You will get a real, production-grade AI proof-of-concept
Project details
Most AI POCs are sandbox demos — they dazzle, then you pay to rebuild them for production. Ours aren't. we start with a paid, fast assessment of where AI actually earns its keep in your business, we co-author a written SOW (scope, acceptance test, architecture, cost-per-run, growth path), and then we build a real, smaller-scope POC on my production kernel — the same spine our live systems run: orchestration, hard guardrails, an independent cross-model judge, monitoring, retries, per-run cost caps, human-in-the-loop, and full audit trails.
Going to production doesn't mean rebuilding. It means WIDENING the slice — more of your workflow, real data, calibrated thresholds, hardening. The foundation never gets thrown away.
You choose the stack: managed AWS serverless, or independent/BYOK on infrastructure you fully own (your keys, your data, zero lock-in). The assessment fee is credited toward the POC. Tell me us workflow you want to prove and your preferred stack — We'll scope it honestly.
Going to production doesn't mean rebuilding. It means WIDENING the slice — more of your workflow, real data, calibrated thresholds, hardening. The foundation never gets thrown away.
You choose the stack: managed AWS serverless, or independent/BYOK on infrastructure you fully own (your keys, your data, zero lock-in). The assessment fee is credited toward the POC. Tell me us workflow you want to prove and your preferred stack — We'll scope it honestly.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI-Enhanced Classification, AI-Generated Code, AIOps, Anomaly Detection, Conversational AIAI Development Language
PythonAI Models
ChatGPT, GPT-4, WhisperWhat's included
| Service Tiers |
Starter
$500
|
Standard
$2,500
|
Advanced
$7,500
|
|---|---|---|---|
| Delivery Time | 5 days | 14 days | 30 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 | - |
Frequently asked questions
About Paul
AI Architect | I Build AI Systems That Survive Production
Minneapolis, United States - 7:43 am local time
real edge cases — and quietly falls apart. The hard part of AI was never the prototype. It's
making it survive production. That's the part I build.
I'm an AI architect who designs, ships, and OPERATES production AI systems — not slide decks, not POCs. I founded and built X20 Creative, a live commercial AI platform with real tenants: feed it one video and a 32-agent suite automatically produces 47+ content assets across 10 social platforms in 5–10 minutes — in the creator's own brand voice. Every piece of it runs in production today.
Before X20, I spent a career in senior engineering and architecture roles at College Board,
Cisco, Optum, and Boston Consulting Group, with a UCLA certification in Artificial
Intelligence. I build from first principles for enterprise-grade reliability: deterministic
orchestration where possible, bounded non-deterministic AI only where the rule-space is
genuinely too large to enumerate, with explicit audit trails and per-job cost accounting
throughout.
What I can do for you:
• AI-first product builds — agentic workflows, RAG, automation, human-in-the-loop — blank
page to production.
• AI into your existing product — drafting, classification, search, summarization,
decisioning — without a rewrite.
• Automation that runs itself — ingest → process → generate → publish pipelines with
self-healing, silent-failure detection, and observability built in.
• Media & video lifecycle automation — turn one recording into a full multi-format content
set, automatically (I've built exactly this, at scale).
• AI strategy & architecture — a clear, written plan for what to build, what to skip, and
what it'll cost, before anyone writes code.
Two ways to own it:
• Managed cloud — I run it for you on AWS serverless: elastic, observable, scale-to-zero.
• Independent / BYOK — the same agents on infrastructure YOU fully control: your API keys, your data, your machine, zero vendor lock-in.
How I work: every engagement starts with a scoped, written definition — outcomes,
architecture, trade-offs, on paper before a line of code. You see working software early and
steer at every step. No black boxes.
Because I build on proven, production-hardened agent cores instead of reinventing the
plumbing each time, I deliver faster and cheaper than a from-scratch build — and I pass that on with flat, fixed-price packages so you know the number before we start:
• AI Opportunity Audit — map where AI actually earns its keep, with a prioritized, costed
plan.
• Working Pilot — one real workflow, automated and running, in about two weeks.
• Production Build — full system shipped to production with monitoring and recovery in place.
• Independent / BYOK Deployment — your agents on your own infrastructure, keys and data fully yours.
• Care Plan — I keep it running, watch for silent failures, and iterate.
Hourly ($75/hr) is available for open-ended or advisory work.
If your AI initiative needs someone who can tell you exactly what to do — and build the thing that's still running a year from now — send me what you're trying to accomplish and I'll tell you honestly whether and how AI moves the needle.
Steps for completing your project
After purchasing the project, send requirements so Paul can start the project.
Delivery time starts when Paul receives requirements from you.
Paul works on your project following the steps below.
Revisions may occur after the delivery date.
Assessment + shared written SOW
Discovery with your team; map your highest-ROI AI opportunities; co-author a written SOW for the one workflow we'll prove — acceptance test, architecture, cost-per-run, growth path
Build the real POC slice on the kernel
A narrow, production-grade POC on my kernel — guardrails, an independent cross-model judge, monitoring, cost caps, human-in-the-loop, audit trail — running your workflow on sample data, your stack (AWS or BYOK).