Now seeking enterprise design partners
The intelligence layer between signals and actions
Observability tools tell teams what is happening. Optillence helps engineering teams decide what to do next — across code, configuration, infrastructure, and operational evidence.
Enterprise evidence boundary
Your engineering evidence stays within your control
Optillence is designed for customer-controlled deployment in private cloud, VPC, or on-premises environments — so sensitive repository code, production diagnostics, AWR reports, operational artifacts, and engineering context can remain within your enterprise boundary.
We are working with design partners to validate the right deployment and data-governance model for sensitive engineering evidence, including where AI inference and vector data are processed.
Optillence works alongside your observability, ITSM, and engineering platforms — without requiring sensitive evidence to be sent to an external AI service.
The challenge
Enterprises have dashboards. They still lack decisions.
Most organizations already have observability, monitoring, AIOps, and cloud dashboards. But engineering teams still spend hours connecting signals, identifying root causes, validating safe changes, and deciding what to optimize.
Alert fatigue
Teams drown in signals from observability, AIOps, and cloud tools — with little clarity on what actually matters.
Slow root cause analysis
Engineers spend hours correlating metrics, logs, traces, and change events before they can act with confidence.
Over-provisioned cloud resources
Kubernetes clusters, JVM heaps, and compute tiers are sized for worst case — driving persistent infrastructure waste.
Manual tuning decisions
Performance and cost optimizations depend on tribal knowledge, spreadsheets, and one-off runbooks instead of governed workflows.
Platform vision
From reactive operations to governed optimization
Automation can increase as confidence, policies, and approval models mature — without bypassing the humans accountable for production change.
Monitor
Ingest signals
Analyze
Cross-stack reasoning
Recommend
Ranked actions
Approve
Human-in-the-loop
Optimize
Governed execution
Governed optimization — not blind auto-remediation
The platform does not blindly change production systems. It surfaces explainable recommendations, scores confidence, enforces guardrails, and requires human approval before any action is taken.
- Explainable recommendations with confidence scoring
- Policy guardrails and safe-change validation
- Full audit trail for every recommendation
- Human approval before any remediation
Platform preview
Governed recommendations — not blind automation
Optillence ranks optimization opportunities across performance, reliability, and cost — then routes safe changes through human approval and auditable remediation workflows.
Optimization recommendation
CPU requests overprovisioned
- Resource
- booking-api
- Confidence
- 92%
- Expected savings
- ~$12.4k / year
- Risk
- Low
Finding: CPU requests set at 2 cores; p95 utilization under 35% for 14 days. Recommend 1.2 cores with HPA headroom.
Illustrative example — design partner preview. Not customer data.
Governed workflow
Recommendation
Ranked with confidence & impact
Human approval
Policy guardrails enforced
Remediation
Git MR or change ticket
Capabilities
Optimization across performance, reliability, and cost
A unified decision layer for hybrid platform teams — cloud, on-prem, and everything in between.
Cloud, on-prem, and Kubernetes optimization
Right-size workloads across Azure, AWS, GCP, and on-prem estates — Kubernetes, VMs, and bare metal. Tune requests, limits, autoscaling, and capacity policies to actual demand and SLOs.
JVM and application tuning
Analyze GC logs, thread dumps, and runtime configs to recommend safe heap, pool, and concurrency settings.
Cloud cost rightsizing
Identify over-provisioned compute, storage, and memory across cloud, on-prem, and container estates.
Root cause analysis acceleration
Correlate metrics, ML insights, and knowledge base context to shorten time-to-diagnosis.
Capacity forecasting
Predict traffic and resource demand to inform scaling decisions before incidents occur.
Guarded remediation workflows
Route approved changes through PR-based Git workflows with audit trails and rollback paths.
How it works
Three steps to governed optimization
Connect signals
Integrate metrics, logs, traces, CI/CD pipelines, and cloud platforms — plus your team's runbooks and performance knowledge.
Reason across the stack
Deterministic correlation combines service topology, deployment changes, repository evidence, runtime signals, and organizational knowledge. AI translates the resulting evidence into clear explanations, recommended actions, and investigation guidance.
Recommend with guardrails
Receive explainable actions with confidence scores, impact estimates, and approval workflows before anything reaches production.
