You will get a single purpose ai agent for one workflow
Project details
I build the specific workflow automation your business needs — the cross-system handoff that lives between tools, the trigger you do manually every morning, the conditional logic that's specific to how you run.
Whether you've tried a pre-built (Claude, Copilot, Workspace AI) and hit a wall, or you're starting fresh — same process: 30-minute intake, clear scope, build starts within 48 hours.
What you get:
• One fully-wired automation flow (your stack, your data, your edge cases)
• Failure-mode handling: what fires when an input is malformed, an API rate-limits, a row fails validation
• Runbook documenting trigger, expected output, monitoring approach
• 30-day post-launch support for environment-related issues (Standard and Premium)
Whether you've tried a pre-built (Claude, Copilot, Workspace AI) and hit a wall, or you're starting fresh — same process: 30-minute intake, clear scope, build starts within 48 hours.
What you get:
• One fully-wired automation flow (your stack, your data, your edge cases)
• Failure-mode handling: what fires when an input is malformed, an API rate-limits, a row fails validation
• Runbook documenting trigger, expected output, monitoring approach
• 30-day post-launch support for environment-related issues (Standard and Premium)
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, AI-Enhanced Classification, AI-Generated Art, AI-Generated Code, AI-Generated Video, Natural Language Understanding, Synthetic Data Generation, Text RecognitionAI Development Language
PythonAI Tools
GitHub Copilot, Hugging Face, Microsoft 365 CopilotAI Models
ChatGPT, Midjourney AIWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$2,000
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 10 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 2 |
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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$500 - $1,000Frequently asked questions
About Jordan
Ex-Amazon PM | I Build & Run Production AI: Automations, Agents, Audit
Seattle, United States - 5:12 pm local time
Pasha Group: regional logistics across ports. Vessels, cargo, chassis coordination. Flexport: consumer goods moving around the world; I stood up the Germany and US Mid-Atlantic operations and made them profitable fast. Amazon: built the first dedicated logistics function for outsourcing partners in Worldwide Customer Service. 15+ organizations, 44 countries, a 60% reduction in delivery times for critical IT hardware. Most recently, leading security and technical programs at scale for the largest customer service organization in the world. Along the way I built a framework: Standardize → Productize → Automate. The org eventually adopted it.
For the past year and more I've been building and running production AI: 17 services across 7 domains. Automated workflows, with and without AI inference. Secure data pipelines and closed improvement loops. Compliance enforcement as a service that workflows call, not a checklist someone runs.
Running AI in production forced me to solve the hard parts. Models forget everything between sessions. Outputs drift. A system that worked last month breaks when the model updates. So I built for it: task scaffolding with defined inputs, outputs, and acceptance criteria before work starts. Verification loops that catch drift before it reaches production. Permission tiers so the AI knows what it can do alone, what needs approval, and what is permanently off-limits. Audit trails and hooks that enforce rules at the boundary.
That system is what I bring to a build. The same discipline: governance before execution, structure before scale. Senior-level scoping at AI delivery speed, and the actual build: diagnosed, built, documented, handed off to production.
Two kinds of problems are the fit. The first: you know exactly what's broken and need it built right. The second: you've already turned on Claude for Small Business, Copilot, or another pre-built and hit a wall on integration or business-specific logic. Either way: 20-minute intake, clear scope, work starts within 48 hours.
Who I work with: SMBs and family offices with a specific workflow to automate or audit. Early-stage teams that need senior structure without the salary. Operations leaders who want an honest read on where AI fits, not what a vendor says it does.
Who this isn't for: discovery-only work with no build on the horizon, or exploratory conversations with no defined problem. If you know what you're trying to fix, or you want someone to tell you fast, let's talk.
Steps for completing your project
After purchasing the project, send requirements so Jordan can start the project.
Delivery time starts when Jordan receives requirements from you.
Jordan works on your project following the steps below.
Revisions may occur after the delivery date.
30 Minute Call
30 Minute Scoping Call