My Claude-Assisted Development Workflow: How I Deliver in Days What Used to Take Weeks
People keep asking how a solo consultant delivers production-grade automations so fast. The answer isn't a productivity hack or 80-hour weeks. It's a deliberate workflow built around AI-assisted coding — backed by a decade of SRE experience that knows where the code needs to be right.
The Stack
My development accelerator is Claude — both the API for building client solutions and Claude Code (the CLI tool) for my own development workflow. My infrastructure is AWS, managed with Terraform, deployed through GitHub Actions. Monitoring goes to Datadog. Everything runs under my linuxlsr GitHub identity.
That's it. No complex toolchain, no expensive platform subscriptions, no team of developers. Just me, Claude, and battle-tested infrastructure patterns.
How a Project Actually Flows
Here's what a typical client engagement looks like, day by day. Not the idealized version — the actual one.
Day 1: Discovery and Architecture
30-60 minutes with the client understanding the pain point. Then I sit down with Claude and work through the architecture: what data flows where, what APIs are involved, what are the failure modes. Claude helps me think through edge cases I might miss and draft the initial system design.
By end of day, I have a system diagram, a Terraform skeleton, and a clear scope document.
Days 2-3: Core Build
This is where the acceleration really shows. I describe what I need — a Lambda function that processes incoming webhooks, validates the payload, transforms the data, and writes to DynamoDB — and Claude generates the initial implementation. I review it, adjust it, test it, iterate.
What used to take three days of writing boilerplate takes three hours of reviewing and refining. I'm not blindly accepting generated code. I'm reading every line with the same SRE lens I'd apply to any production system: error handling, input validation, logging, retry logic. Claude gets me to 80% fast; I spend my time on the 20% that matters most.
Days 4-5: Infrastructure and Deployment
Terraform modules for the AWS resources, GitHub Actions for CI/CD, Datadog monitors for alerting. I have templates from previous engagements, and Claude helps me customize them for each client's specific setup. The infrastructure code gets the same review rigor as the application code.
Days 6-7: Testing, Documentation, Handoff
Integration testing, load testing if relevant, runbook documentation, architecture diagrams. Claude is particularly good at helping write clear documentation — I describe the system and it produces a first draft that I edit for accuracy and tone. The client gets a complete package: working system, monitoring dashboard, runbook, and a 30-day warranty.
The 80/20 rule of AI-assisted development: Claude writes the first 80% of the code in a fraction of the time. I spend my time on the 20% that determines whether it works at 2am on a Saturday — error handling, edge cases, retry logic, and monitoring. That 20% is where a decade of SRE experience earns its keep.
What This Means for Clients
The direct benefit is speed and cost. I deliver a production-grade automation in 1-2 weeks that a traditional consulting shop would take 4-6 weeks to build. The indirect benefit is that I charge for value delivered, not hours burned. My AI-assisted workflow means I'm efficient, and I pass that efficiency on as competitive pricing.
A Tier 1 automation sprint runs $3,000-$8,000 and delivers in 1-3 weeks. That's not because I'm cutting corners — it's because I'm not spending 60% of my time writing boilerplate code and infrastructure config from scratch.
The Important Caveat
AI-assisted development makes a good engineer faster. It does not make a non-engineer into a good engineer.
The reason my output is reliable isn't Claude — it's the decade of SRE experience that tells me where to look for problems, what edge cases matter, and how production systems actually fail. When Claude generates a Lambda handler, I know to check whether it handles API timeouts, whether the error responses match the expected schema, whether the retry logic has backoff, and whether the logs are structured for CloudWatch Insights queries.
Claude generates the code. I bring the judgment. That combination is what makes this work.
Why I'm Sharing This
Transparency. If you're considering hiring me, you should know how I work. There's nothing magic about it — it's a deliberate workflow built on solid engineering fundamentals, accelerated by the best AI tooling available today.
I also think this is the future of technical consulting. Solo practitioners with deep domain expertise and AI-assisted workflows will increasingly outperform small agencies on speed, cost, and quality for focused automation work. The leverage that AI coding tools provide is real, but only when wielded by someone who knows what "production-grade" actually means.
The bottom line: you're not paying for my time typing code. You're paying for the engineering judgment that ensures the code works reliably in production. AI lets me deliver that judgment faster — and pass the savings on to you.
Curious what this workflow can do for your business?
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