AI Infrastructure.
Finally Intelligent.
Deploy, validate, operate and optimize AI infrastructure across cloud, Kubernetes, GPUs and MLOps from one intelligent control plane.
- Validate
- Pre-deploy contracts
- Operate
- Dependency reasoning
- Optimize
- GPU & cost intelligence
- Automate
- Policy-controlled actions
Early-stage product in active development. Product UI shown throughout is demonstration data.
The Problem
AI infrastructure is becoming more powerful — and exponentially harder to operate.
Every layer is a separate tool, a separate console, and a separate mental model. The failure modes live in between them.
Fragmented landscape
Disconnected nodes with no shared understanding of how a model, a GPU pool, a network policy and a cost budget relate to one another.
Where teams lose time
- Deployment failures
- Missing secrets
- Incorrect IAM permissions
- Broken service dependencies
- Incompatible CUDA/GPU configs
- Insufficient GPU capacity
- Over-provisioned infrastructure
- Idle GPU capacity
- Unexpected cloud costs
- Inference latency
- Scaling problems
- Configuration drift
- Networking failures
- Model/runtime incompatibility
- Multi-cloud management
Key Insight
AI workloads changed the infrastructure equation.
AI systems require infrastructure intelligence across the entire workload — not just deployment.
Traditional software
- 01Code
- 02Build
- 03Test
- 04Deploy
- 05Monitor
AI workloads
- 01Data
- 02Training
- 03Model
- 04Evaluation
- 05Model Registry
- 06Inference
- 07GPU
- 08Kubernetes
- 09API
- 10Users
Connected to everything
AI infrastructure needs more than CI/CD.
Delivery pipelines ship artifacts. AI systems need reasoning across models, GPUs, runtimes, dependencies, policy and spend.
The Product
One control plane for your entire AI infrastructure.
AECHO sits at the center — understanding relationships across every layer instead of exposing yet another dashboard.
AECHO
Control Plane
Deployment Contract Validator
Validate the full infrastructure contract before a deployment reaches an environment.
Infrastructure Intelligence
A live model of workloads, resources, configuration and dependencies.
AI Infrastructure Copilot
Ask questions in natural language and get grounded, traceable answers.
GPU & Resource Optimization
Right-size GPU pools, nodes and workloads against real utilization.
Cost Intelligence
Attribute spend to workloads, models and environments.
Dependency Graph
Understand how applications, models, data and infrastructure connect.
Observability
Telemetry correlated with configuration and topology, not isolated charts.
Security & Policy
IAM, secrets, RBAC, approvals and audit as first-class primitives.
Autonomous Remediation
Policy-controlled execution with verification and rollback.
Multi-Cloud Control
One control plane across cloud, private cloud and on-prem.
Deployment Contract Validator
Know what's broken before production does.
Before a deployment reaches an environment, AECHO validates the complete infrastructure contract it depends on.
Contract surface
- Environment variables
- Secrets
- IAM permissions
- APIs
- Database connectivity
- DNS
- Network policies
- Service endpoints
- Container image
- Kubernetes resources
- GPU availability
- GPU memory
- Model compatibility
- CUDA/runtime compatibility
- Python dependencies
- Resource requests
- Security policies
- Cost constraints
Deployment Validation
Production / inference-api
- Secrets
- IAM
- Network
- Database
- GPU capacity
- Model compatibility
- Cost threshold
Interactive product preview — demonstration data.
Infrastructure Graph
AECHO understands how everything connects.
Hover any node to highlight its dependencies. The graph is how AECHO reasons about cause and effect.
Interactive product preview — demonstration data.
Traced question
"Why is inference latency increasing?"
- 1Traffic ↑ 38%
- 2GPU utilization ↑
- 3GPU memory pressure
- 4Autoscaler unable to scale
- 5Node pool capacity exhausted
Root cause identified.
AI Infrastructure Copilot
Ask your infrastructure anything.
Answers grounded in your live dependency graph, configuration and telemetry — with the action and the risk attached.
Example prompts
- Why did my deployment fail?
- Why is inference latency increasing?
- Which GPUs are underutilized?
- How much am I spending on idle infrastructure?
- Can I safely deploy this model?
- What changed in production?
- Which resources should I scale?
- Show me the most expensive workloads.
AECHO Copilot
Deployment blocked.
The inference service cannot reach Redis because the current NetworkPolicy denies traffic on port 6379.
Recommended action: update the policy and revalidate the deployment.
Risk: Low · Rollback: Available
Interactive product preview — demonstration data.
GPU & Resource Optimization
Stop paying for idle intelligence.
AECHO analyzes utilization signals continuously and proposes right-sizing that respects workload demand and latency targets.
Signals analyzed
GPU utilization over time
Current allocation — example data
Current
8 × GPU
Utilization: 18%
Est. cost: $38,400/mo
AECHO recommendation
4 × GPU
+ autoscaling
+ workload scheduling
Projected: $26,500/mo
Illustrative estimates for product demonstration only — not customer results.
Resource optimization
inference-prod
CPU
Over-provisioned
GPU
Underutilized
Recommendation: resize workload. Expected impact: lower resource consumption.
Multi-Cloud
Your infrastructure. Your cloud. One control plane.
AECHO is built on a plugin architecture so the control plane can extend across the environments AI teams actually run in.
