Sennos CEO and Co-Founder Jared Resnick joins CityBiz to discuss how real-time fermentation intelligence is reshaping food and beverage manufacturing — from catching process issues before they impact quality, to democratizing advanced analytics for producers of all sizes. Read the full Q&A.
As food and beverage manufacturers face increasing pressure around efficiency, consistency, traceability, and sustainability, more companies are turning to AI-powered operational intelligence to gain deeper visibility into production environments that have historically been difficult to monitor in real time.
Durham-based Sennos is part of a growing group of technology companies focused on bringing real-time analytics and process intelligence into fermentation and fluid-based manufacturing systems. Following recent funding momentum, the company is expanding its work across brewing, biofuels, alternative proteins, and broader industrial production environments where operators have traditionally relied on periodic sampling and manual oversight to manage highly dynamic processes.
In this conversation, Sennos Co-Founder and CEO Jared Resnick discusses the evolution of AI and sensing technologies in industrial production, the growing importance of operational visibility, and how real-time process intelligence could reshape the future of food and beverage manufacturing.
Sennos recently secured significant new funding. What does this investment signal about the future of AI and real-time analytics in food, beverage, and industrial production?
I think it’s less about AI as a buzzword and more about where the industry is starting to focus.
Companies have digitized a lot around the edges, supply chains, logistics, packaging, but the core production process itself is still largely invisible. That’s where the real opportunity is.
What this funding really signals is that people are starting to pay attention to what’s happening inside the process, not just around it. And once you can see it in real time, you can start to actually control it.
Fermentation and fluid production have historically been difficult to monitor in real time. What opportunity did you see in this challenge, and why is the market ready for change now?
Fermentation has always lived somewhere between science and intuition. And that works… until you need to scale. What we saw was a gap between what producers wanted to know and what they could actually measure. There was just too much happening inside the process that they couldn’t see in real time.
Now the timing is different. Sensors are better, computing and data is cheaper, and there’s more pressure to be consistent and efficient. The industry has reached a point where the old way of doing things just doesn’t scale the same way anymore.
Sennos is often described as bringing visibility to previously “black box” production processes. In practical terms, what does that mean for manufacturers and producers?
For most producers, the tank has historically been a black box. You put ingredients in, monitor a few external variables, and then wait to see how it turns out. What we’re doing is opening that up. You can actually see how the process is evolving in real time, with a depth of understanding that was previously not possible. That means fewer surprises and catching issues earlier. And it means being able to make adjustments before something goes off track, instead of reacting after the fact.
How are AI and sensor technologies reshaping traditional industries like food and beverage, and where do you see the greatest opportunities for innovation?
What’s interesting is that AI isn’t replacing expertise. This has been a long-time discussion with any new technologies. AI is helping and amplifying the process. These industries have decades of knowledge built into them. What’s been missing is the ability to see and quantify what’s actually happening inside the complex processes. The biggest opportunities are in areas that have historically been hard to measure — fermentation, fluid systems, biological processes. The biggest shift isn’t in the data itself, but it is in moving from readings to answers. That’s where better data can really change outcomes.
Beyond quality control, how does improved process visibility impact broader business priorities like profitability, sustainability, and supply chain resilience?
Quality is the obvious starting point, but once you have better visibility, everything else starts to move. You reduce waste because you catch issues earlier, you start to improve yields, and you make better use of raw materials. All of these things have a direct impact on profitability, but also on sustainability and supply chain reliability. Better process understanding leads to better business outcomes — it’s that direct.
Many industries are still trying to determine where AI creates real operational value versus hype. Where do you believe AI is already delivering measurable impact today?
The real value is in pattern recognition and early detection. AI is very good at identifying when something is starting to drift, even if it’s subtle. That’s where we’re seeing impact today and helping operators catch issues earlier. We are able to help operators and brewers understand what’s driving variability and make more informed decisions. Deploying and utilizing AI technologies is not about replacing people. It is about giving people better tools to do their jobs.
You have mentioned in your press releases that Sennos’ applications extend beyond brewing into areas like biofuels and alternative proteins. How do you evaluate expansion opportunities while staying focused on core innovation?
We try to stay grounded in the problem we’re solving, which is understanding and optimizing complex fluid processes. Brewing is a great starting point, and it is where we have success with customers, but the underlying challenges exist in other areas like biofuels and alternative proteins. When we look at expansion, it’s less about chasing new markets and more about asking, “does our approach solve real problems?” If the answer is yes, then it’s worth exploring. Brewing is where we have our deepest customer relationships and most mature deployments — but the underlying challenges exist across fluid-based production, and we’re actively exploring those opportunities.
What leadership lessons have you learned while scaling a company like Sennos?
