can be detected?
Our systems detect point anomalies (single outlier values), contextual anomalies (values unusual for a specific time or condition), and collective anomalies (unusual patterns across multiple data points). The specific types depend on your data and business context.
Does this work with real-time data?
Yes. We build both batch and streaming detection pipelines. Streaming implementations can process events in near-real-time using tools like Apache Kafka and Flink, with typical detection latency under 60 seconds.
How do you reduce false positives?
We use adaptive baselines that account for seasonality, trend shifts, and known events. Combined with analyst feedback loops, our models typically achieve 60-80% fewer false positives than static-threshold approaches within the first quarter of operation.
Stop Reacting, Start Detecting
Anomalies in your data are costing you money, reputation, and compliance standing. Let GRAVITI build a detection system that catches problems before they escalate.
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