Proactively manage SLAs of digital services and minimize SLA breaches.
Respond quickly to critical incidents with automated root cause analysis.
Generate continuous actionable insights about performance bottlenecks.
Platform and SRE leaders are trapped between two fires: software engineering teams shipping code
faster than observability can keep up, and executive leadership demanding strict SLA compliance without
exploding cloud bills.
OccamsHub transforms your platform team from reactive dashboard maintainers into proactive reliability
architects. By embedding our SRE Copilot directly into a native OpenTelemetry core, OccamsHub
automates the tedious, manual overhead of SRE—allowing a single team to govern reliability across
thousands of microservices effortlessly.
Stop spending months manually defining endpoints and tuning brittle thresholds.
Setting up and maintaining SLOs for 100+ microservices manually consumes over 2,000 engineering hours in setup and up to 2 FTEs in perpetual maintenance overhead.
Automatically stitches raw telemetry into business-critical journeys (e.g., Checkout_Flow, User_Auth) and calculates dynamic error budget baselines based on statistical simulations of failures. Out of the box.
Eliminate 99%+ of manual SLO setup time and deploy full journey-level reliability governance across your entire microservice fleet in minutes, not months.
Get zero-touch visibility into your evolving service topology.
Modern deployment pipelines introduce dozens of new microservice dependencies, database calls, and third-party APIs daily—rendering static architecture diagrams and SLOs obsolete within hours.
Combines OpenTelemetry semantic conventions to dynamically map service dependencies and detect circular calls, hidden bottlenecks, and architectural anti-patterns the moment new code deploys.
Achieve continuous topology accuracy with zero manual configuration files, custom tagging rules, or outdated Wiki diagrams.
Fix performance degradation long before static global thresholds breach.
Legacy APM alerts fire after a service breaks, forcing SREs into chaotic P1 war rooms while customer SLAs burn out in real time.
OccamsHub continuously monitors error budget burn velocity across dynamic high-cardinality tags (k8s_pod, customer_tier, region), calculating multi-window burn rates (e.g., 1h vs 6h velocity) to forecast cascading failures before users are impacted.
Reduce P1/P2 incident volume by up to 40% and reduce Mean time to detect (MTTD) by catching performance degradation during early micro-anomaly stages.
On-call shouldn’t mean drowning in alert noise, hunting through endless log tabs at 2AM, or getting dragged into a 20-person bloated war room. Every minute spent context-switching between disjointed dashboards burns your SLAs and your team’s sanity.
OccamsHub replaces chaotic firefighting with human-in-the-loop automation. Powered by the Occams Context Engine, Ops Copilot runs directly on raw OpenTelemetry streams to investigate alerts, drill into live correlations, and deliver evidence-backed RCA in seconds—enabling engineers to validate fixes and generate post-mortems in minutes, not hours.
Pinpoint the exact code commit, feature flag, or config shift in seconds.
Finding the root cause usually requires jumping between Datadog metrics, Splunk logs, AWS CloudWatch, and GitHub commit histories to manually piece together a timeline.
Ops Copilot continuously computes real-time statistical correlations across streaming telemetry—linking metric spikes directly to failing trace spans, error log snippets, recent code commits, and feature flag toggles.
Cut Mean Time to Isolate (MTTI) from 45 minutes to sub-seconds, giving you deterministic proof of what broke.
Turn 200 microservice alerts into a single, cohesive incident timeline.
When a core service fails, downstream microservices trigger an avalanche of secondary alerts—flooding Slack and PagerDuty with noise that hides the initial trigger.
Automatically groups cascading alerts across your microservices in real time into a unified incident context window, deduplicating repetitive log errors and filtering out non-actionable background noise.
Reduce alert noise by up to 85%, giving on-call engineers an immediate, clutter-free view of what actually started the fire.
Get step-by-step fix recommendations backed by verifiable telemetry proof.
Generic AI chatbots hallucinate or offer vague advice like “check your database,” while legacy APMs leave you to write complex ad-hoc queries under pressure.
Delivers actionable, step-by-step diagnostic and remediation guidance embedded with auditable proof—surfacing the exact trace paths, metric anomalies, and log lines that validate the diagnosis.
Reduce lower Mean Time to Resolution (MTTR) while eliminating guesswork and dangerous, unverified trial-and-error fixes in production.
Automate post-mortems and transform historical incident resolutions into continuous training data.
SREs spend hours writing manual post-mortems after an incident, while historical fix knowledge remains trapped in private Slack messages or outdated Wiki pages.
Automatically generates post-mortems (capturing exact timeline, root cause, blast radius, and recovery steps) while learning from past resolutions and runbooks to inform future investigations.
Eliminate cross-team war room overhead and empower junior or secondary on-call engineers to resolve complex issues.
Product leaders shouldn’t have to wait for churned customers or tickets to discover that a critical user flow is broken. In microservice architectures, technical latency spikes, database locks, and silent API failures directly degrade user conversion and retention—yet traditional monitoring hides these issues behind obscure host metrics and pod names.
OccamsHub transforms raw telemetry into business-centric product journey SLOs. By automatically mapping technical trace flows to real journeys (Checkout_Flow, Onboarding_Funnel, Payment_Auth), OccamsHub gives product teams real-time visibility into feature health, conversion risk, and error budget impact without requiring engineering to build custom tracking dashboards.
Stop tracking disconnected server metrics. Start monitoring real product journeys.
Engineers talk in latency percentiles and pod health; product leaders talk in conversion rates and user retention. When an incident occurs, product teams struggle to answer: “Which user flows are currently broken, and how many customers are affected?”
Automatically stitches OpenTelemetry trace spans into end-to-end product journeys (Checkout_Flow, Subscription_Upgrade, Search_Execution), displaying live performance, throughput, and error rates at the business-journey level.
Instant cross-functional alignment during outages with real-time, business-readable visibility into user impact and SLA compliance.
Make data-driven decisions on when to ship new features vs. pay down technical debt.
Feature launches often stall due to friction between Product (wanting to ship fast) and SRE (wanting to freeze deployments due to instability). Without objective metrics, decisions become political.
Auto-generates dynamic, zero-config SLO error budgets for every product journey. Tracks burn velocity in real time so product leaders know exactly how much “reliability risk budget” is available before shipping major code releases.
Faster, safer release velocity backed by objective mathematical error budgets rather than subjective arguments.
Get immediate, natural-language answers about feature health without filing Jira tickets.
Product leaders frequently have to ask data or platform engineers to run custom queries to check if a recent deployment impacted API performance or user flow latency.
Equips product leaders with a read-only, domain-aware Q&A interface. Ask plain-English questions like “How is the checkout journey performing compared to last week’s baseline?” or “Did the latest release impact latency for Enterprise tier customers?”
Eliminate engineering interrupt cycles and empower product teams to self-serve operational insights in seconds.