DATA, AI & TECHNICAL CONSULTING

Better decisions from imperfect data.

I help life-sciences teams work out what their research supports, civil teams make sense of scattered project records, industrial and agricultural teams compare process options, and small businesses build clearer websites or useful tools. I bring a PhD in microbiology and applied machine learning experience, and I have helped take products to market in industry.

DECISION TRACEMETHOD / 001
01 / QUESTIONWhat needs to change?
02 / EVIDENCEWhat do we know?
03 / LIMITSWhat is uncertain?
REVIEW GATE Defined next step Scope · review · handoff

INPUTS TRACEABLEJUDGMENT ACCOUNTABLE

CHOOSE YOUR FIELD

Where I can help

The work varies by field. Explore the area closest to yours, or get in touch about a problem that crosses disciplines.

Clear scope Evidence you can check Files you keep

Examples of the work and the thinking behind it

I've included research projects, employment experience and consulting examples. Each is labeled so you can see what was actually demonstrated.

Flagship case study · Commercialized product

Turn complex data into a product customers can use.

I brought scientific modeling, product development and customer economics together to turn a research program into a commercial decision tool at a major animal-nutrition company.

The original problem was poultry nutrition. The broader capability is turning complicated data into useful recommendations, making the service affordable to deliver and giving customers a reason to adopt it.

$180 → $25per-sample analysis cost

Read the flagship case study →

Major animal-nutrition company · 2015–2018 · Historical employment work

Galleon™ Broiler Microbiome Intelligence received a 2023 Gold Edison Award after my tenure.

A closer look at a recurring workflow

If a process consumes too much time or keeps producing avoidable errors, we can examine it before anyone commits to a build. The options below stand on their own. Website, scientific modeling and writing projects can be scoped directly.

  1. 01

    Workflow Teardown

    A half day on one workflow with the people who run it, then a short readout: where the friction is, and whether the Sprint is the right next step.

    Teardown details →
  2. 02

    Diligence Sprint

    Three calendar weeks after kickoff and receipt of agreed source material. You leave with a pilot specification and a build, narrow, defer, or stop decision.

    Sprint details →
  3. 03

    Pilot build

    If the decision is build, the pilot runs in your environment on representative inputs, with agreed acceptance criteria and run records you can inspect.

    Pilot details →
  4. 04

    Independent advisory

    Review an AI decision, vendor claim or existing workflow at any stage. A single engagement or a scoped monthly retainer ends in a written advisory record. Evaluation can be scoped explicitly; nothing auto-renews.

    Advisory details →

What would you actually receive?

The examples show what a first assignment could look like in each field, including what you would receive and who reviews it.

See example deliverables

Selected work from my employment history

These examples come from my employment history. They show work I led or contributed to in those roles, not results delivered to McIntosh Consulting clients.

Over 62 countries reported adoption of HTSi and its intestinal-integrity index

At Elanco, I owned HTSi strategy, vision, roadmap and analytics direction, coordinating R&D, marketing and sales. A 2020 study reported adoption of HTSi and its intestinal-integrity index by customers in over 62 countries during my tenure.

Elanco · HTSi product ownership · 2020 platform adoption study

Read the 2020 adoption study →
R&D portfolio governance and investment decisions

At Land O'Lakes, I monitored the PMI portfolio against business goals, built dashboards and led go/no-go decisions. I also led customer relationships and intellectual-property strategy for the portfolio.

Land O'Lakes · PMI Business Portfolio

How I check AI-assisted work

A model can produce convincing work that falls apart when someone tries to use it. I test the result against the real task before asking a team to rely on it.

Read the full method →
  • Keep experimental tools away from live systems.

    The agent works in a contained workstation with the tools it needs and nothing else, walled off from your network and operations.

  • Test what happens in the actual workflow.

    AI-built work routinely passes its own tests and still does nothing in the real flow of the job. I check the work in that flow, on the inputs and handoffs the job actually has, before it counts as done.

  • Trace important numbers back to the source.

    Where a value has to be right, it comes from the source document, the published table, or a federal data feed, with a citation. Never from the model's guess.

  • Bring the right specialist into the review.

    I know where my expertise ends. The people responsible for the science, engineering or business decision review the result. I build the workflow that makes their review possible.

Services and what you receive

Review the named deliverables, required inputs and boundaries. These services can stand on their own or support a larger scoped project. The four field pages show where each one fits.

Workflow Engineering

Review a recurring workflow, test an existing synthetic dataset or build a bounded pilot with acceptance criteria and run records.

Scientific & R&D Decision Support

Evaluate AI choices, map R&D and scale-up risks, or compare process options before committing resources.

Federal & Technical Communication

Review an eligible proposal, establish a repeatable proposal process or explain technical work to a defined audience.

Dr. Vernon McIntosh, AI/ML workflow engineer and consultant

Dr. Vernon McIntosh

Founder & Principal Consultant

Much of my work begins with a team that knows something needs to improve but hasn't found a practical way forward. I look at how the work actually happens and help decide whether the answer is a technical review, a simpler process or something worth building. Sometimes that's an AI workflow. Sometimes it's a straightforward business tool.

My background includes product commercialization at a major animal-nutrition company, product ownership at Elanco, R&D portfolio governance at Land O'Lakes and scientific evaluation at Signature Science. I hold a PhD in Microbiology from the University of Tennessee, Knoxville, and am a named co-inventor on granted US patents.

Hiring for your team? View my employment portfolio →

AI/ML Workflow EngineeringAutomation & Data ScienceMetabolic EngineeringFederal Proposal SystemsCivil Engineering Tooling

Published work and patent records

Named co-inventor on six granted US patents for engineered yeast that produces succinate, and on published US and PCT applications, including microbiome analytics for animal nutrition. Doctoral research on the transcriptional response of microbial systems to chemical stressors. Everything below is on file and verifiable.

Patents & Applications

Research & Recognition

Writing about science, technology and how we evaluate ideas

Essays about practical AI, scientific research and the assumptions behind technical decisions.

Bring the question or task.

Tell me what you want to improve, the evidence or material you already have and the result you need. We can decide whether the right next step is a website, a focused tool, an analysis or a narrower review.

Send Vernon a message

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Prefer email? consulting@vernonmcintosh.com