Cloud spend keeps rising
Costs are growing, accountability is fragmented, and no one fully trusts the numbers behind the bill.
William Shane Burrell | Platform, Cloud & AI Delivery Advisor
I help engineering leaders reduce cloud waste, modernize delivery platforms, and adopt AI-assisted development without sacrificing quality, security, or team trust.
Bring the platform, cloud, DevOps, or AI adoption problem you are trying to untangle. I help leaders find the real constraint, choose the right first move, and turn technical complexity into measurable business value.
$14M
annual cloud savings
10x
delivery improvements
25+ yrs
executive and hands-on experience
Buyer Problems
The best conversations start when the symptoms are visible, but the real constraint is still unclear.
Costs are growing, accountability is fragmented, and no one fully trusts the numbers behind the bill.
Teams have more tooling, process, and headcount, but releases still drag and handoffs create friction.
Internal platforms exist, but application teams still fight pipelines, environments, observability, and unclear ownership.
Engineers are experimenting with coding agents and LLMs, but standards, quality practices, security boundaries, and measurement lag behind.
Executives need a senior operator who can assess the architecture, delivery model, team dynamics, and business risk without getting lost in ceremony.
Consulting Offers
Start with a focused advisory engagement that turns a vague platform, cloud, or AI concern into a practical path forward.
Buyer problem
Your delivery platform exists, but teams still fight pipelines, environments, handoffs, and unclear ownership.
Practical output
A clear view of the real constraints, the highest-leverage fixes, and a practical modernization sequence your leaders can act on.
Why this fits
Built platform teams and led modernization work that produced 10x deployment velocity improvements.
Buyer problem
AI coding tools are spreading through engineering, but standards, training, quality practices, security boundaries, and measurement are unclear.
Practical output
A disciplined adoption model that helps teams use AI to improve delivery without turning code quality, security, or trust into an afterthought.
Why this fits
Piloted AI agents across engineering and architecture teams with measured 10x feature completion improvements.
Buyer problem
Cloud spend is rising, reliability risk is hard to explain, and the organization needs a clearer relationship between cost, architecture, and ownership.
Practical output
A grounded assessment of waste, migration options, operational risk, and the changes most likely to improve both cost and confidence.
Why this fits
Led platform migration work that delivered $14M in annual cost savings with zero business impact.
Situational Offer
Temporary senior leadership for platform, DevOps, architecture, modernization, or AI adoption initiatives when you need executive judgment without adding permanent headcount.
Situational Offer
Independent assessment of engineering organizations, platforms, delivery risks, modernization needs, and technical leverage for executives or investors.
Evidence
Past outcomes are not here as resume bullets. They show the kind of diagnostic and execution judgment I bring to platform, cloud, and AI advisory work.
Enterprise workloads were spread across multiple cloud providers, creating avoidable cost, operational drag, and migration risk.
Delivered $14M in annual cost savings while maintaining availability and avoiding business disruption.
Useful when cloud spend, platform ownership, and migration risk need to be turned into a practical modernization path.
A traditional hardware product line needed software-driven value to compete in newer markets.
Created a new high-margin software revenue stream and helped shift the portfolio toward cloud-enabled products.
Useful when leaders need to connect architecture, product strategy, and software-enabled business models.
Growing engineering organizations needed stronger execution systems, clearer standards, and leaders who could scale with the team.
Improved execution quality while developing technical leaders and future managers.
Useful when delivery problems are really leadership, operating model, and accountability problems.
Engineering teams needed practical AI adoption that improved delivery without lowering quality or creating unmanaged risk.
Achieved a measurable 10x improvement in feature completion time while keeping quality and security standards visible.
Useful when AI coding tools are spreading faster than governance, training, and measurement.
Traditional operations and slow release cycles were limiting delivery speed, feedback, and developer productivity.
Moved delivery from slow, batch-oriented releases toward continuous flow with 10x deployment velocity improvement.
Useful when platform teams, DevOps practices, and release systems are not creating the flow leaders expected.
Still Building
Products I have built and launched, showing that my advisory work stays connected to current shipping reality.
URL metadata extraction API for developers
api.shanecode.org
View ProductModern cloud-native Git hosting platform
quikgit.com
View ProductPremium Svelte 5 component library with Tailwind CSS
ui.shanecode.org
View ProductProduction-ready SaaS starter template
ship.shanecode.org
View ProductFree developer tools suite -- JSON, regex, UUID, and more
tools.shanecode.org
View Product5 Products. 5 Published Apps.
Hands-on product work keeps the advisory perspective grounded in current implementation reality.
View All AppsApps built from concept to store release, keeping the advisory perspective grounded in real implementation work.
Leadership perspectives and technical judgment behind the advisory work.
Leadership teams are over-focusing on branded AI tools and agent races. The real advantage comes from repeatable workflows, task-specific clients, operational leverage, and internal tooling shaped around your domain.
How AI-augmented development with expert leadership enabled building a cloud-native Git platform with enterprise features in record time.
Five hard-won insights on integrating AI tools into development workflows. Why adoption is binary, why fundamentals still matter, and why nobody will be competitive without AI.
Useful First Step
Bring the constraint, the urgency, and the outcome you need. I will help frame what is likely worth investigating first and whether an assessment or advisory engagement makes sense.
Past work includes $14M annual cloud savings, 10x delivery improvements, and AI-assisted engineering adoption across real teams.