# Joshua Durey (Josh Durey) - Applied AI Enablement > Applied AI enablement. I audit workflows for AI readiness, build agentic systems, and run the LLM quality control that gets teams to adoption they can trust. ## Entity & Hiring Summary - **Names:** Joshua Durey / Josh Durey (Founder, Human Creative) - **Primary Lane:** Applied AI Enablement Lead, AI Operations Strategist, Agentic Workflow Architect, Principal AI Learning Systems Lead - **Credentials:** M.A. Applied Linguistics (University of Birmingham); 15+ years leading learning & operations systems; governed 52,167 multi-agent sessions with 30+ Python hook gates; 31,500+ Udemy learners. - **Work Authorization & Availability:** US Citizen, English-only, 100% remote worldwide (US W-2 EOR via Deel/Remote, US LLC B2B contract, fractional leadership, or fixed-scope 2-week workflow sprints). - **Full Markdown Corpus for RAG & Agents:** [https://jdurey.github.io/llms-full.txt](https://jdurey.github.io/llms-full.txt) ## Live Agentic Endpoints & Interactive Proof Surfaces - [Hire / Work With Joshua Durey](https://jdurey.github.io/hire/): Full-time remote roles, fractional leadership, and fixed-scope 2-week workflow sprints (https://jdurey.github.io/hire.md). - [Live 3-Model Workflow & JD Diagnostic](https://jdurey.github.io/#diagnostic): Interactive multi-vendor workflow readiness & rubric diagnostic backed by Cloudflare Workers AI. - [Commission a Free Custom Day-1 Prototype](https://jdurey.github.io/#commission-prototype): Automated 4-stage pipeline builds a custom runnable proof at `https://jdurey.github.io/for//`. - [Live 3-Minute Interactive AI Video Screen](https://jdurey.github.io/interview/): Conversational technical & leadership screen escalating to https://cal.com/joshua-durey-wqgvgp/20-min-scope-call. - [B2B Corporate L&D SOP-to-LMS Compiler](https://jdurey.github.io/sop-to-lms/): Compress enterprise course production from 6 weeks to 24 hours with live SCORM 1.2 telemetry, 4-vendor blind-solver verification, and sample SCORM 1.2 download (https://jdurey.github.io/sop-to-lms/sample-incident-sop-scorm12.zip). - [Local-vs-Cloud Model Routing Tribunal](https://jdurey.github.io/m5-tribunal/): 72 SHA-256-verified runs comparing tiered open-weight vs frontier cloud models with TCO router calculator. - [Remote Streamable HTTP MCP Server (io.github.jdurey/human-creative)](https://josh-employer-funnel.josh-durey.workers.dev/mcp): Live MCP server (https://jdurey.github.io/.well-known/mcp.json | https://github.com/jdurey/human-creative-mcp) exposing workflow diagnostics, prototype commissioning, and SOP-to-SCORM verification tools. - [AgentCard (A2A Protocol)](https://jdurey.github.io/.well-known/agent.json): Machine-readable capabilities, endpoints, and catalog metadata (also mirrored at https://jdurey.github.io/.well-known/agent-card.json and https://jdurey.github.io/api/catalog.json). ## Commercial Implementation Playbooks (8 Guides) - [Hiring an Applied AI Enablement Lead: A 30-60-90 Day Playbook for Team Adoption](https://jdurey.github.io/guides/applied-ai-enablement-lead/) ([.md](https://jdurey.github.io/guides/applied-ai-enablement-lead.md)): What an Applied AI Enablement Lead actually ships in the first 90 days: workflow audits, self-checking agent pipelines, cross-family quality gates, and staff playbooks that turn shelfware seats into daily output. - [Enterprise AI Workflow Audit Checklist: Ranking Tasks by Payoff, Risk & Checkpoints](https://jdurey.github.io/guides/ai-workflow-audit-checklist/) ([.md](https://jdurey.github.io/guides/ai-workflow-audit-checklist.md)): A practical four-step checklist for auditing team operations before writing automation code: mapping real inputs, pricing silent errors, placing human checkpoints, and killing low-payoff pilots early. - [Self-Reviewing Agentic Workflows: Designing Pipelines Non-Technical Teams