I’m part of the team building Sniphi.
Sniphi is a modular digital nose that uses gas sensors and machine-learning models to convert volatile organic compound (VOC) data into a machine-readable signal that can be integrated into existing QA, monitoring, or automation systems. The system is currently in an R&D phase, but already exists as working hardware and software and is being tested in real environments.
The project grew out of earlier collaborations with university researchers on gas sensors and odor classification. What we kept running into was a gap between promising lab results and systems that could actually be deployed, integrated, and maintained in real production environments.
One of our core goals was to avoid building a single-purpose device. The same hardware and software stack can be trained for different use cases by changing the training data and models, rather than the physical setup. In that sense, we think of it as a “universal” electronic nose: one platform, multiple smell-based tasks.
Some design principles we optimized for:
- Composable architecture: sensor ingestion, ML inference, and analytics are decoupled and exposed via APIs/events
- Deployment-first thinking: designed for rollout in factories and warehouses, not just controlled lab setups
- Cloud-backed operations: model management, monitoring, updates run on Azure, which makes it easier to integrate with existing industrial IT setups
- Trainable across use cases: the same platform can be retrained for different classification or monitoring tasks without redesigning the hardware
One public demo we show is classifying different coffee aromas, but that’s just a convenient example. In practice, we’re exploring use cases such as:
- Quality control and process monitoring
- Early detection of contamination or spoilage
- Continuous monitoring in large storage environments (e.g. detecting parasite-related grain contamination in warehouses)
Because this is a hardware system, there’s no simple way to try it over the internet. To make it concrete, we’ve shared:
- A short end-to-end demo video showing the system in action (YouTube)
- A technical overview of the architecture and deployment model: https://sniphi.com/
At this stage, we’re especially interested in feedback and conversations with people who:
- Have deployed physical sensors at scale
- Have run into problems that smell data might help with
- Are curious about piloting or testing something like this in practice
We’re not fundraising here. We’re mainly trying to learn where this kind of sensing is genuinely useful and where it isn’t.
Happy to answer technical questions.
[1] There are digital sensors that are readable directly from the pilothouse by the captain which are rigged to automated alarms, as well as manual sensors (e.g. a pressure dial) that are readable from the engine room itself, for redundancy. So I don't think an olfactory sensor would replace the unusual smell check, but it could maybe augment it.
[2] The "bilge pump" is used to pump out water from the bilge (bottom floor cavity of engine room). To be honest on my vessel the policy is to never turn on the bilge pumps in the engine room at all because the risk of dumping contaminants is too high. But I still thought to mention this just in case there's an idea there.
I have a friend with Chrons, IBS, and a handful of other gut issues. He wants me to build something like this to help self-diagnose acute issues as they arise. Yes, a fart classifier.
I want to use a smell classifier to identify ripeness levels in agriculture.
I haven’t tested to see if this is even feasible, but I’d like to also use a tool like this for pest scouting in agriculture. If the sensors are sensitive enough to detect small amounts of fungi, arthropod activity, or hormonal shifts, this could be useful for early detection in integrated pest management systems.
When we published the white paper ( https://sniphi.com/wp-content/uploads/2025/10/Sniphi_digital... ), we expected a queue of agricultural companies interested in the technology. However, pests apparently aren’t “sexy” enough to capture attention.
We observed the same reaction with bananas — fresh vs. overripe, like in the video. Technically interesting, but no one saw clear business potential.
So now we are looking for use cases that are more obvious and compelling from a business perspective. Any ideas?
How good are digital smellers compared with super human smellers?
Digital smellers are scalable and more repeatable than human noses. At the current stage our electronic nose operate either through classification of previously trained odor classes or through anomaly detection. What is still missing is a possibility to run a more sophisticated conversation with the model when something smells "suspicious".
Some thoughts are musty odors from mold/mildew, rotten egg smells indicating gas leaks, and fishy/burning plastic odors from electrical issues.
A mold detector is also an interesting idea. Our ‘digital nose’ can measure humidity and temperature as well, and these factors are often strongly correlated with mold growth. Combining odor detection with environmental data could therefore be very useful for early mold detection.
What is the limit of detection on the sensors? Can they reliably pick up compounds in the parts per billion range? Parts per trillion?
On the limits of detection - with Sniphi we follow a different approach than traditional selective sensors. The system is based primarily on non-selective chemical sensors operating at controlled temperature profiles. Each measurement cycle (6 seconds) generates around 60 measurement points per sensor, creating multidimensional signatures of gas mixtures that are then analyzed using classification models.
We measure both humidity and temperature and use them as additional inputs for the ML models. Regarding sensor drift, it is still difficult to fully assess its impact on the business case. At this stage, our main focus is on the accuracy of the classification models rather than very long-term operation — that would be the next step.
For now, the practical approaches we consider are either on-the-fly calibration through a feedback loop based on the actual process output, or simply replacing the sensor when necessary, as the manufacturing cost is relatively low.”