Why I’m Building CogMind Consulting

Early-stage life-science companies often have excellent science.

They may have a promising assay, a novel diagnostic platform, an emerging therapeutic technology, or years of academic research behind an invention.

What they frequently do not have is unlimited bandwidth.

A five-person biotech company cannot employ a senior specialist for every problem it encounters. The founders may simultaneously be responsible for experiments, fundraising, regulatory strategy, intellectual property, hiring, vendors, investors, and the next development milestone.

That works—until the stakes suddenly get higher.

A validation study is about to begin.

A clinical assay needs to perform reliably.

An FDA interaction is approaching.

An SBIR proposal needs to become an executable development program.

A CRO has been contracted, but someone still needs to make sure the study answers the question the company actually needs answered.

At that point, a technical mistake can mean more than an inconvenient experiment.

It can mean lost samples, months of delay, additional capital, a missed regulatory opportunity, or a study that produces data without producing a useful decision.

That is the gap I am building CogMind Consulting to help fill.

The gap between good science and good development

Scientific expertise and product development are related, but they are not the same thing.

A technically strong team can still struggle with questions such as:

What evidence do we actually need?

What should be demonstrated before we advance?

Which risks matter most?

How should success be defined before the experiment begins?

Does the validation strategy match the intended use?

What exactly should the CRO deliver?

What question do we actually need FDA to answer?¹

How should an SBIR milestone advance the product rather than simply complete the grant?²

These questions sit at the intersection of science, development strategy, regulation, engineering, and execution.

They are also the questions that become increasingly expensive to answer after the work has already begun.

Where my perspective comes from

I am a biomedical engineer by training, but my work has rarely stayed inside a single scientific discipline.

My experience has included diagnostic development, analytical and bioanalytical validation, medical technology, translational research, controlled drug delivery, NIH SBIR-funded development programs, clinical bioanalysis, laboratory scale-up, technical writing, and multidisciplinary scientific program leadership.

I have worked with scientists, engineers, physicians, academic investigators, executives, laboratories, vendors, and regulatory specialists.

The individual technologies have changed.

The underlying development problem has been remarkably consistent:

How do we convert a complicated scientific objective into a structured program that produces useful evidence and a defensible decision?

That is the problem I find most interesting.

A simple development framework

One framework captures much of how I think about this work:

Requirement → Risk → Experiment → Evidence → Decision

Start with the requirement.

What must be true for the technology, assay, device, or development program to succeed?

Then identify the risks.

What could prevent that requirement from being satisfied?

Design experiments that meaningfully interrogate those risks.

Turn the resulting data into traceable evidence.

Then use that evidence to make an explicit decision.

Proceed.

Modify.

Investigate.

Repeat.

Redesign.

Or stop.

The framework is intentionally simple.

But development programs become surprisingly complicated when one of those connections disappears.

An experiment is performed without a clear requirement.

A risk is repeatedly discussed but never tested.

A study produces data without predefined decision criteria.

A CRO completes its scope, but the sponsor still cannot answer the development question.

A decision is made, but six months later nobody remembers the evidence or reasoning behind it.

Good scientific-development systems preserve those connections.

Built for small teams

CogMind is initially focused on early-stage biotechnology, diagnostics, biomarkers, medical technology, and related translational programs.

I am particularly interested in working with organizations approaching high-consequence milestones such as:

• analytical or bioanalytical validation;
• important preclinical or translational studies;
• FDA interactions;
• NIH SBIR/STTR submissions and funded programs;
• CRO or external-laboratory engagements;
• diagnostic verification and validation;
• multidisciplinary development programs that have outgrown the founding team’s available bandwidth.

The objective is not to introduce large-company bureaucracy into a small startup.

Quite the opposite.

Small companies need rigor without unnecessary overhead.

The goal is to determine which decisions matter, what evidence is actually required, and how to create that evidence as efficiently and defensibly as possible.

More than advice

There is also an important distinction between advising a team and helping it execute.

It is easy to say:

The assay needs to be validated.

It is much harder—and more valuable—to help determine:

what the intended use requires,

which performance characteristics matter,

how the experiments should be structured,

what acceptance criteria should be established,

how the CRO should execute the study,

what happens when something unexpected occurs,

and what evidence must exist at the end.

That space between high-level strategy and hands-on execution is where I want CogMind to operate.

Where this can go

CogMind is beginning as a focused, founder-led scientific-development consultancy.

The near-term objective is straightforward: help early-stage companies navigate difficult development problems and build repeatable approaches for doing that work well.

Over time, I expect the model to broaden.

That may mean additional scientists and engineers.

More standardized development frameworks.

Fractional scientific-development teams.

Technical diligence.

Tools for organizing requirements, evidence, and decisions.

Potentially even helping build new technologies and companies rather than only advising them.

But there is no need to start there.

The foundation comes first.

The starting point

For now, the mission is simple:

Help good teams do difficult science more deliberately.

Define the requirement.

Understand the risk.

Design the right experiment.

Preserve the evidence.

Make the decision.

And whenever possible, identify the expensive problem before the expensive work begins.

References

1. U.S. Food and Drug Administration. Formal Meetings Between the FDA and Sponsors or Applicants of PDUFA Products: Guidance for Industry.
2. National Institutes of Health, SEED. Understanding SBIR and STTR.