Autonomous
Site Reliability Engineering

Sedai.io is an end-to-end autonomous site reliability engineering platform that is reshaping the future of resolving infrastructure issues with Artificial Intelligence.

Our products

We offer a spectrum of products that work in unison to ensure a highly efficient, reliable infrastructure and help your team deliver better at their most important work - innovation. No more managing alerts!

Sedai Falcon

Autonomous availability management.

Sedai Eagle

Autonomous efficiency management.

Sedai Hawk

Autonomous SiteOps.

Efficiency and availability on scale

Get to know more about our products!

Automatic remediation workflows

System identifies effective remediation workflows based on the input drift signals by the interfaced agents. Configurations are provided to automatically execute workflows or guided by an operator.

Safe remediation strategies

Remediation workflows are executed autonomously ensuring site stability pre and post actions. Also availability parameter thresholds are maintained while executing these actions.

Continuous learning

Progressively learns the optimal actions for situations in which an action plan results in delayed or no recovery. The system monitors the pre and post state transitions relative to the executed remediation workflows and updates itself for future scenarios.

Avoid redundant remediations

Synthesized remediation worfklows also take into account to eliminate redundant actions by advancing actions. Operators are finally involved when no improvements are observed post the workflow executions.

Rogue boxes detection

Idenitfy rogue boxes sitting in your application cluster hurting the end user experience. Data shows that it takes around 30-40 mins for on-call engineers to identify these machines in a vast heap of computes in a topology. Reduces MTTD drastically.

Dead box detection

Certain scenarios involve in the underlying infrastructure issues killing or isolating compute units without the knowledge of load balancer resulting in failed transacations. These scenarios are automatically identified by Delta agents.

Root cause analysis

Reduce MTTD and MTTR by quickly identifying application level metric drifts that hurts your end user experience drastically. With the topology data available, our proprietary algorithms identify the metric progressions over time to establish a pattern and identify the underlying root cause for the cause of drift.

Capacity drift correction

Ensuring consistency with application and zone specific capacity requirements with a declarative model.

Resource parity

Ensure capacity corrections for applications sitting across zones with an established strategy for disaster recovery.

Compute throughput calculation

Quanitfy the unit compute throughput of all applications in production with no errors with our canary workflow. With the help of machine learning algorithms, we continually model the compute characteristics during workflows to avoid live impacts.

Hybrid autoscaling

An ensemble approach of proactive and reactive methods of autoscaling working in addition to the quantified throughputs. We ensure the systems are geared to reflect to unexpected surges and modelled changes in the traffic demand.

High velocity on-scale SiteOps

Assists operators to execute actions for the entire fleet of computes in your infrastructure(on scale). Massively increases throughput of actions.

Granular visibility

Intuitive dashboards to monitor workflow progress and config controls.