Triangulate AI. Engineer Trust. Scale Decisions.
Using GenAI safely to improve engineering, manufacturing, operations decisions.
TriAIQ triangulates AI to deliver correct, defensible engineering decisions and secure analytics automation without data leaving the firewall.
An engineering-first operating model for safe GenAI adoption.
Validate AI outputs against statistical theory and verified engineering software.
Use GenAI to speed analysis and automation within a structured, auditable framework.
Produce correct, defensible engineering decisions across engineering, manufacturing, and operations.
TriAIQ delivers this through training, coaching, and secure automation — enabling AI scale without increasing risk or data exposure.
The Problem
- Engineering teams are under pressure to use GenAI for speed — but speed without assurance creates risk.
AI outputs can be:
statistically invalid
based on hidden assumptions
silently wrong
difficult to audit
Blind trust does not scale.
Manual validation does not scale.
The TriAIQ Solution
TriAIQ triangulates AI to deliver high-quality, correct, and defensible engineering decisions, and secure automation of statistical analysis and reporting — without data leakage.
We teach and coach engineering teams to improve product development, design, quality, and manufacturing by using GenAI correctly, validating AI outputs through triangulation, and scaling productivity safely behind the firewall.
TriAIQ sits at the intersection of GenAI, statistical rigor, and engineering execution — ensuring AI-assisted engineering decisions are correct, defensible, and scalable.
FRAMEWORK — STAGES 1–4
TriAIQ operationalizes AI governance through a four-stage engineering framework.
Use GenAI correctly for engineering questions
Ask statistically valid, testable questions
Triangulate GenAI outputs using statistical theory and verified tools
Continue triangulating GenAI-generated logic using proprietary master system prompts, then execute validated logic automatically in trusted statistical software
Triangulation does not stop at Stages 1–3. Stage 4 industrializes it.
TESTIMONIAL INTRO
TriAIQ training has been delivered to engineering teams at leading technology companies such as Apple, Amazon Lab126, Western Digital, and Lam Research. Selected testimonials are shown below.
“This training fundamentally changed how our team uses data to make confident engineering decisions.” Hardware Engineering, Consumer Devices
“We immediately applied the methods to manufacturing problems with real impact.” Manufacturing / Process Engineering
“The triangulation approach removed guesswork from our quality decisions.”Quality & Reliability Engineering
“This is the first program that made AI usable without increasing risk.” Engineering Leadership
ROI / AI INVESTMENT STATEMENT
TriAIQ also helps organizations justify AI investment by converting AI usage into measurable engineering productivity, defensible decisions, and scalable automation — without increasing risk or data exposure.
Start with a pilot cohort.
Validate GenAI-assisted engineering decisions safely — with defensible results and no data leaving the firewall.