28+
Years delivering software
Modernize complex software systems, stabilize delivery, and deploy AI into real operations.
Shawn Lawyer helps engineering organizations modernize legacy platforms, improve deployment reliability, and operationalize AI systems with production-grade controls.
Bring the modernization constraint, delivery instability, or AI initiative that needs a structured implementation path.
Software + AI
Core focus
Hands-on consulting
Work style
Remote + confidential
Engagements
Where engagements usually begin
Most engagements begin with a specific operational or delivery constraint.
Most engagements start with modernization complexity, unreliable delivery systems, or initiatives that need stronger architectural structure before scaling further.
Platform modernization
Modernize critical software platforms
Reduce architectural complexity, lower migration risk, and sequence modernization work in stages engineering teams can realistically execute.
Delivery reliability
Stabilize deployments and improve release execution
Improve deployment reliability, rollback readiness, and operational consistency around software delivery.
Production AI implementation
Move AI systems into controlled production use
Define operational guardrails, rollout sequencing, and production controls for AI systems used in live environments.
Technical leadership
Add senior architectural leadership during high-risk delivery phases
Provide architectural guidance for modernization sequencing, execution planning, and delivery-critical technical decisions.
Selected work
Examples of modernization, delivery, and production AI engagements.
These case studies reflect the operational and technical outcomes clients bring Shawn in to help achieve.
Legacy Modernization Under NDA Constraints
Led a phased modernization effort for a legacy platform while maintaining operational continuity and protecting sensitive systems.
- 42% reduction in high-severity incident volume
- 33% faster feature delivery in migrated domains
ML Observability Rollout for Workflow Automation
Implemented observability, operational controls, and inference cost visibility for AI-powered workflow automation.
- 29% improvement in task completion quality
- 34% reduction in inference spend per completed workflow
CI/CD Acceleration for a Regulated Product Team
Improved deployment reliability and release cadence for a regulated SaaS platform while preserving compliance controls.
- 47% faster lead time from commit to production
- 61% reduction in deployment failure rate
How engagements operate
Structured execution, direct technical communication, and measurable delivery progress.
Step 01
Assess the operational constraint
Identify the delivery risk, architectural bottleneck, or operational constraint driving the engagement.
Step 02
Define the implementation approach
Align the engagement structure to the technical scope, delivery risk, and operational constraints.
Step 03
Execute alongside engineering teams
Keep execution aligned with production realities, operational constraints, and delivery priorities.
Step 04
Leave with a defined execution path
Finish with implementation sequencing, delivery structure, or operational work already in progress.
Client feedback
What clients say about the work.
“Shawn brought clear modernization sequencing to a difficult platform transition and gave our team an execution plan we could apply immediately.”
Architecture modernization engagement
Outcome: The team reduced deployment risk while maintaining active product delivery.
“We went from inconsistent deployments to reliable weekly releases in under two months.”
DevOps and CI/CD improvement
Outcome: The team increased deployment frequency with stronger operational reliability.
“The AI implementation plan was measurable, operationally grounded, and production-ready from the start.”
Production AI implementation
Outcome: The team launched production workflows with measurable quality and cost controls.
Writing
Writing on software delivery, modernization, and production AI systems.
Articles covering architecture decisions, delivery reliability, and operational AI systems.
CI/CD Without Drama
How to improve deployment reliability through stronger validation controls, operational visibility, and rollback strategy.
Read articleShipping AI Safely in Production
How to move AI systems from prototype environments into production operations with measurable quality, cost, and governance controls.
Read articleDiscuss the engagement
Describe the delivery or modernization constraint.
If you are dealing with delivery instability, modernization sequencing, or production AI implementation challenges, send the details and we can define the next technical step.