AWS
Azure
GCP
Private Cloud
On-Prem
Integration ecosystem
Planned integrations — not yet availableCloud
Kubernetes
Infrastructure
AI / MLOps
Observability
Data
Architecture
How AECHO is put together.
Experience, intelligence, control and connectors as distinct layers — so reasoning stays portable across environments.
AECHO Experience
Intelligence Layer
Control Plane
Connectors / Plugins
Infrastructure
Autonomous Operations — Vision
From Copilot to Autopilot.
Autonomy is a spectrum. Organizations decide how far AECHO can go, per environment and per action.
Level 1
Advisor
- Observe
- Analyze
- Recommend
Human executes.
Level 2
Copilot
- Observe
- Analyze
- Recommend
- Human approves
- AECHO executes
Human stays in the loop.
Level 3
RoadmapAutopilot
- Observe
- Reason
- Execute
- Verify
- Rollback if necessary
Policy-controlled and configurable.
Trust & Security
Automation without losing control.
AECHO doesn't blindly change production. Organizations define what AECHO can observe, recommend, approve, or execute.
Permission level
RECOMMEND
Propose changes with risk and impact attached.
Observe → Recommend → Approve → Execute → Verify
Guardrails by design
AI does not get unlimited access. Every autonomous action is scoped by policy, recorded in an audit trail, and reversible.
Knowledge Graph
From metrics to understanding.
AECHO is designed to reason across telemetry, configuration, dependencies, workloads, models, resources and policies — not just display them.
Traditional monitoring
CPU = 82%
A number without a cause. The engineer still has to reconstruct the story.
AECHO aims to explain
CPU increased because traffic increased 38%, causing additional inference requests. The autoscaler could not add capacity because the current node pool has reached its configured limit.
Telemetry + configuration + dependencies + policy = understanding
Workflow
One continuous infrastructure loop.
Connect once. AECHO keeps discovering, validating, observing and optimizing as your infrastructure changes.
Connect
Discover
Understand
Validate
Deploy
Observe
Optimize
Remediate
Verify
Before / After
Less infrastructure firefighting. More product building.
Before AECHO
- Developer
- Git
- CI/CD
- Terraform
- Kubernetes
- Cloud Console
- Monitoring
- Logs
- Manual debugging
- Incident
After AECHO
- Developer
- AECHO
- Validate
- Deploy
- Observe
- Reason
- Optimize
- Remediate
- Verify
Who it's for
Built for the companies building with AI.
A small AI team should be able to operate infrastructure with the capabilities of a much larger platform engineering organization.
AI Startup Founder
"Ship AI products without building a massive platform team."
Platform Engineer
"Manage cloud, Kubernetes and AI infrastructure from one control plane."
MLOps Engineer
"Deploy and operate models with infrastructure-aware automation."
Enterprise SRE
"Govern infrastructure changes while enabling safe automation."
Primary
Secondary
Brand
Built by infrastructure engineers. For the companies building the future.
Startup for startups.
AI startups shouldn't need enterprise-sized platform teams just to operate production infrastructure. AECHO gives small engineering teams infrastructure intelligence that scales with them.
Why now
AI infrastructure is becoming a first-class business problem.
More AI workloads reach production every quarter, and each one adds GPU, model and runtime concerns on top of already-complex cloud environments.
AI adoption
↑Directional illustration — not a measured statistic.
GPU demand
↑Directional illustration — not a measured statistic.
Infrastructure complexity
↑Directional illustration — not a measured statistic.
Cloud spend
↑Directional illustration — not a measured statistic.
Category map
Where AECHO sits.
AI workloads introduce infrastructure requirements that span models, GPUs, inference, dependencies, cost, and runtime compatibility.
CI/CD
Delivery pipelines
DevOps Platforms
Harness and similar platforms
Infrastructure as Code
Terraform, OpenTofu
Kubernetes Optimization
Cast AI and similar
MLOps
Kubeflow, MLflow
Observability
Datadog and similar
FinOps
Cloud cost tooling
AI-Native Infrastructure Operations
AECHO — infrastructure + AI workloads + autonomous operations
Interactive preview
See how AECHO reads your infrastructure.
Run a simulated analysis. AECHO connects, discovers, understands, then explains what it found — and what it would do about it.
Idle. Start an analysis to stream the reasoning trace.
Findings appear here once reasoning finishes.
Interactive product preview — demonstration data.
Roadmap
What we're building, in order.
Everything beyond Phase 1 is planned work. Nothing here is presented as already available.
Phase 1
MVPAI Deployment & Infrastructure Copilot
- Infrastructure discovery
- Deployment validation
- Dependency graph
- AI diagnosis
- Resource recommendations
- Cost recommendations
- Safe remediation
Phase 2
PlannedMulti-cloud
- Cross-cloud discovery
- Unified policy
- Cross-cloud cost intelligence
Phase 3
PlannedAI / MLOps ecosystem
- Model registries
- GPU orchestration
- Inference-aware validation
Phase 4
VisionAutonomous Infrastructure
- Observe
- Recommend
- Execute
- Verify
- Rollback
Early access
Join the AECHO waitlist.
AECHO is in early development. Early access is opening gradually to teams running AI workloads on Kubernetes. Tell us about your setup and we'll be in touch.
Get started
Build AI products. Let AECHO handle the infrastructure intelligence.
Founded by Jayesh Jadhav · work.jayeshjadhav@gmail.com