About / Toni Montez

I help people turn business ideas and messy operations into systems they can actually use.

Toni Montez

Builder / Advisor

My background crosses full-stack software engineering, enterprise AI and solutions architecture, founder projects, business operations, AI/ML training, and coaching. The useful thread is simple: understand the real problem, shape the path, build the system, and leave the owner with something they can run.

Start with a Diagnostic Call

Background

Where the capability comes from.

Software engineering JPMorgan Chase

Large systems teach you that a useful application has to survive real users, shared data, APIs, integrations, review, and handoff. Build clear paths, not just screens.

Enterprise AI and solutions architecture Enterprise AI and cloud · major technology company

In my professional work in enterprise AI and cloud, I lead enablement and solutioning workshops where business and technical teams meet: discovery, constraints, stakeholder language, cloud paths, and practical AI adoption. The job is to translate business needs into systems people can actually use, at scale and under real review.

AI/ML training Texas McCombs / UT Austin

Forecasting, classification, RAG, deep learning, and computer vision all need different data, metrics, risks, and review boundaries. Use AI where it supports a real business decision.

Founder and operator Omnexus / AMT Fitness / 300+ clients

Founder work and coaching both teach the same lesson: people need the next step to be visible, usable, and owned. Build systems that can be operated after the build is done.

AI/ML applied

AI should make the business clearer, not harder to trust.

I work in enterprise AI and cloud in my professional life and trained through business-applied AI/ML projects at Texas McCombs / UT Austin. The consulting version stays practical: source material, workflow, review boundaries, and a human-owned operating path.

01Forecasting

Planning over guessing

Random Forest sales forecasting to support real planning decisions, not dashboards no one reads.

Time seriesRF modelSales planning

02Retrieval

Grounded retrieval

RAG pipelines that stay tied to source material and keep hallucination risk visible and contained.

RAGk=3Grounding

03Classification

Failure prediction

Classification models that surface risk early, with recall as the metric that matters in the business context.

Recall 0.837Test setRisk signal

+How I use it

AI as leverage, not a black box

Every AI use in a client engagement has a stated purpose, a review boundary, and a human-owned operating path. I do not build AI for the sake of it.

Stated purposeReview boundaryHuman-owned path

What this means for clients

Bring the thing you want to build or the process you cannot keep carrying manually.

Websites, apps, admin portals, AI-supported workflows, migrations, and business setup all start the same way: get the idea out of your head, make the path visible, and build the first useful version.