The agentic AI platform for
proactive cloud operations
Engineered for the world’s most demanding architectures
OccamsHub platform
Built on a native streaming core, a high-performance Context Engine, and human-in-the-loop intelligence, OccamsHub converts high-cardinality telemetry into structured context—powering specialized copilots that automate reliability workflows.
Occams Context Engine
The real-time, stateful “digital twin” of your distributed system.
Built as a dynamic multi-dimensional context graph, it layers live state, temporal changes, simulated failure paths, and human feedback on top of traditional topology—giving OccamsHub Copilots the exact, real-time environment model required to prevent hallucinations.
Multi-dimensional context graph
Transforms real-time telemetry into stateful operational context.
- Topological dependency and failure mapping
Dynamically maps service topologies, traces failure propagation paths across microservices, and maintains system state in real time. - Performance simulations
Runs simulations to model end-to-end journey performance under synthetic latency, component degradation, or failure conditions. - Closed-loop domain learning
Captures engineer validations to continuously train models on your specific architecture.
High-throughput streaming and OLAP Core
The real-time data engine powering the Occams Context Engine.
- Instant high-cardinality queries
Enables OccamsHub Copilots to scan billions of raw logs, metrics, and trace spans instantly without indexing caps. - Up to 10x storage efficiency
Reduces telemetry storage using advanced columnar compression, passing the savings directly back to you. - Zero-index overhead
Eliminates legacy log-indexing tax, allowing you to retain and query full-fidelity raw events without exponential license fees.
AI reasoning engine
Context-aware orchestration and narrative synthesis.
- Deterministically anchored RCA
Translates pre-correlated context objects into human-readable root cause narratives backed by auditable trace links and metric attribution. - Guided playbooks
Maps live incident context to recommend step-by-step diagnostic workflows. - Token efficiency
Operates over pre-structured context objects rather than raw log dumps, reducing LLM token consumption and API latency by over 90%.
Enterprise security & governance guardrails
Trust-first AI architecture engineered for strict enterprise compliance.
- Zero LLM data retention
Raw telemetry and proprietary system context remain 100% private and within isolated tenant boundaries. Your operational data is never used to train public foundational models. - Strict read-only guardrails
Copilots operate as diagnostic and analytical assistants with zero production write risk and full Role-Based Access Control (RBAC). - Enterprise compliant
SOC 2 Type II compliant architecture built to satisfy strict corporate governance and regulatory requirements.
Why context matters
You cannot simply feed millions of raw log lines into an LLM at once—it is too slow, prohibitively expensive, and causes the AI to hallucinate. OccamsHub Context Engine decouples heavy quantitative computation from the LLM reasoning layer, preventing hallucinations and allowing OccamsHub Copilots to drive proactive cloud operations.
SRE Copilot
Production failure shouldn’t be your first alert.
SRE Copilot acts as an automated reliability engineer embedded in your stack. It continuously reasons across service topologies, tracks journeys, detects architecture drift, enforces error budget health, and predicts cascading failures before end users are impacted.
Automated SLOs
Skip dashboard building and manual threshold tuning with zero-config SLOs.
- Zero-config error budgets
Automatically derives dynamic Service Level Objectives (SLOs) and error budgets by accounting for all service dependencies and running performance simulations. This eliminates manual endpoint configuration and threshold tuning entirely. - Architectural drift detection
Automatically detects architectural drift across evolving microservices and triggers automated SLO update workflows to keep reliability targets aligned with production reality. - Multi-window burn velocity alerting
Monitors journey error budget burn velocity in real time, triggering multi-window multi-burn-rate alerts before a critical path breaches its SLA.
Zero-touch topology mapping
Get total visibility into your architecture without writing a single line of config.
- Automatic discovery
Ingests OpenTelemetry semantic conventions out of the box to dynamically map service dependencies, database connections, and API boundaries. - Tracking product journeys
Automatically analyzes distributed trace flows and logs into journeys (e.g. checkout_flow, identity_auth). - Performance bottleneck discovery
Navigates topological dependencies in real time to isolate performance bottlenecks across endpoints, underlying services, and critical journeys.
