Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Deploy AI that automatically identifies the primary source of complex IT failures, reducing manual investigation from hours to seconds.
Our graph-based causal inference algorithms analyze multi-layer dependencies across your entire stack—from application code to network infrastructure—to pinpoint the exact failure origin, not just correlated symptoms. This delivers:
OpenTelemetry and vendor APIs.Move from reactive firefighting to proactive system intelligence. We engineer deterministic analysis that learns your unique environment's failure patterns.
Implementation delivers measurable outcomes in weeks:
ServiceNow or PagerDuty workflow, delivering automated root cause tickets.This service is part of our comprehensive Artificial Intelligence for IT Operations (AIOps) pillar, which also includes Predictive IT Incident Management and Intelligent Network Monitoring AI.
Our automated root cause analysis engineering delivers quantifiable improvements to your IT operations, directly impacting your bottom line. Here are the key results you can expect.
Automatically pinpoint the primary source of multi-layer failures, reducing manual investigation from hours to minutes. Our graph-based causal inference algorithms correlate events across your stack to identify the true root cause, not just symptoms.
Learn more about our approach to Predictive IT Incident Management.
Move from thousands of noisy, low-level alerts to a handful of high-fidelity, actionable incidents. Our intelligent correlation clusters related events and suppresses duplicates, allowing your SRE team to focus on what matters.
This capability is a core component of our Intelligent Network Monitoring AI.
Shift from reactive firefighting to proactive stability. By analyzing historical patterns and real-time telemetry, our models identify precursor signals, allowing you to remediate issues before they cause user-facing downtime.
This predictive capability is enhanced by our IT Operations Anomaly Detection Systems.
Gain a single pane of glass across AWS, Azure, GCP, and on-premises environments. Our platform ingests and correlates data from all sources, providing holistic context for root cause analysis regardless of where the failure originates.
Build towards truly self-healing systems. Our precise RCA provides the trusted diagnostic layer required to safely execute automated remediation scripts, creating a closed-loop for common failure patterns and reducing manual toil.
Achieve direct ROI through reduced downtime, optimized resource allocation, and improved team efficiency. The reduction in MTTR and incident volume translates into hard cost savings and allows your engineering talent to focus on innovation.
A clear breakdown of the phases, key outputs, and typical timeframes for our Automated Root Cause Analysis Engineering service, designed for predictable delivery and rapid ROI.
| Phase | Key Deliverables | Typical Duration | Outcome |
|---|---|---|---|
Discovery & Data Assessment | Data source audit report, RCA feasibility analysis, initial graph schema | 1-2 weeks | Validated project scope and data readiness |
Algorithm Design & Prototyping | Causal inference model architecture, proof-of-concept on sample data, performance baseline | 2-3 weeks | Working prototype demonstrating core RCA logic |
Pipeline & Integration Engineering | Production-grade data ingestion pipelines, integration with monitoring tools (e.g., Datadog, Splunk), API endpoints | 3-4 weeks | Fully integrated system ingesting live telemetry |
Model Training & Validation | Trained graph neural network models, validation against historical incidents, explainability dashboard | 2-3 weeks | AI models meeting accuracy targets (>90% primary cause identification) |
Deployment & Pilot Launch | Deployed microservices, pilot configuration for 1-2 critical services, operational runbooks | 1-2 weeks | Live RCA system operating in a controlled environment |
Monitoring, Tuning & Handoff | Performance monitoring dashboards, fine-tuning report, knowledge transfer sessions, SLA documentation | Ongoing (2+ weeks) | Optimized system with your team fully enabled for ongoing management |
Our causal inference and graph-based AI algorithms are engineered to solve complex, multi-layer IT failures across critical industries. Reduce manual investigation time from hours to seconds and achieve measurable improvements in system reliability and operational efficiency.
Automatically trace the source of trading platform latency, payment gateway failures, or fraud detection system anomalies. Our algorithms correlate market data feeds, order books, and network telemetry to pinpoint root causes, ensuring compliance with strict SLAs and minimizing revenue-impacting downtime.
Learn more about our work in Financial Services Algorithmic AI and Risk Modeling.
Identify the precise cause of checkout failures, inventory sync issues, or recommendation engine degradation during peak traffic. Our systems analyze application logs, microservice dependencies, and CDN performance to isolate failures, protecting conversion rates and customer experience.
See how we enable Retail and E-Commerce Hyper-Personalization.
Rapidly diagnose failures in EHR systems, medical imaging pipelines, or patient monitoring IoT networks. Our privacy-preserving causal models work across sensitive, siloed data sources to ensure critical health IT systems remain operational, directly supporting patient care continuity.
Explore related solutions in Healthcare Clinical Decision Support and Ambient AI.
Isolate performance issues to specific tenants, features, or underlying infrastructure components. Our graph-based analysis maps complex service dependencies across shared environments, preventing localized issues from cascading and impacting overall platform stability.
Pinpoint the root cause of network congestion, dropped calls, or degraded service quality by analyzing RF signal data, core network elements, and subscriber telemetry simultaneously. Move beyond simple threshold alerts to understanding the causal chain in dynamic spectrum environments.
Correlate failures across OT and IT layers—from PLCs and sensors to MES and ERP systems. Our algorithms identify whether a production line stoppage originated from a mechanical fault, a network anomaly, or a software bug, drastically reducing costly unplanned downtime.
Integrate with Smart Manufacturing and Industrial Copilot solutions.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get clear answers about our engineering process, timelines, and outcomes for implementing automated root cause analysis in your IT environment.
We follow a proven, four-phase engineering methodology. First, we conduct a data and topology discovery audit to map your environment's dependencies. Next, we develop and train causal inference models on your historical incident data. We then integrate the AI engine with your existing monitoring tools (like Datadog, Splunk, or Dynatrace) via APIs. Finally, we implement a closed-loop validation system where the AI's root cause predictions are measured against actual resolutions to ensure continuous accuracy improvement. This process is based on our experience delivering 50+ AIOps projects.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
