The Accuracy Paradox

In Silicon Valley, 99%, two nines, is usually a number to celebrate.

If you build a photo-sharing app with 99% uptime, you are a reliability hero. If you train a Generative AI model that answers prompts correctly 99% of the time, you are ahead of the curve. In the world of consumer SaaS, "good enough" is often synonymous with "great," provided you ship it fast enough to capture the market.

After leaving the robust, highly structured environment at Microsoft to establish Nēdl Labs, I encountered a distinct set of challenges.

We operate to solve the healthcare payment integrity, a critical yet often underrecognized domain that ensures providers are reimbursed appropriately, claims are processed accurately, and patients receive timely prior authorization for medically necessary treatments.

By addressing these issues in real time rather than over several weeks, we help prevent scenarios in which patients may delay or ration their care. Additionally, we support payers in managing Medical Loss Ratio (MLR) challenges, ensuring payments are accurate and timely.

In our world, 99% isn't a victory. It's a starting point for a challenging conversation.

If a large payer processes 100 million claims a year, and your AI model is "99% accurate," you have just wrongly adjudicated one million claims. That is one million providers facing payment delays. One million administrative headaches. One million points of friction in a system that demands precision.

This is the Accuracy Paradox.

As a startup founder, I am constantly bombarded with the gospel of "Move Fast and Break Things." Investors want velocity. The market wants speed. But as a leader in a zero-error industry, I face a critical challenge: How do you maintain the agility of a startup while operating with the rigor required for critical infrastructure?

The answer lies in a concept I call Selective Perfectionism, and it mirrors the exact technology we are building.

The Microsoft Imprint: Trust as an Absolute

My 19+ years at Microsoft didn't just teach me how to ship software; it rewired my understanding of trust.

When you work at Microsoft scale, building platforms that power global enterprises, you learn quickly that trust is binary. You either have it or you don't. There is no "mostly trusted." In the enterprise world, a feature that works "most of the time" is often viewed as a liability.

I carried this DNA with me to Nēdl Labs. I knew that while we needed the speed of a startup, we couldn't afford to be reckless. The "Big Tech" lesson wasn't about bureaucracy; it was about the discipline of reliability. I learned that enduring value is built on a foundation of trust, not just speed.

However, I also realized I couldn't simply copy and paste the Microsoft playbook. I don't have thousands of engineers, data scientists, or endless QA cycles.

I had to find a way to achieve enterprise-grade reliability on a startup budget, without suffocating our ability to innovate.

Neuro-Symbolic Leadership

At Nēdl Labs, our key differentiator is Neuro-symbolic AI.

In simple terms, we combine the creative, probabilistic power of Compound AI (the "Neural" brain) with the rigid, deterministic rules of traditional logic (the "Symbolic" brain). The AI reads the messy, unstructured medical notes; the Logic engine enforces strict payment policies with reasoning.

We operate in two distinct zones:

1. The "Probabilistic" zone prioritises speed and experimentation.
2. The "Deterministic" zone demands accuracy and precision.

Zone 1: The Probabilistic Playground (Move Fast)

In these areas, we embrace experimentation. We aim for progress over perfection because speed is the currency. We operate with the "growth mindset" I saw championed at Microsoft, but applied to:

1. UI/UX Experiments: If the dashboard layout isn't perfect yet, we iterate.
2. Internal Tooling: If the script we use to clean data is rough but effective, we use it.
3. Marketing & Messaging: We test different narratives to see what resonates.

In the Probabilistic zone, I encourage the team to take calculated risks. If they aren't trying new things here, we aren't innovating enough.

Zone 2: The Deterministic Standard (Zero Error)

In these areas, we operate with the absolute rigor I learned in Redmond. "Move Fast" is replaced by "Verify Twice."

1. Claims Adjudication: We never guess on claims edits, repricing, and allowed amounts.
2. PI, HIPAA: Security is not a feature; it is the foundation.
3. Policy: If the policy has complex rules, we will provide a rationale for the rules and explain why the action is taken.

Leading in the High-Stakes Zone

One of the most challenging aspects of serving as a founder and CEO in the current environment is navigating shifts in perspective. I may advise an engineer that it is acceptable to push a policy extraction in a demo, even if it misses a rule. Still, in production, we must have rigorous validation and review to ensure 100% coverage and zero hallucinations.

To manage this, we use three principles:

1. Explicit Risk Budgeting

We don't just build features; we conduct risk assessments and budget for zones. I explicitly tell the team: "We have a high-risk budget for this demo UI. We have a zero-risk budget for the data ingestion pipeline." By defining the budget upfront, I give them permission to sprint where it's safe and be methodical where it's critical.

2. The "Governor" Role

In a traditional startup, the CEO is the accelerator. In a Deep Tech healthcare startup, the CEO must also be the governor. My role isn't just to push for velocity; it's to ensure that velocity never compromises our standards. I am the one who validates that we are moving at the right speed for the right task.

3. Boring is Beautiful

We celebrate "boring" code. In the Probabilistic Zone, we reward cleverness and novelty. In the Deterministic Zone, we reward predictability and clarity. If an engineer writes a piece of logic that is so simple and robust that it cannot possibly be misunderstood, that is a win.

Solving the Paradox

The "Accuracy Paradox" challenges founders who try to apply a single mindset across the entire company.

If you apply "Move Fast" to healthcare compliance, you risk losing customer trust. If you use "Zero Error" on your marketing website, you risk losing momentum in the market.

My experience at Microsoft underscored the importance of trust, while my tenure as a founder highlighted the significance of agility. The key is not to prioritize one over the other, but rather to clearly identify and communicate to your team which approach is most appropriate for the current context.

We are building the future of healthcare payments. That future needs to be innovative, yes. But more importantly, it needs to be right.