Trusted by companies of all sizes
Predictive AI SRE for Trustworthy Production
InsightFinder ARI AI SRE is the only Predictive Reliability Agent, powered by our patented unsupervised AI engine to predict failures hours before impact, investigate emerging problems, identify and explain root causes, and recommend or execute corrective actions—helping prevent incidents across your entire production stack, from AI agents and applications to IT services and infrastructure.
Built for modern reliability across services, infrastructure, agentic AI workflows, and operations.
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Move from reactive alerting to proactive prevention
Predict
We start hours before the outage.
Modern enterprise systems rarely fail all at once. A small problem starts, propagates, and only then slows down performance. That propagation time is a prediction window, and most tools spend it waiting for a threshold to break. InsightFinder's unsupervised engine learns your environment's real behavior with no rules and no thresholds to configure, and uses causal inference rather than correlation. We show you what happened first, what it’s impacting, and the root cause of incidents that haven’t happened yet.
Investigate
ARI works the problem and gathers evidence.
When our engine predicts a failure path, ARI, our AI SRE agent, opens an investigation. It analyzes your agents, models, data, and infrastructure together, in one pass. ARI works across your entire stack. And it doesn't stop at the telemetry you already collected: ARI proactively probes the suspect systems and their upstream dependencies to confirm the cause and rule out the rest. Go beyond incident summaries with real AI-driven investigations.
Explain
Causal chain evidence in your own vocabulary.
Root cause analysis runs on our own proprietary causal inference and dependency AI systems (not on the same LLMs everyone else uses). LLMs predict text; we take it further. We use small language models, customized to your domain, for one job: writing the explanation in the language your teams actually use for your systems. Every finding carries citations and links straight through to the underlying evidence. Analysis lands in seconds, not after an indexing job or external calls to frontier models.
Prevent
Remediations arrive before the impact does.
Because the prediction came first, the fix is a prevention rather than a recovery. ARI recommends the action and, with human-in-the-loop confirmation, implements the procedure: roll back the deployment that started the resource leak, open the Jira ticket with the full technical report attached, run the probe, etc. ARI works where you are: desktop, mobile, Slack, or Teams.
Trace every AI incident back to its source
A production AI application can fail in one layer while the underlying cause lives somewhere else. A model may appear to be producing poor results when the real issue is stale data, a degraded API, a changing dependency, or an infrastructure problem.
InsightFinder connects AI behavior with available operational signals and system dependencies to help teams investigate failures across the full technology stack. See how a single production issue unfolds: from the first abnormal AI behavior to the evidence that reveals its root cause and the action needed to resolve it.
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Why Fortune 500 companies choose InsightFinder
Operational Savings
Model Drift Detected and Mitigated
Data Quality Issues Uncovered and Resolved
Reduction in Root Cause Analysis (RCA) Time
Deterministic IT. Probabilistic AI. Same engine.
InsightFinder’s unsupervised intelligence engine isn’t taught or configured to your system designs. It learns them—unsupervised, online, from whatever data you provide. That’s why our same technology works across traditional IT infrastructure, to Kubernetes, to cloud infrastructure, to AI agents without being rebuilt. InsightFinder’s reliability approach is data-agnostic and system-agnostic by design.
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IT Reliability
Anomaly detection, causal root cause analysis, incident prediction, and prevention across everything your environment produces: alerts, logs, metrics, and traces from any observability tool, plus infrastructure, network, and edge devices.
- Tool-agnostic: works on top of whatever you run, and keeps working when you migrate
- Augments ITSM: add root cause to tickets so operators never leave native interfaces
- Alert quality analysis: find out which alerts are redundant, noisy, or never contribute to a root cause
AI Reliability
Continuous evaluation and anomaly detection for agents in production: on every response, every prompt, every tool call. Find issues in realtime, not in a controlled lab setting.
- Multi-agent tracing: workflow monitoring with tracing across LLMs, tools, and function calls
- Evaluations: surface hallucinations, bias, safety, and toxicity—with custom evals and explanations
- Guardrails: flag and intercept sensitive data, automatically redact PII and PHI, and ensure agents stay in scope
- Real-time model and data drift detection: get drift root cause analysis, not just alerts
- Fine-tuning: generate training data directly from evaluation results and use it to improve your models
Ensuring reliable agentic operation is a cross-stack problem. You can’t tell whether an agent is failing without watching the model driving it, the data feeding it, and the infrastructure running it: all at the same time. Other tools see one layer. We analyze all of them together.
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Why our engine is different
Patented technology, not the same LLM wrapper
Three granted patents (for prediction, root cause analysis, and prevention with auto-remediation) built on 25 years of distributed-systems AI research. We purpose built this technology from scratch, this isn’t the same foundational model wrapper everyone else is using.
Unsupervised, so it finds problems nobody has seen
No rules. No thresholds. No correlation logic to configure or maintain. That matters twice over: it's why we diagnose unknown and novel problems (not just ones resembling past incidents), and it's why adding a new data source doesn't add headcount. Rules-based platforms take years of tuning. Our customers see value within days to weeks of data ingestion.
Composite AI techniques, matched to the problem
Different data types need different techniques. Most platforms just run generative AI across everything (one AI approach, often on just one LLM) and accuracy suffers as a result. We apply the right AI technology to each kind of data and problem set, then we analyze the results together.
Enterprise-ready, to meet your company’s needs
Real-time in-memory analysis, so answers are always fast. Fully packaged for cloud, on-premise, or air-gapped deployment. SOC 2 Type II and HIPAA. Reinforcement from your team's feedback, continuously training the agent on your environment.
Built for modern reliability teams
IT Reliability
Predict incidents hours ahead, get causal root cause in seconds, and prevent the failure — on top of whatever observability stack you already run.
AI Reliability
Continuous evaluation, anomaly detection, and drift root cause for agents in production: across the agent, the model, the data, and the infrastructure together.
ARI, The AI SRE Agent
Investigates predictions, gathers new evidence, explains the causal chain in your language, and takes preventive actions on your confirmation: from desktop, mobile, Slack, or Teams
See how InsightFinder helps your team deliver reliable services across every layer of the stack
Take InsightFinder AI for a no-obligation test drive. We’ll provide you with a detailed report on your outages to uncover what could have been prevented.
