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Custom AI Anomaly Detection Solutions for Early Risk Detection

Improving operational intelligence with AI-powered anomaly detection for real-time monitoring, abnormal pattern identification, and risk detection

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Helping organizations detect critical anomalies and accelerate incident remediation with custom AI anomaly detection solutions

Business digitization and the growing scale, speed, and complexity of modern data make it difficult to identify anomalies. At SHALIGRAM, our advanced AI anomaly detection solutions enable continuous monitoring of operational data, identification of outliers from the baseline, and detection of unusual behavior and hidden risks.

Deloitte predicted that GenAI-enabled fraud losses could reach US$40 billion in the United States by 2027. At SHALIGRAM, we develop AI-powered anomaly detection systems to curb such losses by bringing AI-first resilience in the form of anomaly detection solutions that provide real-time alerts.

Solving challenges caused by reactive monitoring approaches with AI intelligence

Business Challenges SHALIGRAM Solve

Hidden operational risks

We deploy proactive AI-powered anomaly detection systems to identify blind spots and reduce delays in root cause analysis.

Unplanned disruptions

We develop systems that flag minute operational deviations and trigger explainable alerts before failures occur.

Equipment failures

Our experts train AI models to learn the normal operating baseline of a machine and flag early signs of failure.

Process anomalies

We develop platforms that use operational data streams and flag micro-deviations before they turn into costly failures.

Quality inconsistencies

We help solve quality inconsistencies spanning data, product manufacturing, and digital performance using AI solutions.

Fraud exposure

We apply AI at different layers of your enterprise, ranging from user behavior and data infrastructure to deep transaction monitoring to detect fraud.

Delayed issue identification

We design systems that track your infrastructure’s health and map system dependencies while using multi-model statistical learning to detect anomalies.

Excessive manual monitoring

We remove the need for manually monitoring dashboards or tweaking alert triggers through AI-powered platforms.

Data overload

We solve data overload by replacing manual static alerting with automated baselining, event correlation, and edge-to-cloud filtering.

Slow response times

We resolve slow response times by using ML baselines and graph-based causal AI to detect degradations and pinpoint root causes in real time.

Events and abnormalities identified through AI-based real-time monitoring

Equipment performance anomalies

Deviations are detected by streaming data from sensors attached to machines to a cloud or edge model.

Process deviations

Live operational data is compared against normal to spot unusual sensor readings, execution times, and gradual shifts in parameters.

Production abnormalities

We help you identify production abnormalities by comparing ingested data from machinery or software performance against trained AI models.

Product quality anomalies

Cameras or inline measurements fed to computer vision models are used to find defective units and pull them before they reach packaging.

Inventory irregularities

AI models establish expected inventory levels and movement patterns from ERP and warehouse data, then flag deviations from expected levels.

Transaction anomalies

Unusual transaction amounts or sudden changes in transaction frequency are found by training AI models on customer behavior data.

User behavior anomalies

Unusual login times or locations and abnormal data downloads are flagged, and alerts are fired on combined evidence.

System performance anomalies

Metrics, logs, and user journeys are tracked against evolving baselines to help identify contributing services or sources of anomalies.

Network anomalies

Network traffic is unpredictable, and anomaly detectors track anomalies related to changes in traffic volume, connection behavior, or timing.

Sensor anomalies

ML and deep learning models are deployed to track and flag regularities, spikes, drop-outs, drift, or noise from sensors for maintenance teams to investigate.

Energy consumption abnormalities

Energy consumption changes if there is a change in production volume or the operating conditions. These deflections are monitored through anomaly detectors, and action is taken accordingly.

Supply chain disruptions

Supply chain disruptions and delays in shipment are flagged by intelligent anomaly detection systems. The alerts help plan detours or assist in planning the next course of action.

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Capabilities enabling intelligent anomaly identification

Pattern recognition

We analyze complex data and identify unusual patterns and isolate potential sources of failure through anomaly detectors.