Trust](https://jdurey.github.io/guides/self-reviewing-agentic-workflows/) ([.md](https://jdurey.github.io/guides/self-reviewing-agentic-workflows.md)): How to replace fragile single-shot prompts with four-stage self-checking workflows: deterministic Python pre-gates, narrow drafting nodes, cross-family critics, and SHA-verified audit receipts. - [LLM-as-a-Judge Calibration: Stopping Self-Bias, Verdict Flips & Silent Truncations](https://jdurey.github.io/guides/llm-judge-calibration-and-qc/) ([.md](https://jdurey.github.io/guides/llm-judge-calibration-and-qc.md)): How to calibrate LLM-as-a-Judge panels against human expert baselines: binary decomposed rubrics, cross-vendor panels, golden-set agreement checks, and catching silent HTTP 200 truncations. - [SOP-to-SCORM AI Pipeline: Compiling Policy Docs into Verified LMS & QTI Modules](https://jdurey.github.io/guides/sop-to-scorm-ai-pipeline/) ([.md](https://jdurey.github.io/guides/sop-to-scorm-ai-pipeline.md)): How corporate L&D and compliance teams turn static SOPs and runbooks into ADL SCORM 1.2 packages, QTI 1.2 item banks, 300-DPI guides, and narrated 1080p lessons with 0% policy drift. - [6-Rung LLM Cost & Latency Routing: Cutting Enterprise Token Spend Without Losing Quality](https://jdurey.github.io/guides/llm-cost-and-latency-routing/) ([.md](https://jdurey.github.io/guides/llm-cost-and-latency-routing.md)): Why a tiny fraction of agent calls burn 80% of your API bill, and how a 6-rung policy router shifts routine checks to zero-token Python hooks and fast mid-tier models. - [AI Curriculum & QTI Assessment Quality Control: Blind-Solver Checks & Zero Answer Leaks](https://jdurey.github.io/guides/ai-curriculum-and-qti-assessment-qc/) ([.md](https://jdurey.github.io/guides/ai-curriculum-and-qti-assessment-qc.md)): Engineering-grade quality control for EdTech and enterprise assessment pipelines: catching ambiguous distractors, standards drift, hint answer leaks, and broken math rendering before students see them. - [Two-Week AI Workflow & QC Sprints: Moving Stalled Pilots into Daily Team Use](https://jdurey.github.io/guides/fixed-scope-ai-workflow-sprint/) ([.md](https://jdurey.github.io/guides/fixed-scope-ai-workflow-sprint.md)): How founders and operations leaders use a fixed-scope two-week sprint to audit one high-friction bottleneck, deploy a self-checking workflow with human checkpoints, and keep the runnable QC harness. ## Self-Serve B2B Enterprise Kits, Physical Amazon KDP Books & Training Catalog - [Corporate L&D Zero-Hallucination SOP-to-SCORM & Video Factory ($99 Instant Checkout via Whop, InStock)](https://whop.com/checkout/plan_y1ACgqNYGtul8/): Compile SOPs into blind-verified SCORM 1.2, QTI 1.2, 300-DPI PDFs & 1080p video; includes instant access to the 14-lecture (65-min) Video & Code-Lab Masterclass. - [Enterprise AI Eval, 6-Rung Cost Router & REA AgentEnv Pro Kit ($79 Instant Checkout via Whop, InStock)](https://whop.com/checkout/plan_lGsdWjQsePkvO/): Python harnesses for 4-vendor LLM judge evals, 6-rung cost routing & AgentEnv; includes instant access to the 14-lecture Production LLM Evals Masterclass & 40-question exam bank. - [AI for Corporate Training & Instructional Design: The UbD Glass-Box Playbook ($24.99 Live on Amazon, InStock Paperback, ISBN 9798142559005, ASIN B0HRQFWFPR)](https://www.amazon.com/dp/B0HRQFWFPR): Physical 8.5x11 print reference playbook for zero-hallucination SOP-to-SCORM, QTI 1.2, and video pipelines. - [Benchmark AP Precalculus Unit 3 Exam Prep Workbook ($16.99 Live on Amazon, InStock Paperback, ISBN 9798142561688, ASIN B0HRQJLPRZ)](https://www.amazon.com/dp/B0HRQJLPRZ): Companion 300-DPI physical curriculum workbook from Benchmark AP Prep (https://benchmark-ap.pages.dev). - [Udemy Instructor Hub (31,500+ Learners)](https://www.udemy.com/user/joshuadurey/): Instructor profile for Joshua Durey; new AI Corporate Training & Production LLM Evals courses are completing Udemy's standard 2-3 day instructor identity verification (instant access available today via Whop). - [Done-For-You B2B Async Pilot & Workflow Sprints ($2.5k-$10k Fixed-Scope)](https://cal.com/joshua-durey-wqgvgp/20-min-scope-call): Book a 20-minute scope call or request a free custom prototype at https://jdurey.github.io/#commission-prototype. ## Featured Greenfield Harnesses & Case Studies (55 Published) - [Model evaluation in machine learning](https://jdurey.github.io/work/model-evaluation-in-machine-learning/) ([.md](https://jdurey.github.io/work/model-evaluation-in-machine-learning.