InsightFinder AI

What InsightFinder AI does

InsightFinder is a reliability platform that uses composite AI techniques and observability data for end-to-end workflows, delivering high-quality AI products and digital services. The platform quickly and accurately pinpoints root causes behind system failures, detects, surfaces, and predicts performance anomalies, and reduces MTTR by up to 80% with automated intelligent insights. It detects model drift and LLM quality issues, then uses them to reinforce learning with training data that fine-tunes models. Founded by Dr. Helen Gu, InsightFinder holds foundational patents that shift large AI platforms and IT infrastructures from reactive firefighting to proactive, automated resilience. It provides a complete lifecycle platform for building, evaluating, deploying, and continuously improving AI agents, LLMs, and ML models, ensuring AI systems get smarter, safer, and more reliable over time. The platform is system-agnostic and adaptive, continuously learning and adapting to unique constraints, working with any data set from any source (metrics, logs, traces, events).

Products

• ARI: The Operational Reliability AgentAn operational AI agent that speeds up incident management, diagnoses incidents, takes remediation actions, and provides instant shift handoff summaries, root cause analysis, and comparison reporting. It integrates with existing incident response workflows and improves through production feedback loops via Composite AI techniques.
• Unified Intelligence Engine (UIE)The brain behind InsightFinder's AI Observability and IT Observability solutions, delivering real-time anomaly detection, root cause analysis, problem prediction, and auto-remediation for enterprise-scale ML and LLM models, IT infrastructures, and production applications. It leverages unsupervised online machine learning algorithms and can be integrated into open source monitoring platforms and third-party monitoring solutions.
• AI Reliability PlatformA complete lifecycle platform for building, evaluating, deploying, and continuously improving AI agents, LLMs, and ML models. It includes features for multi-agent tracing, LLM prompt comparison, model fine-tuning, LLM Labs for model testing and comparison, and an AI Gateway for deploying and governing AI models in production.
• IT Reliability PlatformAn AI-driven platform that learns systems to automatically detect anomalies, pinpoint root causes, generate remediations, and prevent incidents across traditional infrastructure, applications, and services. It uses Composite AI technologies to keep apps and infrastructure healthy before outages reach customers.

Key capabilities

• Predictive Analytics
• Anomaly Detection
• Unsupervised Machine Learning
• AIOps
• Scalable Stream Learning
• Root Cause Analysis
• Big Data Performance Tuning
• IT Operations
• Artificial Intelligence
• Distributed Networks
• Digital Transformation
• Self-healing
• Incident Prediction
• Unified Health View
• Composite AI Techniques
• System Agnostic and Adaptive AI
• Data-Agnostic Real-Time Insights
• AI-native Workflow Management
• Reinforced Learning
• Patented Technology
• Unified Platform
• Enterprise Readiness
• Model Drift Detection and Mitigation
• Data Quality Issues Uncovered and Resolved
• Reduction in Root Cause Analysis (RCA) Time
• Multi-agent tracing
• LLM Prompt Comparison
• Model Fine Tuning
• LLM Labs
• AI Gateway
• Precise Anomaly Detection
• Real-time Streaming Anomaly Detection
• Incident Prevention
• Auto-Remediation
• Log File Compression/PII Compliance
• Dependency Graph
• Real-time insight alerting
• Performance Dashboard
• Service Level Objectives (SLO)
• Secure Dedicated Tenancy
• Model Drift Monitor
• Data Quality Monitor
• LLM Evaluation
• Model Workbench
• Model Infrastructure Monitor
• Prompt Injection Monitor
• LLM Co-Pilot
• Single Sign On (SSO)
• Unlimited Data Ingestion
• Unlimited node cap

Use cases

• Delivering reliable AI and IT services in production
• Fixing issues in real-time with the ARI operational agent
• Managing traditional infrastructure or modern AI workflows
• Faster and more accurate root cause analysis
• Preventative actions and real-time remediations
• Evaluating prompts and finding best models for use cases
• Tracing multi-agent workflows in production
• Fine-tuning domain-specific SLMs
• Detecting traditional system anomalies in real-time
• Surfacing root causes with auto-remediation
• Accurately predicting and preventing issues
• Accelerating incident response
• Optimizing prompts and identifying best-fit models
• Protecting data integrity and reducing unpredictable output risk
• Capturing real-world user feedback to improve AI quality
• Pinpointing reliability issues faster and improving agent behavior
• Comparing prompt performance across multiple underlying LLMs
• Generating training datasets from real-world failures for reinforcement learning
• Testing and comparing foundational and open-source models
• Applying guardrails for bias, hallucination, safety, and other quality dimensions
• Deploying and governing AI models in production
• Routing traffic across foundational and open-source models
• Managing availability and overcoming rate limits
• Applying continuous trust and safety screenings
• Monitoring for model drift in fraud detection systems
• Ensuring high availability in AI systems
• Detecting and preventing model drift, LLM hallucinations, model data quality issues, and application/infrastructure failures
• Automating log and metric reviews to cut MTTD and MTTR
• Automating response processes to boost uptime and accuracy
• Predicting performance, availability, and scaling incidents

AI approach

InsightFinder AI leverages patented unsupervised machine learning algorithms and composite AI techniques for real-time anomaly detection, root cause analysis, and incident prediction in enterprise AI and IT systems. It builds domain-specific AI models, including fine-tuning small language models (SLMs) and evaluating LLMs, to provide AI Observability and IT Observability solutions. The platform uses a Unified Intelligence Engine (UIE) that learns autonomously and continuously adapts to environments without manual labels or thresholds. It also employs a distributed unsupervised machine learning approach for edge learning.

Tech named: Predictive Analytics, Anomaly Detection, Unsupervised Machine Learning, AIOps, Scalable Stream Learning, Root Cause Analysis, Big Data Performance Tuning, Artificial Intelligence, Composite AI, Generative AI, Reinforced Learning, Small Language Models (SLMs), Large Language Models (LLMs), Machine Learning (ML), Unified Intelligence Engine (UIE), Online Machine Learning, Multivariate Machine Learning

Industries served

• Software Development
• Financial Services
• Education Technology
• Credit Card Companies
• Investment Banking
• Cloud Monitoring
• Telecommunications
• Healthcare

What it says sets it apart

• Composite AI techniques
• Patented unsupervised machine learning algorithms
• System agnostic and adaptive AI engines
• Data-agnostic real-time insights
• AI-native workflow management
• Reinforced learning for continuous improvement
• Unified platform for reliability across the entire stack
• Enterprise readiness with compliance, traceability, and scalability
• Ability to work in SaaS or air-gapped environments
• Reduces MTTR by up to 80%
• Detects model drift and LLM quality issues
• Proactive incident prevention
• Automated root cause analysis
• Distributed unsupervised machine learning approach for cost efficiency and scalability
• SOC 2 Type II compliant
• HIPAA compliant

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.