Behavioral modeling

We use User and Entity Behavior Analytics (UEBA) to flag deviations from normal behavior when malicious or faulty behavior occurs.

Real-time event monitoring

Our solutions help detect incidents early before they spread by ingesting a large volume of data simultaneously for faster response times.

Predictive intelligence

Systems are maintained by analyzing trends and forecasts when a component or service shows the prospect of crossing a failure threshold.

Root cause analysis

Our AI models narrow down likely causes of anomalies, reducing the MTTR and computing conditional probabilities of failure origins.

Risk scoring

We convert abstract security or performance anomalies into quantifiable numbers for strategic prioritization.

Automated alert generation

Subtle deviations such as unusual network traffic shifts or performance dips are identified and signal generated about emerging security threats or failures.

Multi-variable correlation analysis

Data across variables and sources are correlated to detect complex anomaly patterns, identify contributing factors, and find potential issues.

Historical trend comparison

Past data is analyzed using AI and compared with system behavior to identify unusual changes and anomalies.

Adaptive learning models

We perform monitored retraining of AI anomaly tools so that they automatically update and evolve as your company's systems change and grow.

Automated anomaly classification

We build AI systems that categorize deviations so they are routed to the correct remediation teams and systems.
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Business functions strengthened through AI-powered monitoring

Equipment health monitoring

Maintenance teams get early warnings and alerts about abnormal vibration and changes in temperature patterns through an AI-based system that is fed data from equipment sensors.

Equipment health monitoring

Recent projects & case studies

From startups to enterprises – see how our expertise in software consulting and development in the USA has led to measurable impact across industries.

Machine Monitoring System

Reduced unplanned machine downtime by 99% for an automotive component manufacturer

SHALIGRAM built an AI predictive maintenance platform that analyzes machine performance, sensor data, and historical logs to predict and prevent unexpected equipment failures.

Industry
Manufacturing
Technologies
AI, Machine Learning
Challenges
  • Frequent unexpected machine breakdowns
  • Heavy maintenance backlogs & high costs
  • No real-time equipment health visibility
  • Unoptimized reactive maintenance schedules
Business impact
  • 99% reduction in unplanned machine downtime
  • 85% reduction in unexpected equipment breakdowns
  • 42% faster maintenance response time
  • 28% improvement in overall equipment availability
View case study
print-defect-detection-ai-system

Built AI-powered label inspection system to reduced print defects

Built AI-powered label inspection automated system to reduce print defects for label and printing companies.

Industry
Printing
Technologies
Next.js, React
Challenges
  • High-volume manual bottlenecks
  • Hard-to-detect visual & code defects
  • Lack of defect visibility & high rework
Business impact
  • 99%+ defect detection accuracy
  • 30% reduction in rework & 60% effort saved
  • Data-driven quality control
View case study

Industries benefiting from AI anomaly detection

Manufacturing

We empower manufacturers by improving OEE by exposing load imbalances, parameter drift, and unstable performance, and alerting technicians to abnormal readings through AI-powered systems.

Logistics and supply chain

Logistics companies can monitor vehicle malfunctions and door openings in refrigerated trailers, along with insight into the likelihood of delay with AI-based anomaly detection.

Healthcare

We help healthcare organizations identify abnormalities in reports or medical imaging with configurable detection thresholds that help control false positives and reduce unnecessary alerts.

BFSI

BFSI organizations facing modern threats are empowered through anomaly detection for AML and KYC. We help BFSI to detect fraudulent transactions, cyber threats, and compliance risks through our AI anomaly detection solutions.

Retail and eCommerce

Retailers find pricing errors, poor allocation, mis-executed promotions, and unit miscounts, and receive explainable alerts on unusual product movement and point-of-sale errors.

Energy and utilities

Our AI systems flag deviations in data from transformers or renewable integration grids, and detect irregularities in smart meters in near real time.

Telecommunications

We develop systems that analyze call detail records and network packet data to identify robocalling, spoofing, SIM swapping, and DoS or DDoS attacks.

