InsightFinder

InsightFinder is an AI-driven reliability platform that monitors models, data pipelines and infrastructure together, using unsupervised machine learning to detect anomalies, find root causes and predict incidents before they occur. It is built for enterprise SRE and AI platform teams who need to know why an agent or model failed across the whole stack, not just that a metric moved. It overlays an existing observability estate rather than replacing it.

InsightFinder AI, headquartered in Durham, North Carolina and founded in 2016 by Dr Helen Gu — a North Carolina State University computer science professor previously at IBM and Google — builds a reliability platform spanning both AI systems and conventional IT infrastructure. Its core is patented unsupervised machine learning applied to metrics, logs, traces and events, which detects anomalies without preset thresholds, performs root cause analysis, predicts incidents hours ahead and generates remediation playbooks. The platform splits into two lines: AI Reliability, covering prompt evaluation, model selection, multi-agent workflow tracing, hallucination, safety and bias monitoring, drift detection and domain-specific fine-tuning of small language models; and IT Reliability, covering streaming anomaly detection, root cause analysis, incident prediction and automated remediation across infrastructure and applications. An operational agent, ARI (Autonomous Reliability Insights), surfaces evidence-grounded findings to shorten incident cycles and ships with a mobile version for on-call response. The company deliberately positions as system-agnostic with no rip-and-replace: it ingests from existing tooling, and maintains a listed Datadog integration that streams metric and event data into its Unified Intelligence Engine, plus a documented Dynatrace pairing. InsightFinder raised a $15 million Series B led by Yu Galaxy in April 2026, bringing total funding to $35 million, on the back of more than threefold revenue growth and a seven-figure Fortune 50 deal closed within three months. Named customers include UBS, NBCUniversal, Dell, Lenovo, Comcast, FedEx, Visa, TD Bank and Google Cloud. It is SOC 2 Type II and HIPAA compliant, and available as SaaS or air-gapped deployment.

Category

Infrastructure & Cloud

Pricing

Usage-based, Subscription, Contact for pricing

Target Market

CIOs, CTOs, SRE Leaders, Heads of AI, IT Operations Directors

Deployment

Cloud-first, Self-hosted, Hybrid, API-based

Founded

2016

Headquarters

Durham, North Carolina, USA

Team Size

11-50

Customers

Fortune 500 customers including UBS, NBCUniversal, Dell, Lenovo, Comcast, FedEx, Visa, TD Bank and Google Cloud; total count not disclosed

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  Threshold-less unsupervised anomaly detection

  Patented unsupervised ML flags anomalies across metrics, logs, traces and events without preset thresholds, removing the tuning burden that breaks at scale

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  Incident prediction ahead of impact

  Predicts incidents hours before they occur rather than alerting after degradation, which is the difference between prevention and faster firefighting

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  Multi-agent workflow tracing

  Traces multi-step agent executions so failures can be attributed to a specific step, model call or data dependency rather than the system as a whole

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  LLM hallucination, safety and bias monitoring

  Real-time evaluation of model outputs in production alongside drift detection, covering risks conventional infrastructure monitoring has no view of

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  ARI autonomous reliability agent

  An operational agent that shortens incident cycles with evidence-grounded insights, with a mobile edition so on-call engineers can respond from anywhere

• ✓
  System-agnostic ingestion

  Ingests from existing observability tooling with a listed Datadog integration and documented Dynatrace pairing, so adoption needs no rip-and-replace

• ✓
  Fine-tuned small language models

  Customises compact domain-specific models to a customer's business context, keeping evaluation and remediation cost low relative to frontier-model approaches

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  Preventing revenue-impacting model drift

  Proactive drift detection catches a degrading production model before its predictions start costing money, rather than after a quarterly review

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  Root cause across the full AI stack

  When an agent misbehaves, correlated model, data pipeline and infrastructure telemetry identifies whether the fault is prompt, data or capacity

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  Layering prediction onto Datadog

  Existing Datadog users stream metrics and events into the Unified Intelligence Engine to gain incident prediction without changing their instrumentation

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  Air-gapped reliability monitoring

  Regulated customers run the platform inside a controlled boundary where SaaS observability vendors cannot be granted telemetry access

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  On-call triage from mobile

  ARI Mobile gives on-call engineers evidence-grounded incident context and remediation guidance without needing to reach a laptop first

Unusually for this category, InsightFinder publishes list prices. AI Observability meters per trace at $0.001 after a 30-day trial, capped at 10 models on Growth; IT Observability meters per CPU core at $2.50/core/month with a $250/month floor and unlimited data ingestion — the inverse of ingest-based competitors, so high-volume logging does not inflate the bill. Both Enterprise tiers are quote-only and gate what large deployments need: unlimited models and integrations, on-premises or dedicated tenancy, custom roles, the UIE SDK and SLOs. Budget for the Enterprise tier, since Growth's 10-model and 10-integration caps bind quickly at enterprise scale.

This page was written from 6 sources, 3 on domains other than insightfinder.com.

1. 1.insightfinder.com — insightfinder.comvendor
2. 2.insightfinder.com — insightfinder pricingvendor
3. 3.techcrunch.com — insightfinder raises 15m to help companies figure out where
4. 4.prweb.com — insightfinder ai launches holistic ai observability platform
5. 5.docs.datadoghq.com — insightfinder insightfinder
6. 6.insightfinder.com — about our missionvendor