Closing the Last-Mile AI Gap

Driving real-world results in vision AI for people, planet, and science

Book a Call

More than 80% of AI projects fail - and vision AI is no exception.

Most models succeed in the lab but collapse in the field. Real-world deployment exposes weaknesses:

Fragile data — works on internal sets, but breaks with new sites or scanners
Unreliable models — fine in validation, but fail under drift and edge cases
Lack of trust — bias and opacity undermine confidence
Stalled adoption — pilots excite, but never scale into real-world use

The cost: delays, wasted investment, and lost credibility.

This is the last-mile AI gap — and closing it is what turns research into real-world results.

What changes when your vision AI survives the real world?

Close the last-mile AI gap, and projects don’t stall — they accelerate. Instead of delays and wasted resources, you deliver results that earn trust and move science forward.

Robust and reliable across sites and conditions
Faster validation with fewer costly setbacks
Confidence from executives, investors, and regulators
Teams aligned on ROI, not novelty
Pilots that scale into real-world adoption
A competitive edge while others stall

That’s how vision AI drives progress you can see — for people, planet, and science.

Here are three ways I can help...

Pixel Clarity Call (Free)

A 30-minute conversation to surface risks and uncover opportunities in your vision AI.

Learn more

Risk & Readiness Assessment

A focused review to design new projects against the risks that lead to last-mile failures.

Learn more

Last-Mile AI Gap Diagnostic

A targeted check-in to uncover hidden problems in ongoing projects before they derail deployment.

Learn more

About Heather Couture

Early in my career, I watched computer vision models that looked flawless in validation collapse the moment they faced the real world. Variability exposed weaknesses, trust broke down, adoption stalled — and impact was lost. That gap between research and reality became the problem I’ve spent the last 20 years solving.

I founded Pixel Scientia Labs to help science-driven teams close the last-mile AI gap. My role isn’t to build models for you — it’s to make sure your models are robust, trusted, and actionable by aligning data, modeling, and domain insight.

Clients describe me as a source of clarity and direction — someone who spots risks early, reframes them for executives, and keeps teams focused on what drives impact.

That’s what Pixel Scientia Labs is about: delivering vision AI that survives the real world.