One of the biggest lessons we have learned is to work closely with our customers. It’s easy to get caught up in technology, and of course innovation is important. But at the end of the day, we are solving real-world problems for people running real operations.
The other is being comfortable with uncertainty. When you’re building something new, there’s no playbook. You have to listen, adapt, and keep moving forward even when things aren’t perfectly defined.
As industries face growing pressure around transparency, efficiency, and compliance, especially in the food and bev industry, how do you see operational intelligence evolving over the next five years?
I think we’re moving toward a world where real-time visibility is the baseline. Over the next five years, we expect to see deeper integration between sensing, analytics, and automated decision-making across food and beverage production. It won’t just be about collecting data, but it will be about turning that data into actionable insights that operators can use in the moment. Transparency and traceability will also become much more important as regulatory and consumer expectations continue to rise. And of course, there is the use of technology for safety and helping beverage manufacturers strengthen safety protocols, detect contamination risks earlier, maintain consistent conditions, and ensure product integrity from tank to shelf.
Can you explain how your approach differs from traditional monitoring methods, and why that level of visibility is important for fermentation-based processes?
Traditional monitoring has historically been built around periodic snapshots. You pull a sample, run lab analysis, and then make decisions based on what the process looked like at a single point in time. The challenge is that fermentation and fluid processes are incredibly dynamic, and a lot can change between those checkpoints.
Our approach is continuous because we are capturing multi-parametric data directly from inside the process itself, in real time, so operators can see how conditions are evolving as they happen instead of trying to reconstruct what happened after the fact.
That level of visibility changes the nature of decision-making. Instead of reacting to problems after they’ve already impacted quality or yield, producers can identify subtle shifts much earlier and make adjustments proactively. In fermentation, where variables like temperature, pressure, dissolved oxygen, and pH are all interconnected, even small changes can create downstream effects.
The other important piece is about context. Real-time data doesn’t just tell you where the process is, but it helps explain where it’s trending. That’s a fundamentally different capability. You move from isolated measurements to a living view of the process, and that ultimately creates more consistency, less waste, a safer product and production and better operational control.
As you collect more fermentation and fluid process data across different environments, what makes these datasets uniquely valuable, and how do they translate into better decision-making for producers over time?
What makes the data valuable is not just the volume, but it is what is inside the data.
Fermentation is influenced by a huge number of interconnected variables – things like ingredients, environmental conditions, operator decisions, equipment differences, timing, and biological variability. When you start collecting continuous data across different systems and production environments, patterns begin to emerge that simply aren’t visible through manual observation or isolated testing. We’re now drawing on more than 26,000 fermentations — and that foundation is what makes the pattern recognition meaningful rather than theoretical.
Over time, that creates a much deeper understanding of what actually drives successful outcomes and what tends to introduce variability or risk.
For producers, that has a practical impact and helps them understand why one batch outperformed another, why certain conditions consistently create inefficiencies, or why quality drift occurs under specific circumstances. That knowledge becomes incredibly valuable because it allows producers to move from reactive troubleshooting toward predictive decision-making.
As these datasets grow, the system becomes more intelligent. It can identify early warning signs, benchmark performance across facilities or batches, and surface signals that help operators act earlier and make more informed decisions.
Ultimately, the value is in turning process knowledge that used to live mostly in experience and intuition into something measurable, repeatable, and scalable.
Looking ahead, what is your broader vision for how Sennos can transform not just fermentation, but the future of food and bev overall?
At a high level, our vision is really about bringing more intelligence and visibility into processes that have historically been difficult to understand or manage in real time.
Fermentation is one example, but there are many areas across food and beverage production where manufacturers are still operating with limited visibility into what’s happening inside complex biological or fluid systems. In many cases, teams are still relying heavily on manual sampling, delayed lab analysis, and operator experience to make critical decisions.
We think that changes over the next decade. As sensing technology, real-time analytics, and AI continue to evolve, manufacturers are going to have a much more connected and dynamic understanding of their operations. That has implications far beyond quality control. It affects efficiency, sustainability, resource utilization, consistency, food safety, and ultimately profitability – so many different areas.
One of the things that excites me most is the ability to democratize these capabilities. Historically, advanced process intelligence was only accessible to very large producers with significant budgets and infrastructure. We believe these tools should be available to companies of all sizes, including smaller and mid-sized producers that drive so much innovation in food and beverage.
Long term, I think the industry moves toward a model where production environments become far more predictive, adaptive, and data-driven. Instead of reacting to issues after they happen, operators will increasingly be able to anticipate outcomes and optimize processes in real time.
That’s the bigger shift we’re working toward.
Originally published by Win Warfield, citybiz, May 27, 2026.