Security
8/10
Auth foundation is solid and RLS policies are well-scoped. One finding: payment entitlement writes should be wrapped atomically, and a single stale secret reference needs rotation.
passWeight x1.5
AI can get you to a prototype. Engineering keeps it from falling apart.
I stabilize the foundation and walk you through every change I make. You get a production-ready app and the skills to keep building without breaking things.
Are you at the point where you fix one bug and two more show up? Maybe it runs fine locally and then falls over the moment a real user touches it. So you patch it and then something else breaks over and over again. The unfortunate truth is you're not fixing the app. You're managing the symptoms of a codebase that was never built to hold weight.
The problem is every patch is only temporary and the underlying issues are never addressed. Often what's underneath is bad infrastructure, poor database management, and mounting workarounds and shortcuts. Moving fast without proper engineering fundamentals leaves you with cruft that compounds every time you ship.
You need an engineer to come in and identify the production risks underneath the surface, stabilize the foundation, and hand you back an app you can keep building on. I'll walk you through exactly what I did and why. You'll leave with a stable production-ready app and the skills to keep it that way.
Fixed scope. Fixed price. A stable app and the skills to keep it that way.
6+ Years Experience · Deloitte · WWT · Pluralsight
Is This For You?
Built for founders and builders who already have a working app.
This is for you if:
- ✓You built an app with Cursor, Claude, Lovable, Bolt, Replit, or v0.
- ✓The app works, but you do not fully trust it.
- ✓You are about to launch, onboard users, or accept payments.
- ✓Every new feature seems to break something.
- ✓You need senior engineering help but not a full-time hire.
This is not for you if:
- ✗You only have an idea.
- ✗You want a full app built from scratch.
- ✗You want the cheapest possible developer.
- ✗You need unlimited feature work.
- ✗You are not willing to clean up the foundation before scaling.
Free Production Risk Audit
Start with a free Production Risk Audit.
Send me your repo. I'll review the parts of your AI-built app most likely to fail in production: deployment, database design, auth boundaries, error handling, logging, monitoring, environment configuration, and code structure.
What the audit checks
- →Deployment and environments
- →Database schema and migrations
- →Auth/session boundaries
- →Error handling and validation
- →Logging and monitoring
- →Secrets and configuration
- →API boundaries
- →Code structure and maintainability
What you'll receive
- ✓Production Readiness Score
- ✓Top 5 risks
- ✓Severity rating for each risk
- ✓Business impact explanation per risk
- ✓Fixed-price quote to stabilize the app
Paid engagement
- ○Full findings list with line numbers
- ○Remediation path
- ○Recommended stack implementation
Get Your Free Production Risk Audit
No commitment. No charge.
Sample Output
What you'll get back.
Every engagement includes a scored report across 9 engineering areas
pass — With recommendations7.1 of 10Sample · Redacted
Architecture
8/10
Clean service boundaries and a sensible request shape for the product stage. A handful of read paths are unbounded under load — worth addressing before scaling.
passWeight x1
Data
7/10
Schema is well-constrained with useful indexes and FK integrity. Migration replay and restore verification are the main gaps before this qualifies as fully recoverable.
passWeight x1.5
AI Engineering
8/10
Agent scaffolding and context management are ahead of most repos at this stage. Tighten tool permissions to prevent production side-effects from AI-assisted workflows.
passWeight x1
Infrastructure
6/10
Hosting and service topology are appropriate and working in production. Infrastructure-as-code coverage is partial — the environment can run but not yet be reproduced from repo alone.
watchWeight x1
Observability
5/10
Product analytics and platform logs are in place. The next tier — error tracking, uptime monitoring, and routed alerts — would give the team meaningful signal when things go wrong.
watchWeight x1
Code Quality
8/10
Codebase is clean, well-typed, and passes CI. A few high-traffic modules have grown large enough to benefit from splitting before the next feature cycle.
passWeight x1
Delivery
7/10
CI/CD pipeline is in place and deployments are tracked. Adding a required-review gate and migration sequencing check would close the remaining gaps before a compliance review.
passWeight x1
Documentation
6/10
Core setup and feature docs are present and reasonably current. Operational runbooks for deployment, secret rotation, and incident response are the main missing pieces.
watchWeight x1
The audit tells you what is risky, why it matters, and what package I recommend. No 40-page generic code review. No noise.
