QuantumLayer Earns a 118 Proof of Usefulness Score by Building a Developer-Facing Risk Intelligence Engine

In this interview, we catch up with Robert Esentals to discuss QuantumLayer, a developer-facing risk intelligence engine. This project translates climate and infrastructure uncertainty into actionable data for system integration, testing, and failure analysis.

What does QuantumLayer do? And why is now the time for it to exist?

QuantumLayer is a developer-facing risk intelligence engine that helps translate climate and infrastructure uncertainty into something systems can actually work with. It’s designed for integration, testing, and failure under real-world constraints — not for dashboards or slideware. Now’s a good time for QuantumLayer to exist because technical systems and infrastructure must increasingly operate under climate uncertainty, requiring tools that treat risk as an integration problem rather than just a reporting metric.

What is your traction to date? How many people does QuantumLayer reach?

Currently reaching a small group of early technical users, collaborators, and pilot discussions (tens to low hundreds per month). The project prioritizes depth of use and real integration over broad visibility at this stage.

Who does your QuantumLayer serve? What’s exciting about your users and customers?

QuantumLayer is for developers, researchers, infrastructure practitioners, and public-sector teams working with systems exposed to climate, environmental, or long-term risk. It’s especially relevant where decisions must be made despite uncertainty, incomplete data, and real consequences.

What technologies were used in the making of QuantumLayer? And why did you choose ones most essential to your techstack?

QuantumLayer utilizes custom data models and an API-first architecture to manage complex geospatial and temporal data. The team consciously prioritized explainability and interoperability over proprietary tooling to ensure the system supports robust scenario modeling in diverse production environments.

What is traction to date for QuantumLayer? Around the web, who’s been noticing?

Traction is currently defined by exploratory integrations and pilot-oriented use cases rather than vanity metrics. Feedback from these early technical discussions is actively shaping the data models and interfaces to ensure they function in real decision contexts.

QuantumLayer scored a 118 proof of usefulness score (proofofusefulness.com/quantumlayer-report)

What excites you about this QuantumLayer's potential usefulness?

The most interesting potential is whether risk and uncertainty can become something systems and developers actively work with, rather than abstract concepts handled after the fact. If useful, QuantumLayer helps decisions be stress-tested against reality before consequences arrive.

What would say is your most concrete evidence of usefulness?

Our strongest proof of usefulness is not user count but decision influence. In an early pilot discussion with a Nordic municipality working on climate adaptation planning, our risk engine was used to compare two infrastructure maintenance strategies under different climate stress scenarios. The outcome directly changed which assets they prioritized for inspection and budget allocation. That moment, when our model altered a real-world decision,  is the clearest signal that QuantumLayer is genuinely needed.

How do you measure genuine user adoption versus "tourists" who sign up but never return? What's your retention story?

We deliberately avoid open sign-ups at this stage. Every user is onboarded through a specific pilot, workshop, or institutional dialogue. Adoption is measured by repeat scenario runs, parameter adjustments, and follow-up questions,  not logins. A “real” user returns to the system to test a decision again, with new constraints. If that doesn’t happen, we don’t count it as adoption.

If we re-score your project in 12 months, which criterion will show the biggest improvement, and what are you doing right now to make that happen?

The criterion that will improve most is Depth of Use. Over the next year, we are formalizing pilot frameworks with municipalities and infrastructure owners, embedding QuantumLayer into their planning cycles rather than treating it as a one-off tool. We’re currently investing in better auditability of assumptions and clearer scenario-to-action mapping to support this.

How did you hear about HackerNoon?

I came across HackerNoon through the Proof of Usefulness initiative and immediately appreciated its focus on substance over hype. The process has felt unusually respectful to early-stage builders, focused on learning, reflection, and real impact rather than growth theatre.

You mentioned focusing on pilot-oriented use cases. Can you share a specific example of a pilot discussion that significantly altered your roadmap or data model?

A discussion around heritage-listed buildings exposed a gap in our data model: traditional risk models ignore cultural and emotional value. This led us to extend QuantumLayer to support non-monetary impact layers, allowing stakeholders to weigh social, cultural, and historical loss alongside physical risk.

Since you prioritize depth of use over broad visibility right now, what defines a "successful" user interaction for you at this early stage?

Success is when a user pauses, reflects, and says: “I hadn’t thought about it that way,  let’s test another scenario.” That moment of reframing is more important to us than scale at this stage.

In the context of climate and infrastructure, how does QuantumLayer ensure that its "risk intelligence" remains actionable rather than just theoretical modeling?

QuantumLayer avoids abstract outputs. Every risk assessment is tied to a concrete choice: delay, reinforce, relocate, or monitor. We design outputs for workshops and decision meetings, not dashboards alone. If a result cannot be acted upon in a planning or budget context, we treat it as incomplete.