Every venture capitalist I meet eventually asks the same thing: “But how do you know it works?”
It’s a reasonable question. Measuring emotion is audacious. It challenges the very foundation of how we think about human experience. Investors want to see validation. They want certainty.
Here’s the paradox: in AI, certainty doesn’t exist. Accuracy isn’t an endpoint – it’s a journey.
Why Agility Beats Perfection
The most disruptive technologies didn’t wait to be perfect. Google didn’t launch with perfect search. Tesla didn’t launch with perfect autopilot. They launched early, learned fast, and improved relentlessly.
Emotion AI is no different. Our models started by identifying four emotional quadrants. As our dataset grew, they recognised 16. Soon, they’ll recognise hundreds, maybe thousands. Every day, every user, every data point makes the model smarter.
This is where traditional VC thinking often fails. They want to invest in something finished, risk-free. But that’s not how category-defining technologies are built. They are built by companies that are willing to learn in public, iterate in real time, and let the network effects of their data flywheel do the work.
Our Secret Weapon: Infrastructure
Some investors think the moat is the algorithm. It’s not. Algorithms can be copied. What can’t be copied is the ecosystem.
We’ve built our system around federated learning, meaning our model learns from the edge without ever pulling raw data into a central server. Combine that with blockchain-based biometric authentication, and you have a system that is not only more secure but also self-reinforcing: the more it’s used, the more intelligent it becomes.
And here’s the kicker: if competitors copy our open-source algorithms, they’re feeding our model. We become the standard by design.
Addressing the Accuracy Question
Back to the objection: how do we know it works? We validate against the gold standard—EEG data and referenced emotional stimuli—while simultaneously scaling into real-world environments. The lab proves the theory. The field proves the product.
The irony is that the competitors who insist on language models will never achieve this fidelity, no matter how much data they scrape. Why? Because they’re starting from the wrong end of the signal. They’re trying to infer emotion from thought, while we measure it at the source.
The Ethical Question – and Our Answer
There’s another objection, one few investors voice but many think: If you can measure emotion this precisely, can’t you also manipulate it?
The answer is yes. And that’s exactly why we need to design differently. We have strict design principles: sovereignty over addiction, integrity over extraction, organic intelligence over disembodiment, truth-seeking over comfort.
Where other companies use data to control, we use it to empower. We’re building tools that increase agency, not reduce it. The goal is not to make people dependent on inTruth, but to help them regulate their own emotions and make better decisions. The mission is empowerment, not control.
The Path to Becoming the Standard
Our go-to-market reflects this philosophy. Instead of rushing into the consumer market, we’re starting with high-value B2B environments – mental health, defence, first responders, corporate leadership – where the stakes are high and the need for objective emotional data is urgent. These controlled rollouts allow us to build credibility, refine our models, and generate revenue while we prepare for a consumer launch.
Once we reach scale, we won’t just be another wearable app. We’ll be the default emotional intelligence layer across industries, platforms, and devices.
The Takeaway for Investors
Some investors want to back something safe. Others want to back something that changes the game. If you’re looking for the next incremental improvement, inTruth isn’t for you.
If you’re looking for the next standard – something as inevitable as GPS, Wi-Fi, or the smartphone…we’re building it.
If this resonated with you:
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