Outage forecasting
Fix performance degradation before it turns into a P1 fire drill.
- High-cardinality anomaly detection
Scans raw telemetry for subtle micro-anomalies across dynamic tags (k8s_pod, customer_tier, region) long before static global thresholds breach. - Pre-outage issue detection
Continuously forecasts performance signals across cascading dependencies, proactively flagging service degradation issues before they cause an outage. - Proactive prevention guardrails
Recommends architectural adjustments and capacity tweaks before error budgets burn out.
Drive reliability as a feature
Legacy APMs force engineers to spend weeks building static dashboards and tuning threshold alerts that only trigger after an outage begins. SRE Copilot leverages native OTel streams to automatically map your system, baseline SLOs, and predict failures before your customers ever notice.
Ops Copilot
Eliminate P1 fire drills
Ops Copilot acts as an automated incident responder embedded in your stack—continuously correlating telemetry in real time, isolating the exact root cause in seconds, and delivering deterministic execution plans so your team recovers instantly.
Automatic root cause analysis (RCA)
Pinpoint the exact code commit or infrastructure shift causing the outage.
- Automated context-aware triage
Analyzes incidents in seconds by cross-referencing golden signals, statistical correlations, anomalies, and live telemetry with recent code commits, feature flags, and infrastructure deployments to surface immediate root cause insights. - Automated blast-radius analysis
Performs drill-downs across high-cardinality dynamic tags—filtering by container_id, customer_tier, or region to surface immediate blast-radius insights. - Noise suppression & deduplication
Consolidates hundreds of cascading alerts across microservices into a single, cohesive incident timeline, reducing alert fatigue.
Human-in-the-loop remediation
Get immediate, evidence-backed diagnostic answers without chasing red herrings.
- Actionable remediation guidance
Generates step-by-step, precise fix recommendations—giving engineers the exact context needed to resolve issues quickly. - Auditable attribution
Provides transparent, verifiable reasoning alongside every diagnosis, surfacing the exact log snippets, trace paths, and metric anomalies that led to the conclusion. - Seamless workflow integration
Surfaces diagnostic summaries and recommended action plans directly in your incident response tools where your engineers already collaborate.
Systematize tribal knowledge
Turn chaotic firefighting into repeatable institutional intelligence.
- Automated post-mortem generation
Instantly compiles comprehensive, accurate incident summaries—including timeline, root cause, blast radius, and recovery actions. - Tribal knowledge ingestion
Learns from historical incident resolutions and runbooks to continuously improve future correlations and root-cause recommendations. - Escalation path reduction
Eliminates cross-team war rooms by providing engineers with instant, clear context across every layer of the stack.
Prevent hallucinations in your RCA
Generic AI SRE agents guess what went wrong based on text summaries. Ops Copilot queries raw, high-cardinality telemetry in real time—delivering deterministic proof, and actionable remediation before your SLA burns out.
Custom Copilots
Drive operational intelligence powered by specialized agents with real-time, domain-aware Occams Context Engine.
Developed in direct collaboration with world-class platform engineering teams, OccamsHub gives you the exact context engine needed to build, backtest, and deploy domain-specific custom copilots and AI agents tailored to your unique operational stack.
Multi-source context ingestion
Fuse live streaming analytics, ML model outputs, and custom signals into unified AI context.
- Streaming statistical correlation matrices
Continuously compute real-time statistical correlations across streaming analytics views—feeding exact cross-service dependency math directly into AI context windows. - Custom ML model training
Trains outlier and forecast models on aggregated metrics across high-cardinality dimensions or complex Spark Sql analytical pipelines. - Custom SQL & analytical views
Expose bespoke operational queries, FinOps cost metrics, or business-specific KPIs running on your raw telemetry directly to custom investigative agents.
Contextual tool-assisted Q&A
Query your operational data alongside runbooks, historical post-mortems, and live correlations.