Why Optillence
Decision intelligence — not another optimization engine
Most optimization and AIOps solutions excel at infrastructure rightsizing, cloud autoscaling, and automated tuning. Optillence addresses a different gap: how enterprises decide what to optimize, when, and with what guardrails — across the full stack.
Typical solution providers
Strong at capacity, cost, and runtime decisions — often with less depth in application behavior, release causality, and governed change workflows.
Optillence
The intelligence layer between signals and actions — how performance engineering leaders actually run optimization programs.
Example: elevated API latency
Illustrative — how decision depth differs
Typical approach
- · Scale CPU or replica count
- · Autoscale workload based on utilization
- · Apply generic JVM or pool tuning
May mask a root cause such as a release regression or missing index.
Optillence response
- · Correlate latency spike with recent release + query plan change
- · Recommend index creation — not CPU increase
- · Expected impact: ~35% latency reduction
- · Confidence: 94% · Risk: Low
- · Route through human approval before change
How Optillence fits your stack
Optillence works alongside your observability, ITSM, and engineering platforms — turning their signals, plus evidence outside those tools, into governed mitigation and remediation decisions.
| Capability | Other optimization tools | Observability platforms | Optillence |
|---|---|---|---|
| Customer-controlled deployment and evidence boundary | Often vendor-managed or cloud-first | Often SaaS or managed deployment | Designed for customer VPC, private cloud, or on-premises — validated with design partners |
| Incident evidence intake | Limited | Strong within native telemetry | Cross-stack: APM, AWR, Linux, load balancer, queues, middleware, network, code and deployment context |
| Next-best evidence guidance | Limited | Emerging | Guided requests for the artifact or metric needed to reduce uncertainty |
| Code-to-production decision trace | Moderate | Growing | Repository, deployment, runtime evidence, standards, and remediation linked in one workflow |
| Validated decision memory | Limited | Moderate | Human-approved incident outcomes and proven remediation patterns |
| Governed human approval | Moderate | Moderate | Built into the operating model — architect and SRE approval before action |
| PE / SRE / Architect expertise encoded | Limited | Limited | Performance engineering, reliability, and architecture guardrails as reusable logic |
Illustrative landscape view — strengths vary by deployment and maturity. Optillence emphasizes governed, evidence-backed decisions over unattended automation.
Decision intelligence
Not just what to change — why, with what confidence, and under what governance. Ranked decisions, not raw alerts.
Optimization knowledge graph
Reasons across application code, JVM, database, Kubernetes, cloud cost, releases, and workload patterns — not a single layer.
Enterprise governance
Confidence and risk scoring, explainability, human approval, change guardrails, and a full audit trail — built for regulated industries.
Cross-stack remediation
One engine connects symptoms to root cause to safe action — index creation, pool tuning, rightsizing, or release rollback.
Performance engineering depth
Grounded in how senior performance engineers actually work — not generic “scale CPU” heuristics.
We don't claim a better algorithm. We help enterprises govern optimization decisions the way senior performance engineers and platform teams already think — with evidence, confidence, and approval.
Design partners
Seeking enterprise design partners
We are partnering with a small group of engineering and platform teams to shape the next generation of governed optimization. Join us as a co-creator — not just a customer.
What a design-partner pilot proves in 60–90 days:
Select one high-value journey
API latency, Kubernetes rightsizing, JVM tuning, Oracle bottleneck analysis, or another diagnostic or optimization workflow your team already struggles to decide on.
Connect existing telemetry and a limited evidence set
Your current APM plus deployment history, code or configuration context, and selected operational artifacts — co-scoped with your platform and SRE teams.
Prove measurable decision value
Faster diagnosis, fewer blind changes, stronger remediation confidence, or measurable cost and performance improvement over 60–90 days.
Ideal design partners:
- Running Kubernetes, OpenShift, EKS, or AKS
- Managing JVM, API, database, or cloud performance challenges
- Looking to reduce incident resolution time and infrastructure waste
- Willing to co-create use cases over a 60–90 day pilot
Leadership
Built from enterprise performance engineering experience
Founded by a technology leader with nearly two decades of experience across Performance Engineering, SRE, Observability, Cloud Automation, DevOps, Non-Functional Testing, and enterprise-scale platform optimization.
Get in touch
Start a design partner conversation
We're looking for 3–5 enterprise teams to co-create the platform. Share your context and we'll schedule a focused discussion.