What You Get
The goal is not to rewrite everything. The goal is to make the next change predictable.
Enterprise engineering practices applied to the apps you've already built. The kind of work that used to require a full-time senior engineer, delivered as a fixed-scope engagement at a fraction of the cost.
- ✓Free Production Risk Audit — Production Readiness Score, top 5 risks with severity, business impact, and fixed-price quote
- ✓Fixed-price estimate before you commit to anything
- ✓Structural remediation: infrastructure, database, error handling, logging, monitoring
- ✓Education and walkthrough so you understand what was fixed and why
- ✓Clean documentation. You own everything.
- ✓30-day workmanship guarantee: if something I changed stops working as specified within 30 days, I fix it at no charge
Launch Readiness Sprint
$2,000–$4,0001–2 weeks
“I built this with AI and want to launch without embarrassing myself.”
- ·Deployment review and cleanup
- ·Environment variable/secrets sanity check
- ·Basic error handling pass
- ·Logging setup
- ·README/runbook
- ·Production risk fixes surfaced in audit
- ·Final walkthrough
Production Stabilization Sprint
Recommended$4,000–$7,0002–4 weeks
“The app works, but every new feature breaks something.”
- ·Deployment and infrastructure cleanup
- ·Database migration workflow
- ·Auth/session/security boundary review
- ·Error handling and validation
- ·Logging and monitoring
- ·Repo structure cleanup
- ·CI/CD basics if needed
- ·Documentation and owner walkthrough
- ·30-day workmanship guarantee
Rescue Sprint
$7,000–$10,0004–6 weeks
“We have users, the app is fragile, and we need someone senior to stabilize it.”
- ·Deeper architecture cleanup
- ·Database repair or migration
- ·Production incident triage
- ·Major reliability fixes
- ·Deployment rebuild
- ·Security hardening
- ·Monitoring/alerting setup
- ·Handoff documentation
- ·Optional production support retainer
Production Support Retainer
$500–$1,000/moAfter stabilization sprint
“Once it's stable, keep it that way.”
- ·Monthly production health check
- ·Monitoring and error review
- ·Small fixes and dependency updates
- ·Deployment support
- ·Async technical guidance
- ·48-hour response window
- ·Priority access for new sprint work
- ·Larger feature work quoted separately
- ·30-day cancellation, no lock-in
Payment
50% upfront, 50% on delivery
Scope Changes
Change order required, quoted before work
IP Ownership
You own everything delivered
Availability
Select engagements, limited capacity
What Gets Fixed
The foundation I harden.
Database
Schema review, migrations, safer data changes, indexes that hold up under real load.
Infrastructure
Hosting shape, environment separation, secrets management, reproducible setup.
AI Engineering
Agent context controls, tool permissions, prompt hygiene, safe AI-assisted workflows.
Deployment
Reliable release path, CI/CD basics, rollback sanity, no more cowboy deploys.
Observability
Logs, error tracking, uptime monitoring, alerts — visibility into what breaks.
Code Quality
Type safety, lint gates, dead code removal, structure that can actually grow.
The Process
How the engagement works.
01
Send the repo
I review the app and identify production risks.
02
Get the audit
You receive a written report, score, and fixed-price quote.
03
Stabilize the foundation
I fix the structural issues. You get a full walkthrough so you understand what changed and why.
04
Walkthrough and handoff
You get documentation, a walkthrough, and clear next steps.
05
Optional support
Stay on retainer if you want a production safety net.
Where I've Built
My Approach
Fixing fast is easy. Fixing it properly is the hard part.
01
Identify the failure modes, not the symptoms
Most patching fails because it treats what you can see, not what's underneath. I don't start by fixing bugs. I read the codebase as a system: database schema, infrastructure, error handling, API boundaries, and failure modes. The bugs are symptoms. The architecture is the diagnosis.