- Connected operational tools
Equips custom agents with read-only tools to fetch correlations across historical RCAs, incident reports, architecture bottlenecks, and performance simulations alongside live telemetry. - Domain-aware investigative Q&A
Enable engineers to ask complex, natural-language questions about system state, failure modes, and past incident patterns with zero risk to production. - Strict read-only safety guardrails
Custom Copilots operate with zero production write access—functioning purely as diagnostic and analytical assistants to ensure complete operational safety.
Open MCP server
- Native Model Context Protocol (MCP)
Serve real-time telemetry, statistical matrices, and context graphs directly to external developer tools, Cursor IDEs, Claude Code, or internal Slack bots. - Event-driven trigger loops
Automatically initiate custom diagnostic investigations via triggers, or custom alert engines. - Immutable auditability
Maintain complete enterprise compliance with full audit logs tracking every context query, historical document retrieval, and diagnostic answer.
Deep operational intelligence with zero write risk
You cannot simply feed millions of raw log lines into an LLM at once—it is too slow, too expensive, and causes the AI to lose focus or hallucinate. OccamsHub features an open, high-throughput AI Context Engine that continuously processes, correlates, and structures live telemetry streams into deterministic context—allowing platform teams to deploy specialized read-only Copilots to answer complex operational questions.
AI-native Observability
Full-stack visibility. Eliminate cardinality tax.
Stop paying millions just to store log noise. OccamsHub embeds native OpenTelemetry ingestion directly into the AI runtime layer—unifying metrics, logs, and traces while slashing your legacy APM bills by up to 60% through columnar OLAP storage efficiency.
Automated trace-to-log correlation at scale
Stop hopping between disconnected dashboards during an active incident.
- Real-time span linking
Leverages native OpenTelemetry trace_id and span_id conventions to instantly link metric spikes directly to failing trace spans and downstream error logs. - Automatic dimensional correlations
Dynamically discovers and navigates relationships between trace errors and log events across high-cardinality dimension spaces. - Smart fuzzy entity resolution
Uses intelligent algorithms to resolve variations in service naming conventions, overcoming static entity mapping limitations to surface related logs automatically.
Native OpenTelemetry (OTel) pipeline
- Zero code rewrites
Point your existing OpenTelemetry collectors directly to OccamsHub in minutes. No custom SDKs, proprietary agents, or vendor lock-in. - Unified logs, metrics & traces
Ingest all three pillars of observability into a single runtime engine—eliminating data silos and disjointed monitoring tools. - Unthrottled streaming
Streams uncompressed telemetry directly to our Occams Context Engine with zero third-party API lag, rate limits, or query timeouts.
Elimination of the high-cardinality tax
Tag, query, and debug without watching your vendor bill explode.
- Zero tag penalties
Add dynamic metadata (user_id, container_id, git_commit_sha, customer_tier) without getting hit with exponential custom metric charges. - Interactive dimensional drill-downs
Seamlessly drill down into golden signals across any combination of dimensions to instantly isolate anomalous behavior and outliers. - Predictable platform pricing
Replace complex host taxes, log rehydration surcharges, and dynamic metric fees with transparent, flat-rate pricing.
Custom analytics & ad-hoc SQL
- Search across logs, metrics, and traces
Search across custom dimensions and attributes using standard Apache Lucene query syntax without learning proprietary query languages. - Logs-and-traces-to-metrics conversion
Compute custom metrics and statistical aggregations on raw log and trace streams using ad-hoc queries. - Bespoke analytics extensibility
Build operational views and custom dashboards directly on top of raw runtime streams using Spark SQL without legacy APM indexing taxes or artificial query limits.
Eliminate vendor markup
Legacy APMs charge you three times: once to ingest raw noise, once to store it, and once to query it. OccamsHub embeds OpenTelemetry natively into the AI execution engine—giving SRE and Ops Copilots instant access to full-fidelity, high-cardinality telemetry while completely eliminating the vendor markup.