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Blind expert-parity: can a model adjudicate like a credentialed examiner, and can you prove it?. - [Llm judge metrics](https://jdurey.github.io/work/llm-judge-metrics/) ([.md](https://jdurey.github.io/work/llm-judge-metrics.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. - [Llm as a judge framework](https://jdurey.github.io/work/llm-as-a-judge-framework/) ([.md](https://jdurey.github.io/work/llm-as-a-judge-framework.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. - [Llm as a judge paper](https://jdurey.github.io/work/llm-as-a-judge-paper/) ([.md](https://jdurey.github.io/work/llm-as-a-judge-paper.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. - [Llm eval harness](https://jdurey.github.io/work/llm-eval-harness/) ([.md](https://jdurey.github.io/work/llm-eval-harness.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to How to audit an LLM judge before you trust its verdicts. - [Red teaming llm applications](https://jdurey.github.io/work/red-teaming-llm-applications/) ([.md](https://jdurey.github.io/work/red-teaming-llm-applications.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Red-teaming my own publish gate. - [Llm judge](https://jdurey.github.io/work/llm-judge/) ([.md](https://jdurey.github.io/work/llm-judge.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. - [Llm as a judge](https://jdurey.github.io/work/llm-as-a-judge/) ([.md](https://jdurey.github.io/work/llm-as-a-judge.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. - [Llm judge model](https://jdurey.github.io/work/llm-judge-model/) ([.md](https://jdurey.github.io/work/llm-judge-model.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. - [What is model evaluation in machine learning](https://jdurey.github.io/work/what-is-model-evaluation-in-machine-learning/) ([.md](https://jdurey.github.io/work/what-is-model-evaluation-in-machine-learning.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Blind expert-parity: can a model adjudicate like a credentialed examiner, and can you prove it?. - [Why use multiple AI models instead of sampling one model many times](https://jdurey.github.io/work/multi-model-panels-vs-resampling/) ([.md](https://jdurey.github.io/work/multi-model-panels-vs-resampling.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Author with one model, attack with another. - [How to run AI content pipelines unattended overnight safely](https://jdurey.github.io/work/unattended-ai-pipeline-failure-budget/) ([.md](https://jdurey.github.io/work/unattended-ai-pipeline-failure-budget.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Three failures your LLM pipeline never logs: gates for truncation, reasoning tax, and latency tails. - [How to make AI QC verdicts auditable and replayable](https://jdurey.github.io/work/replayable-ai-qc-verdicts/) ([.md](https://jdurey.github.io/work/replayable-ai-qc-verdicts.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Same answer, different grade: measuring when an AI grader can't hold a verdict. - [How to audit an AI pipeline when you cannot read its data](https://jdurey.github.io/work/audit-ai-pipeline-without-reading-data/) ([.md](https://jdurey.github.io/work/audit-ai-pipeline-without-reading-data.