Teaching machines to smell

From Model Lab to Agent Lab

This week I confronted a hard question: does Neosmia need to build the foundation model first, or can we capture value now while building toward it? The answer changes everything.

The shift is to stop thinking like a model lab and start thinking like an agent lab. Instead of waiting to train the perfect smell model, we build specific decision-making agents that work today with existing sensors and ML pipelines. Each agent solves a narrow, urgent problem. A food freshness routing system that decides which pallets ship first, a quality assurance agent that flags spoilage before loading, an environmental safety monitor that detects VOCs early.

The key insight is that these agents generate the exact data we need to train the foundation model later. Every workflow collects time-series odor traces and labeled outcomes.

This means we can demonstrate value immediately, get paying pilots that fund data collection, and own the workflows where smell matters most. The foundation model is still the long-term vision, but now there is a path to survive long enough to build it.

Accepted into Expedition FEMSA Bioworkshop Cohort

Today I am excited to share that Neosmia has been accepted into the Expedition FEMSA Bioworkshop cohort. This is a huge step forward for us.

Expedition is where entrepreneurs, researchers, and creatives unite to transform ideas into revolutionary solutions. It is a dynamic and collaborative space that drives synergy between industries and society, creating the perfect environment for innovation and change. Being part of this community means access to labs, infrastructure, and a network of people working on ambitious science-driven ventures right here in Monterrey.

What makes this especially meaningful is the timing. We are moving from prototype design into real world testing, and Expedition gives us the resources and setting to pressure test our early hardware and accelerate validation cycles. The cohort will also help us refine our approach as we prepare for the launch of the Innovation and Entrepreneurship Hub in March 2026.

This is the kind of support that turns ambitious ideas into working systems. I am looking forward to what we will build here.

Weekly Update – October 15, 2025

This week we took the system from bench testing to field readiness. Two pilot partners confirmed: one working with avocado ripeness tracking, the other testing olive oil authenticity. Both want structured odor sequences they can tie to quality outcomes, which is exactly what the capture kit was designed for.

The breakthrough came Tuesday. We ran the first stable 10-minute odor sequence with no baseline drift. The ambient correction loop held, the heater stayed within 0.3°C, and the pump delivered consistent flow across the full run. That stability matters because it means the data we collect today will align with data collected next month, which is the foundation for any learning system.

What I learned this week: field deployment is not just about hardware working. It is about partners trusting the data enough to act on it. The avocado grower wants to know if a batch is ready to ship. The olive producer wants to catch adulteration before it reaches buyers. Both need answers they can rely on, not experimental outputs. That shifts how I think about the next phase. It is no longer "can we capture odor?" It is "can we deliver clarity from odor?"

Weekly Update – October 8, 2025

This week the hardware design crossed a threshold. I completed the control loop for the pump and heater, which means the system can now hold airflow and temperature stable during a capture run. That might sound minor, but it changes everything. Without active control, readings drift as room conditions shift. With it, we can compare samples taken hours or days apart.

The firmware now logs ambient corrections in real time: temperature offsets, humidity compensation, and baseline adjustments for each sensor. This creates a traceable record of what the system was doing when it captured each odor signature. If a reading looks strange later, we can go back and see whether the heater spiked or airflow dropped.

What I built this week is not just a sensor array. It is a standardized capture environment. The same kit can sit in a warehouse in Monterrey, a processing facility in California, or a testing lab in Europe, and produce comparable data. That comparability is what makes a shared model of smell possible. Without it, we are just collecting local snapshots that never add up to general knowledge.

Weekly Update – October 1, 2025

This week Neosmia took some tangible steps forward. On the technical front, we've moved from concept sketches into concrete prototype design. The data acquisition architecture is now mapped out, balancing precision channels for chemical sensors with fast capture for dynamic signals. Collaborators have started working on hardware builds and experiment design, which means we're finally setting the stage for our first real datasets rather than simulations or desk work.

The new challenges surfacing now are less about "what to build" and more about how to make the early choices stick. One is prioritization: with so many potential sensor types and test cases, I need to carefully choose the first pilot experiments that can prove value quickly without overextending. Another is coordination: aligning hardware design, data collection, and AI model planning so they advance in sync rather than getting out of phase. Finally, there's the question of framing: while the moonshot vision is clear, I need to translate that into near-term milestones and a validation roadmap that feels real to both partners and potential investors.

