clairitybio Let’s talk

AI CONSULTING FOR BIOTECH

You know
the science.
I know AI.

I help biotech leaders turn AI possibilities into tools that save their teams time. From choosing the right opportunity to building it—you don’t need to become a technology expert.

Discuss your workflow

Talk directly with Aaron Kravitz. No technical brief needed.

Stanford CS graduate · Former Google engineer

FROM POSSIBILITY TO A PLAN
THE QUESTION THAT MATTERS

Is this worth building?

Map the opportunity. Test the value. Decide with evidence.

Plain-language advice.Hands-on AI expertise.Tools your team can own.

A CLIENT’S PERSPECTIVE

Listening closely.
Finding the next step.

“You are a fantastic listener, in a very specific and analytical way … [you] quickly hone-in on exactly what needed to be done”

Allison AugustExcerpt from feedback on our consulting work

01 / WHERE I CAN HELP

Less AI uncertainty.
More forward motion.

You don’t need another list of possibilities. You need to know what deserves your team’s time.

01

Find the right
starting point.

“We know there’s an opportunity. We’re not sure where.”

Map the repetitive work, decision bottlenecks, and data constraints in your workflow.

WHAT YOU GET

A ranked list of opportunities, a recommendation to build or buy, and a plan for a first test.

02

Test the value.
Before you scale.

“We have an idea. We need to know if it works.”

Build a focused pilot around a real task, with human review and a baseline to compare against.

WHAT YOU GET

A working pilot, an evaluation against agreed success criteria, and a go / revise / stop recommendation.

03

Make it useful.
Make it yours.

“The demo works. Now our team needs to use it.”

Bring the tool into the workflow, document its limits, and help the people using it get comfortable.

WHAT YOU GET

A practical handoff: documentation, team enablement, and a plan for ownership and oversight.

02 / A SMALLER, SMARTER FIRST STEP

One workflow.
One clear decision.

Start with a focused opportunity assessment. We’ll choose one workflow and work out whether AI belongs in it.

  1. 01

    Understand the work

    Identify the users, current process, bottleneck, and constraints.

  2. 02

    Define what better looks like

    Agree on a baseline, acceptable quality, and the outcome that would justify a pilot.

  3. 03

    Choose the next move

    Get a recommendation and a scoped plan. Implementation is a separate decision.

Scope, timing, and fees are agreed before work begins.

A LOOK AT THE THINKINGIllustrative example

Should we pilot
AI-assisted literature triage?

A sample decision framework—not a client result.

The friction
Scientists spend time sorting papers before they can assess the evidence.
The small test
Compare assisted triage with the current process on a defined set of papers.
The measures
Review time, missed relevant papers, and the effort needed to correct the output.
The guardrail
Scientists review the evidence. Generated summaries are not treated as verified findings.

The decision: Continue only if the time saved is worth the review burden and quality meets the agreed threshold.

03 / YOUR CONSULTING PARTNER

Hi, I’m Aaron Kravitz.
Your AI partner.

I’m a Stanford CS graduate, former Google engineer, and AI practitioner with 10+ years building software and machine-learning systems.

I’ve led data science teams and built AI products, including a wet-lab platform at Codifi. Through Clairity Bio, I work directly with biotech experts who want a technical partner to explain the options and do the building.

The work starts with your problem. That may lead to a pilot, a simpler tool, or a decision that AI isn’t the right investment.

More about my background

Based in California · Working with teams across the US

SELECTED EXPERIENCE

Scientific context.
Real implementation.

BIOTECH CONSULTING

Comanche Biopharma

Evaluated digital-twin and synthetic-control methods for clinical-trial planning, including work on the sFlt-1 program.

Consulting experience · Clinical development

PRODUCTION AI

3,000+ hours saved annually

As Head of Data Science at Net Purpose, built an automated fact-extraction platform that eliminated manual processing and reduced operating costs by 20%.

Prior role · Results at Net Purpose, not a biotech client outcome

BIOMEDICAL RESEARCH

600,000 PubMed abstracts

Built an information-extraction system over biomedical literature as an undergraduate researcher in Professor Russ Altman’s lab at Stanford.

Research experience · Biomedical information extraction

A FEW PRACTICAL QUESTIONS

Before we talk.

Is this right for our team?

This is a good fit for biotech teams with a specific workflow bottleneck, an AI idea to evaluate, or a build-vs-buy decision to make. You don’t need a finished brief—just a problem and someone who understands the current process.

Do we need to know exactly what to build?

No. The assessment is designed to clarify that. We can start with the task that takes too much time or the decision your team is struggling to make.

How do scope and pricing work?

We’ll discuss the problem first, then agree on deliverables, timing, and fees before an engagement begins. An initial assessment doesn’t commit you to a larger implementation.

What about confidential or sensitive data?

Keep your initial inquiry high level. Before project data is shared, we’ll agree on confidentiality, access, permitted tools, and any security requirements. A pilot should be designed around those constraints from the start.

LET’S FIND YOUR NEXT STEP

What’s slowing
your team down?

Tell me a little about the workflow and what you’d like to improve. I’ll follow up by email to explore fit and, if it makes sense, arrange an introductory conversation.

No polished brief needed. Please leave out confidential details.

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