AI Systems Operationalized
We use an adaptive approach to meet you where you are and architect AI systems for your specific needs.
You get solutions to business problems, increased productivity, and a toolkit of resources to manage the AI systems and programs. Transfer of ownership happens with us — not an afterthought.
About
Docktor Labs is the consulting practice of James Docktor — a Senior Instructional Designer and AI systems builder with 10+ years of experience at Silicon Valley's most demanding technology companies, including Google, Apple, VMware, Amazon, Facebook/Meta, Atlassian, and Waymo.
The practice sits at a rare intersection: deep instructional design expertise combined with hands-on AI systems building. Docktor Labs operates as your trusted advisor across all four layers — strategy grounded in diagnosis, architecture built to production standards, evaluation that proves it works, and adoption that makes it stick.
Before Docktor Labs, James spent 13 years as a mathematics teacher. That background informs everything; active listening so a real understanding of clients' needs is structural, not superficial. Design patterns and principles govern how Docktor Labs designs AI systems.
Proprietary Framework
Think • Integrate • Pair & Share is a multi-model consensus architecture invented by James Docktor to solve a fundamental problem in enterprise AI: how do you trust an LLM recommendation when you can't verify it yourself?
The TIPS Framework routes every query through multiple Claude models simultaneously. A separate LLM Evaluator microservice scores outputs against each other before anything surfaces to the user — flagging honest uncertainty rather than projecting false confidence.
Production-deployed on Render & Vercel. The IP core of every Docktor Labs engagement.
How Docktor Labs Works
Docktor Labs works in individual layers and connects all four, from first diagnosis to full adoption.
AI roadmaps, governance frameworks, risk guardrails, and vendor selection — grounded in understanding your actual workflows first.
RAG pipelines, MCP server integrations, multi-agent workflows, and the API connections that make AI reliable in your actual systems.
LLM benchmarking, red-teaming, TIPS Framework confidence scoring, and automated evaluation — so you know it works before it goes live.
Champion networks, enablement programs, and handoff packages designed so your team owns the system — not a dependency on us.
Services
Half-day or full-day team enablement grounded in 10+ years of L&D expertise. Designed for teams moving from AI awareness to AI operation. Role-differentiated tracks for individual contributors, managers, and technical leads.
Half-day • Full-day • Custom
The TIPS Framework applied to your AI stack. Multi-model consensus validation, confidence-scored findings, and prioritized recommendations for improving LLM reliability, governance, and output quality across your organization.
Assessment • Report • Roadmap
End-to-end engagement: discovery, build, enablement, and handoff. Your team owns what we build together. Phases cover needs analysis, AI workflow design, champion network activation, and measurement infrastructure.
Discovery • Build • Enablement • Handoff
Background & Credentials
Contact
Whether you're running your first AI pilot or scaling adoption across a global team — let's talk about what you're building and where the friction is.
jjd@docktorlabs.org