KubeSlice
Avesha Enterprise for KubeSlice
Service Connectivity Layer for managing fleet of clusters for better application performance
KubeBurst
On demand cloud capacity for datacenters
KubeTally
Multi - cluster chargeback by application and teams
KubeAccess
Service gateway for multi-cloud applications
Smart Scaler
Smart Scaler
Predictive autoscaling based on application behaviors
Smart Karpenter
Predictive autonomous scaling of pods and nodes
Elastic GPU Service
EGS
Single/Multi-cluster and multi-cloud GPU provisioning and management platform
Obliq
Obliq
Obliq adds intelligence and autonomy to Kubernetes
EGS Resources
Explore Resources for Elastic GPU Service
Analyst Reports
Navigating Key Metrics for Growth and Success
Blog
Source for Trends, Tips, and Timely Topics
Documentation
The Blueprint for Mastering Tools and Processes
Customer Case Studies
Success stories from our valued customers and partners
Why AI-Native SRE Now?
- GenAI code drift sneaks past CI gates and explodes incident volume.
- Dashboards stay reactive; alert storms bury the real root cause.
- 2 a.m. heroics still decide MTTR — engineers grind while customers wait.
↓
90%MTTR
↑
99.9SLO HIT RATE
↓
28%CLOUD SPEND*
*Average savings across early-access customers.
The Obliq Formula
Cognition
Agents reason, ask why, and diagnose intent.
Context
Telemetry is stitched with memory — prompt, tool, outcome.
Coordination
Swarms span QA, infra, data & SRE for holistic fixes.
Self-Improvement
Incidents feed RL loops; the system upgrades itself.
Core Capabilities
Autonomous Triage & RCA
Agents isolate anomalies, trace causality and surface first-fix actions instantly.
Policy-Driven Self-Healing
Declarative playbooks trigger validated rollbacks or auto-patches, no heroics required.
Model-Aware Observability
Correlate infra metrics with token latency, context-window overflows & prompt drift.
Cost & Performance Optimizer
Cross-stack insights tune replicas, burst GPU, and right-size resources automatically.
From Data → Decision → Action
- Ingest: Telemetry, traces & agent metadata flow in as contextual events.
- Pattern: Vector & graph engines detect anomalies and drift in real time.
- Decision: Reasoning agents weigh risk, cost & policy to propose fixes.
- Action: Auto‑Remediator executes or recommends; feedback loops retrain.
Role-Based Benefits
Platform Teams
Autonomous triage & root-cause traceability
Multi-agent ops coordination
Policy-based rollouts & rollbacks
AI/ML Engineers
Model-aware telemetry & RAG insights
Prompt-to-impact visibility
Agent feedback loop tracking
FinOps / CTOs
Reliability-cost modeling
Cross-stack optimization
SLA-linked failure analytics
Agent Showcase
Code-Drift Analyzer
Watches CI, PRs & GenAI commits to flag untested diffs before rollout.
Root-Cause Synthesizer
Stitches logs, traces & metrics into a causal narrative in seconds.
Auto-Remediator
Executes safe rollbacks, replica bursts or config hot-patches, then documents the fix in Slack.
MCP‑Ready, Slice‑Aware Architecture
AI Agent Layer
(Obliq from Avesha)
Adds an intelligence layer on top of Kubernetes to power autonomous operations.
Detects and resolves issues autonomously (e.g., self-healing, noisy neighbor mitigation)
Enforces smart routing for compliance and performance
Surfaces observability-driven enforcement (cost, resource abuse, etc.)
Recommends actions based on policies, usage patterns, and behavior
Powers model-aware infrastructure for future GPU workloads (EGS readiness)
Automation Layer
(Current/Traditional Stack)
Executes zero-touch automation across your Kubernetes environments.
Zero-touch namespace and slice creation
Policy-as-code + RBAC integration with existing identity providers
Automated provisioning of services (DNS, Git, Redis, etc.)
Time-bound access controls and audit hooks for internal compliance
Seamless multi-cluster scaling
Obliq + Automation = Our Differentiator
Together, they enable truly autonomous cloud-native operation
Obliq brings actionability and intelligence.
Automation delivers speed and consistency.
Architecture Overview
Obliq sits on top of the Avesha platform, delivering autonomous monitoring, intelligent decision-making, and self-healing across
Obliq Workflow
Data Collection
Continuously collects metrics, logs, and events across Kubernetes clusters. Focus areas include:
- CPU, memory, performance
- Access patterns and latency
- Slice health and network traffic
- Resource utilization and cost
Pattern Recognition
AI engine analyzes data to:
- Establish performance baselines
- Detect policy violations or threats
- Forecast usage patterns
- Identify optimization zones
- Correlate patterns across environments
Decision-Making & Enforcement
Based on real-time insights:
- Enforces custom SLAs and policies
- Calculates risk scores
- Automates slice optimization and node actions
- Learns from prior decisions to improve outcomes
Autonomous Actions
Executes real-time decisions such as:
- Healing degraded slices
- Rebalancing workloads
- Improving performance
- Scaling resources
- Reducing costs through smart placement