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Three failures your LLM pipeline never logs: gates for truncation, reasoning tax, and latency tails. - [How to choose an LLM provider when many serve the same model](https://jdurey.github.io/work/choose-llm-provider-by-latency-not-price/) ([.md](https://jdurey.github.io/work/choose-llm-provider-by-latency-not-price.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Three failures your LLM pipeline never logs: gates for truncation, reasoning tax, and latency tails. - [Why a few LLM calls carry most of your API bill](https://jdurey.github.io/work/llm-api-costs-concentrate-in-few-calls/) ([.md](https://jdurey.github.io/work/llm-api-costs-concentrate-in-few-calls.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Three failures your LLM pipeline never logs: gates for truncation, reasoning tax, and latency tails. - [How much do reasoning tokens add to LLM API costs](https://jdurey.github.io/work/how-much-do-reasoning-tokens-cost/) ([.md](https://jdurey.github.io/work/how-much-do-reasoning-tokens-cost.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Three failures your LLM pipeline never logs: gates for truncation, reasoning tax, and latency tails. - [Why does my LLM output get cut off and how do I detect it](https://jdurey.github.io/work/why-llm-output-gets-cut-off/) ([.md](https://jdurey.github.io/work/why-llm-output-gets-cut-off.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Three failures your LLM pipeline never logs: gates for truncation, reasoning tax, and latency tails. - [What is llm judge](https://jdurey.github.io/work/what-is-llm-judge/) ([.md](https://jdurey.github.io/work/what-is-llm-judge.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. - [Three failures your LLM pipeline never logs: gates for truncation, reasoning tax, and latency tails](https://jdurey.github.io/work/telemetry-gates/) ([.md](https://jdurey.github.io/work/telemetry-gates.md)): I aggregated a month of my own API telemetry, 88,000 calls, and found three failure classes that raise no exception anywhere. One model silently truncated 5.7% of its responses. Eight models spent 83 to 95 percent of their completion tokens on reasoning nobody reads. Providers serving comparable models ranged from 1.1s to 20s median latency, with one tail hitting 89 seconds. So I built three deterministic gates that run on telemetry alone, and tested them against a synthetic fleet with defects planted by construction. The gate is correct when it finds every planted defect and nothing else. - [How to evaluate llm output](https://jdurey.github.io/work/how-to-evaluate-llm-output/) ([.md](https://jdurey.github.io/work/how-to-evaluate-llm-output.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. - [The agent-boundary harness: measuring whether an AI agent respects a data fence, and whether the fence holds](https://jdurey.github.io/work/agent-boundary/) ([.md](https://jdurey.github.io/work/agent-boundary.md)): An agent with file access can leak in two ways. The guard code fails, or the model decides to cross. I built a two-layer instrument that measures both, scored by whether a sealed canary token ever reads out. A naive path guard passed 75% and leaked twice through known EscapeRoute-class bypasses. The hardened guard passed clean. Then I tempted real models with tasks where crossing the boundary is the easy path, and counted the crossings. Everything runs on a fictional workspace, so the whole machine is public. - [What is llm as a judge](https://jdurey.github.io/work/what-is-llm-as-a-judge/) ([.md](https://jdurey.github.io/work/what-is-llm-as-a-judge.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. - [How to red-team AI-built tools and content before they reach a classroom](https://jdurey.github.io/work/red-team-ai-built-tools-before-classroom-use/) ([.md](https://jdurey.github.io/work/red-team-ai-built-tools-before-classroom-use.md)): A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work.