Pharmaceuticals

We develop systems to maintain safety, regulatory compliance, and strict quality control, and surface data integrity risks, such as backdating patterns, before an inspection.

Smart infrastructure

We use sensor networks on bridges and civil structures to feed anomaly detection systems that reduce reliance on time-consuming manual inspection.

Why organizations invest in AI anomaly detection

MLops pipeline development

Reduced operational risk

AI solutions help you spot abnormal machine behavior before breakdowns and potential attempts at breaching security, thus reducing financial losses.

Lower technology costs

Lower downtime-related losses

Connecting your machine data to AI systems helps build alerts into existing maintenance and production workflows, giving your teams time to act.

Dashboard assessment

Improved asset reliability

AI anomaly detection solutions assess asset health from industrial data and give predictive warnings.

Healthcare compliance and reporting automation

Faster incident response

Get a quick response through correlation and root cause analysis added on top of anomaly detection, and receive alerts from different monitoring sources in a contextualized incident.

business-intelligence-consulting

Stronger business continuity

Various inputs are aggregated from monitors and sensors and are used to provide early warning, thus shortening the time to decision and reducing financial risks.

Better security

Better resource utilization

Anomaly detectors help in better utilization of resources, cloud instances, and flag unexpected cost spikes. Our experts design anomaly detection solutions that detect wasteful activity and irregular cloud spend.

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Increased operational resilience

Anomaly detectors work with your telemetry and logs, catch technical and transactional anomalies, and feed data back into the models to improve operational resilience.

Intelligent decision automation

More confident decision-making

Get greater context around alerts, understand the signals and factors behind anomalies, and trace them right back to their source for faster decision-making.

Model monitoring

Reduced monitoring overhead

AI systems help consolidate a flood of separate notifications into a single incident alert, so that your teams work on the root cause.

Integrating AI anomaly detection solutions with existing systems and data sources

Organizations function with multiple operational systems, and issues in any one cause a larger operational issue everywhere. Integration of these systems with anomaly detectors helps provide end-to-end visibility across these disconnected systems so that even a small deviation gets tracked, and action is taken before it escalates into a larger issue. 

  • ERP systems
  • MES platforms
  • Quality management systems
  • Supply chain platforms

Our approach to building custom anomaly detection systems

01

Business and risk assessment

We define the end goals and decide the relevant use case based on what an anomaly costs your business.
02

Data discovery and preparation

We inventory the data sources and apply feature engineering to prepare relevant inputs for anomaly-detection models.
03

Model design and training

Our experts choose unsupervised, supervised, or deep learning models based on data type, volume, and objectives.
04

Validation and testing

We ensure that the model accurately identifies irregular patterns while minimizing false alerts by tuning thresholds against your labeled incidents.
05

System integration

We connect the system to client data sources and workflows through APIs and modular services and build the enterprise integrations into existing platforms.
06

Deployment and optimization

We offer MLOps, drift monitoring, alert quality tracking, retraining workflows, performance dashboards, and SLA-backed support.

Why organizations choose SHALIGRAM for anomaly detection solutions

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Production-ready anomaly detection systems

From data ingestion to model monitoring and remediation workflows, we build anomaly detection systems around your operational environment. We help you prevent financial fraud, reduce costly system downtime, and handle enormous data volumes through our advanced AI anomaly detection solutions. 

  • AI-driven causal and behavioral analytics
  • Advanced multi-modal ingestion capabilities
  • Robust governance & compliance
  • Faster remediation and automated workflows

Uncover anomalies and hidden operational risk with our advanced AI anomaly detection solutions

Move beyond static rules. Connect with our experts to learn how AI-powered anomaly detection helps you grow.

FAQs

Trusted by businesses around the world for their digital transformation journey

Organizations must check their current systems and their capabilities to handle anomaly detection tools. Next, they need to evaluate data readiness. Without these, anomaly detection systems won’t bring the desired results.