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Results

While every organization is different, disciplined modernization programs consistently deliver predictable outcomes: lower run-rate cost, clearer operational visibility, fewer preventable incidents, and teams that can move faster with confidence.

Project summary

Customer Problem Solution Financial result People result
F100 Manufacturer SolarWinds noise; missed outages; weak visibility for unique and complex services Multi‑data center clustered Prometheus architecture with self‑service automation $1M less over 3 years vs licensing and remediation costs Maintained ops headcount while scaling infrastructure 4x; team sees issues before customers call
Commercial / Enterprise IT VMware licensing increases under Broadcom; migration risk and skills gap concerns Proxmox migration pattern, enablement, and backstop with periodic health checks $100K per year reduction in licensing spend (initial small project) Team gains confidence and operational muscle without adding fragility
Startup Need to ship a customer-facing web application quickly with limited engineering capacity AI-augmented build approach with tight scope control and support model Built in 3 weeks with 2 people (vs ~3 months with 3 people without AI leverage) Small team delivers faster, learns the patterns, and sustains the system post-launch

PaaS APM (Internal and External Facing)

Multi-data center Prometheus architecture for a complex IaaS and platform environment.

Architectural diagram of a clustered Prometheus APM deployment

Problem

  • SolarWinds produced high volumes of noise with low signal.
  • Customer-impacting outages were missed or detected too late.
  • Limited visibility for unique services and complex dependencies.
  • Inconsistent alarm design and poor operational runbooks.
  • Trend and capacity data were not retained long enough to support planning.

Solution

  • Technology: Clustered Prometheus across data centers.
  • Automation: End-user onboarding and instrumentation.
  • Execution: Phased rollout to reduce risk.
  • Interfaces: Standards, escalation paths, runbooks.
  • Scale: Startup → Fortune 100 IaaS.

Results

  • Financial: ~$1M savings over 3 years.
  • People: 4× infrastructure growth with same ops team.
  • Operational: Issues detected before customers call.

Proxmox Virtualization

A migration and operating pattern to reduce virtualization run-rate cost without creating new operational risk.

Architectural diagram of the Proxmox deployment

Problem

  • Broadcom-driven VMware licensing increases and uncertainty.
  • Teams interested in alternatives but not comfortable without a backstop.
  • Migration risk: operational confidence, training, and day-2 operations.
  • Fear that cost savings could become a reliability or security liability.

Solution

  • Technology: Proxmox VE-based virtualization with a practical HA, backup, and recovery pattern.
  • Enablement: Deploy alongside your team, document operating standards, and train for day-2 operations.
  • Backstop: Provide periodic health checks and escalation support so the platform remains an asset, not a risk.
  • Execution: Start with a bounded pilot to de-risk the approach before scaling.

Results

  • Financial: Initial project reduced Broadcom-related licensing spend by ~$100K per year.
  • People: Teams gain confidence through a repeatable pattern and an experienced backstop.
  • Operational: Easily scaled and supported with enterprise class support available.

Startup Web App Build and Support

AI-augmented delivery that compresses time-to-value without sacrificing maintainability.

Architectural diagram of a clustered Prometheus APM deployment

Problem

  • Need to launch a customer-facing web app on an aggressive timeline.
  • Limited engineering bandwidth and a high opportunity cost of delays.
  • Risk of accumulating architecture debt while moving quickly.
  • Support expectations immediately after launch.

Solution

  • Approach: AI used as a build partner with disciplined scoping, review gates, and coding standards.
  • Resources: Two-person delivery team with clear ownership and rapid iteration loops.
  • Execution: Three-week build with integrated support and operational readiness.
  • Interfaces: API-first design and instrumentation baked in from day one to support stability and growth.

Results

  • Financial: Delivered in 3 weeks with 2 people (versus an estimated 3 months with 3 people without AI leverage).
  • People: The team internalized a repeatable delivery pattern and could sustain the system post-launch.
  • Operational: Ongoing operations were easily picked up by a junior admin in an hour a week.