Real-time data monitoring continuously watches streams of business, machine or system data, detects unusual patterns with rules and machine learning, and alerts the right people within seconds or minutes, so problems are fixed before they grow.
Some problems cannot wait for tomorrow's report. We build real-time monitoring that processes streaming data as it arrives, detects anomalies with machine learning and triggers instant alerts or automated responses.
Detect anomalies and respond instantly with streaming data insights. At Prominent AI Services LLP, we deliver real-time data monitoring end to end, from discovery and data preparation to deployment, monitoring and support, so your teams get dependable results quickly and securely.
Detect anomalies and respond instantly with streaming data insights.
Ideal for: Cybersecurity, Manufacturing, Finance. We assess your current data flows and model operations, design an integration and MLOps architecture, automate deployment pipelines and set up monitoring dashboards and alerts for data and model health.
We stream data with Apache Kafka, Azure Event Hubs or Amazon Kinesis, process it with Spark Structured Streaming, Azure Stream Analytics or Flink, and store it in time-series stores such as Azure Data Explorer. Anomaly detection uses statistical methods and machine learning models built in Python. Alerts go to email, SMS, Microsoft Teams, Slack or incident tools, and live views are built in Power BI or Grafana. Cloud options include Azure and AWS.
Choose a fixed-scope build, or a managed monitoring service where we maintain the detection models. You can also hire data engineers to extend your platform. Related services: data engineering and MLOps for monitoring AI models in production.
Want to know about problems as they happen? Tell us what you need to watch, and we will design a monitoring solution. Start your AI journey.
Common questions about our Real-Time Data Monitoring services.
Fraudulent transactions, equipment faults, security threats, website outages and sudden changes in demand or quality metrics.
Typically within seconds of the event, through email, SMS, Teams, Slack or direct system actions.
Yes. ML-based anomaly detection learns normal patterns, which reduces noise compared with static thresholds.
Cost depends on data volume, the number of sources and the complexity of detection. Streaming infrastructure has an ongoing cloud cost, which we estimate from your event volumes before you commit.
A first set of monitored events with alerts and a live dashboard usually takes four to eight weeks, including tuning on historical data.
Yes. We connect to databases, logs, APIs, message queues and IoT devices, usually without changing the source systems.
Dashboards show data when someone looks at them. Real-time monitoring watches the data all the time and alerts people automatically when something needs attention.