Testing Update – September 23, 2025

This week I’ve been diving into experiments with high dimensional, weakly structured datasets. Unlike images or text, these signals don’t come with clear labels or a simple geometry. They exist as messy, overlapping clouds in feature space. Extracting order from them requires techniques that can tolerate noise, sparsity, and shifting conditions at the same time.

Working with olfactory signals in Neosmia made this painfully clear: volatile compounds never behave the same twice, yet hidden in the chaos are patterns that determine freshness, safety, or even identity. What I am seeing now is that the same challenge repeats across other domains. The tools we use for structured or richly labeled data simply do not fit.

My early tests are small, but they confirm the intuition: to make progress we need approaches that embrace high dimensional structure rather than flatten it away. Over the next weeks I will share how this exploration is shaping into a more general path forward.

Company Update – September 15, 2025

Wins

This week we locked down the core hardware plan for Neosmia's prototype. By carefully selecting the right sensors and external ADCs, we now have a clear path to building a system that captures high-quality odor data. We also began conversations with researchers who see the potential in joining Neosmia's journey, signaling that the mission resonates beyond industry.

Challenges

The challenge ahead is focus. While Neosmia's long-term vision is to make machines truly capable of smell, our immediate priority is building a reliable MVP for food supply chains. Making this concrete while preparing the foundation for a moonshot is not trivial. On top of that, funding constraints make every decision critical as each dollar must push us closer to a working demo.

Accepted into Founder University Cohort

We're excited to share that Neosmia has been accepted into the Founder University cohort. This gives us access to a community of builders and mentors as we push forward on the world's first foundation model for smell.

Over the coming weeks we’ll be refining our go-to-market, accelerating data collection, and sharing more frequent updates. If you’re interested in partnering, piloting, or contributing, reach out. I’d love to connect.

Month 1: First Experiments with Produce Freshness

This month we began recording olfactory signals from fruits and vegetables at different stages of freshness. The goal was simple: see if a small sensor array could distinguish fresh from not-fresh, and how those signals change over time.

The early data is both promising and tricky. Simple models can often tell when an item is fresh, but accuracy falls once the same test is repeated under different humidity, temperature, or airflow. A system trained in one context struggles when the context shifts.

We also saw how individual sensors behave very differently: some react quickly but fade, others respond slowly but stay steady. Together they create useful signals, but also plenty of noise. Across a few hundred thousand readings, the picture is clear: there is real signal, but it is fragile.

This is why smell is unlike vision or sound. Odors are deeply entangled with environment, and variability is the rule. Any true model of smell will have to embrace that complexity, not hide from it.

The Missing Sense in AI

Computers can see. They can hear. They can converse in human language. Yet one of the most powerful senses, smell, remains invisible to machines.

Smell shapes life every day. It tells us when food is fresh or spoiled, when danger is near, when a memory is real. It is central to health, safety, and creativity. And unlike vision or language, it has no digital representation.

Neosmia exists to change that. We are creating the world's first foundation model for smell. A universal representation that makes olfaction part of the digital world, accessible, useful, and generative across industries.

Imagine preserving the scent of a place so it can be relived decades later. Imagine drones and satellites mapping ecosystems through their chemical fingerprints. Imagine global supply chains where freshness is monitored at every step, reducing waste and hunger. Imagine culture and media enriched with scent as naturally as sound and color. Imagine exploring another planet and knowing not only what it looks like but what it smells like.

And imagine how people will interact with it. A chef asking if fish is safe to serve and receiving a clear answer. A parent scanning a lunchbox and being told the fruit is fine but the sandwich may be past its best. A city inspector waving a handheld device and discovering a hidden gas leak. A designer requesting the formula for a "spring morning in Kyoto" to embed in VR. A winemaker comparing a new vintage to historic bottles. An artist blending "rain on stone" with "freshly cut grass" for an installation.

This is a long-term project. The ambition is to open a new sensory dimension for artificial intelligence and to unlock applications that today can only be imagined.

Smell is the missing sense in AI. Neosmia will build it.