02
Stabilize it properly, not quickly
Fast fixes compound. I go in and address the actual problem: infrastructure, database design, error handling, security. No shortcuts, no duct tape. The goal is a codebase that holds weight under real users, not one that passes the next demo.
03
Educate, not just hand off
A fixed app you don't understand is a future liability. I walk you through exactly what I did and why. You'll know your own codebase, understand the decisions made, and have the skills to keep it healthy going forward.
04
Production principles built in, not bolted on
Error handling, logging, monitoring, and security are not things you add at the end. Every remediation ships with structured error handling, proper authentication, environment-based configuration, and observability. These are not extras. They are the baseline.
My Story
I spent years learning what breaks production systems at scale. Turns out AI-generated apps break for all the same reasons.
When AI launched I watched the barrier to building collapse overnight. I began to see it in the enterprises I worked at and then I went and experienced it myself. I built my first app with AI-generated code and I was genuinely blown away. I got caught up in the ease of deploying code and all of the engineering principles I'd learned went out the window. But the demo looked great. I thought I had it all figured out.
Then the user showed up bringing the bug reports with them. One after another. Every new feature I tried to ship broke something else. I wasn't building anymore. I was playing whack-a-mole with a codebase that had no real structure underneath it.
Here's what made it embarrassing. I had years of production experience. I knew exactly what a well-engineered codebase was supposed to look like. And I still ended up with the same mess I had spent years helping enterprise teams clean up. Because I moved fast without applying any of it. I didn't ask the AI for proper error handling. I didn't design the database schema first. I didn't build in monitoring or logging. I just described features and shipped.
That's when it clicked. All those years of production experience don't show up in an AI-generated codebase unless you specifically know to ask for it. If I could fall into that trap with all the background I had, anyone could. This isn't about being a bad developer. It's about knowing what to ask for, and that's years of engineering experience that nobody tells you that you need.
I know what production-ready looks like and I know what to ask the AI to get there. I come in, find what's actually broken underneath the surface, and fix it properly. Then I walk you through exactly what I did and why. You'll understand your own codebase and know how to keep it healthy. You walk away with a stable app and the skills to keep it that way.
Over time I've built a systematic process for this. I go in and restructure the codebase without touching the functionality. What your app does stays exactly the same. The foundation underneath gets rebuilt properly, architected to hold weight and grow. I take what I learned breaking production systems at enterprise scale, combine it with what I know firsthand about how AI generates code, and apply both to your project. That's how you stop patching symptoms and start building on solid ground.
Past Work
Production engineering applied to real business software.
Win More Bids
85% accuracyBuilt and operationalized an ML app used for job quote predictions, giving estimators data-backed confidence before committing to a bid.
Relevant experience: Data pipelines, deployment, reliability, and business-facing software.
Automated Research
Hours saved weeklyBuilt a scraper and dashboard that replaced hours of manual research every week by automatically compiling competitor pricing into a live view.
Relevant experience: Scheduled jobs, monitoring, data quality, and automation reliability.
AI-Powered Workflow
Faster response timesConnected enterprise data sources into a single AI-powered interface, automated outbound messaging, and replaced a fragmented multi-tool workflow.
Relevant experience: Integrations, permissions, workflow reliability, and production handoff.
Ready to stop patching and start building?
Book a free 30-minute call. Tell me what you need. I'll tell you what it costs and how long it takes.
Common Questions
Everything you were going to ask anyway.
Free Production Risk Audit
Send me your repo. I'll tell you exactly where it will break.
Send me your repo and I'll review the parts of your AI-built app most likely to fail in production: deployment, database design, auth boundaries, error handling, logging, monitoring, environment configuration, and code structure. You'll get a short written report with your top risks, a production-readiness score, and a fixed-price quote to stabilize the app.
- ✓Production Readiness Score
- ✓Top 5 risks
- ✓Severity rating for each risk
- ✓Business impact explanation per risk
- ✓Fixed-price quote to stabilize the app
Get Your Free Production Risk Audit
No commitment. No charge.