Research & Development

Building the
next version.

Our R&D lab is where the next platform features are born. AI agents, predictive provisioning, multi-hypervisor support — we prototype, stress-test, and ship improvements that flow to every client automatically.

Methodology

How we
improve the platform.

Every feature starts as a hypothesis in our lab. We prototype against realistic workloads — thousands of tenants, concurrent orders, edge cases in billing. If it doesn't survive the lab, it doesn't reach production.

Improvements ship continuously. When we optimize provisioning speed or add AI-powered diagnostics, every client gets the upgrade automatically — no migration, no downtime.

Focus Areas

Research Domains

Strategic investigations into the future of infrastructure.

01 — 04

AI Agents for Cloud Ops

Building MCP-connected agents that monitor, diagnose, and resolve infrastructure issues autonomously. From predictive scaling to automated incident response — reducing human intervention to zero.

MCP ServerAutonomous AgentsPredictive Scaling

Provisioning Intelligence

Making provisioning smarter. ML models that predict resource requirements from order patterns, pre-warm infrastructure before demand spikes, and optimize placement across compute nodes.

Resource PredictionPre-warmingPlacement Optimization

Multi-Tenant Security

Hardening tenant isolation at every layer — network policies, namespace boundaries, storage encryption, and API-level access control. Zero-trust between tenants by default.

Tenant IsolationNetwork PoliciesRBAC

Billing & Metering Precision

Sub-second resource metering with zero data loss. Testing edge cases — what happens when a VM is created and destroyed within the same billing minute? We make sure every cent is accounted for.

Real-time MeteringUsage TrackingRevenue Assurance
Lab Activity

Active Projects

Current research initiatives in various stages of the experiment lifecycle.

Active
72%

MCP Agent for Infrastructure Diagnostics

An AI agent connected via MCP that reads OpenStack logs, correlates events, and suggests fixes — before the customer notices. Goal: 80% of L1 incidents resolved without human intervention.

Active
58%

Predictive Provisioning Engine

ML model trained on order patterns to pre-warm compute nodes before demand spikes. Reducing provisioning time from 90 seconds to near-instant for predicted workloads.

Research
35%

Multi-Hypervisor Abstraction Layer

Unified provisioning across OpenStack, Proxmox, and bare metal. One API, multiple backends — so clients aren't locked into a single hypervisor.

Validation
85%

Real-Time Revenue Dashboard

Live revenue tracking per tenant, per product, per region. Streaming aggregation from billing events with sub-second latency. Currently in stress testing with 50K simulated tenants.

Process

The Experiment Lifecycle

PHASE 01

Identify

Spot a bottleneck in the platform — slow provisioning, billing gaps, scaling limits. Define the problem with real production data.

PHASE 02

Prototype

Build a solution in our isolated lab environment. Test against realistic workloads — 1000 tenants, 10K VMs, concurrent orders.

PHASE 03

Validate

Run it against production traffic patterns. Measure latency, accuracy, resource usage. If it doesn't improve metrics, we kill it.

PHASE 04

Ship

Merge into the platform. Every improvement flows to all clients automatically — no manual upgrades, no migration.

Toolchain

Lab Instrumentation

The stack we use to build, test, and validate platform improvements. Everything runs in an isolated lab environment that mirrors production — same scale, same complexity.

Infrastructure

  • OpenStack
  • OpenShift
  • Kubernetes
  • Terraform

AI & Automation

  • Claude MCP
  • LangChain
  • Custom Agents
  • Event-driven

Observability

  • Prometheus
  • Grafana
  • OpenTelemetry
  • Custom dashboards

Testing

  • k6 load testing
  • Chaos Mesh
  • E2E provisioning
  • Billing simulation
Output

Publications & Research

Findings from our lab distilled into actionable knowledge.

WHITE PAPER

MCP Agents in Cloud Operations: Architecture & Results

Mar 2026
BENCHMARK

OpenStack Provisioning: 90 Seconds to VM — How We Got There

Feb 2026
CASE STUDY

From Zero to 500 Tenants: Scaling a Cloud Business on PLATFORMA

Jan 2026
TECHNICAL BRIEF

Event-Driven Billing: Real-Time Metering with Kafka & NestJS

Dec 2025
32

Features shipped from lab

4

Active research tracks

3w

Avg. weeks to production

90%

Automation rate target

Shape the Platform.

Have a feature request? A scaling challenge? An integration idea? Our R&D team works directly with clients to prioritize and build what matters most to your cloud business.

Talk to R&D