You will get hands-on Claude Code coaching with your repo configured and a working eval
Project details
Most people using an AI coding tool are getting maybe a third of what it can do. They paste code into a chat, get something plausible back, and never build the harness that makes it reliable. I'll show you the rest.
I'm a software engineer with over a decade of professional experience, most of it on API and systems integration, and I now build and run production AI agent workflows. I've hit the failure modes personally, not read about them: agents colliding over a shared resource, saves that report success and never persist, silent stalls with no recovery.
This is hands-on and specific to your codebase. Screen share, your repo, your actual problem. Not slides.
We can cover prompts, context, loops and harnesses, when an agent is the right call, evals, and reliability.
Every session ends with something working in your repo - a configured project file, a custom command, a first eval that runs. Not notes.
For engineers and technical founders who want to go faster, and managers who need to understand what their team is doing with these tools. I adjust the depth to you.
US-based. Same-day replies.
I'm a software engineer with over a decade of professional experience, most of it on API and systems integration, and I now build and run production AI agent workflows. I've hit the failure modes personally, not read about them: agents colliding over a shared resource, saves that report success and never persist, silent stalls with no recovery.
This is hands-on and specific to your codebase. Screen share, your repo, your actual problem. Not slides.
We can cover prompts, context, loops and harnesses, when an agent is the right call, evals, and reliability.
Every session ends with something working in your repo - a configured project file, a custom command, a first eval that runs. Not notes.
For engineers and technical founders who want to go faster, and managers who need to understand what their team is doing with these tools. I adjust the depth to you.
US-based. Same-day replies.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI-Generated CodeAI Development Language
PythonAI Models
GPT-4What's included
| Service Tiers |
Starter
$100
|
Standard
$450
|
Advanced
$1,400
|
|---|---|---|---|
| Delivery Time | 3 days | 10 days | 21 days |
AI Model Integration | - | - | - |
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | - | - |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code | - |
Frequently asked questions
About Rob
AI Automation & Claude Workflow Engineer
Midlothian, United States - 12:02 am local time
Seventeen years as a professional software engineer sits behind that, most of it spent on the unglamorous part: API and systems integration. Microservices, ETL and batch pipelines, third-party verification and dispatch integrations, partner-facing APIs. I have spent most of my career making other people's systems talk to each other reliably, which turns out to be exactly what AI automation is once the demo is over.
What I bring that most people bidding on these jobs do not: I have already hit the failure modes.
- Agents colliding over a shared resource
- Saves that report success and never persist
- Silent stalls with no recovery path
- Intermittent API syncs that only show up if you re-read the record
- Steps that genuinely need a human in the loop, where pretending otherwise breaks the run
I keep a documented log of every one of these I have found in production, with the reproduction and the workaround. I build for them now instead of discovering them on your project.
Recent work:
- A multi-agent publishing pipeline that runs end to end unattended: asset generation, code-composited typography, file preparation, and agent-driven publishing, with an automated verification pass after every step because agents fail quietly.
- An adversarial builder-versus-critic harness where independent fact-checking agents audit the primary agent's output. On its first real run it invalidated three recommendations that would have cost real money.
Tools: Claude and Claude Code, Anthropic API, Python, Java, browser automation, n8n, REST APIs, AWS, PostgreSQL.
Good fits: connecting an existing stack (CRM, accounting, comms, storage) with automation and a sensible amount of AI on top; building an agent workflow that has to run without a babysitter; or being taught over screen share if you would rather own the thing yourself afterward.
US-based in Virginia. Veteran-owned LLC. Fluent Spanish. I reply the same day, and I will tell you when automation is the wrong answer.
Steps for completing your project
After purchasing the project, send requirements so Rob can start the project.
Delivery time starts when Rob receives requirements from you.
Rob works on your project following the steps below.
Revisions may occur after the delivery date.
Intake and planning
I read the repo and goals you send and come to the session with a plan, so we spend your time on the problem instead of on discovery.
Live working session
Screen share, working in your actual codebase on your actual problem. I adjust the depth to your experience level as we go.