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It’s easy to get caught up in the news and activities of the industry’s global giants, but what about the smaller firms pushing boundaries with bold ideas? In this instalment of Start-up of the Month – which celebrates lesser-known companies and their innovations – we speak to Paulo Gaspar, CEO Brainr, a European start-up focused on digitising food manufacturing.

Can you begin by introducing Brainr, highlighting the reason for its inception and its core objectives?

Brainr was founded in 2023 by a team of senior executives who spent over two decades leading digital transformation across some of Europe's largest food manufacturing groups. One of our co-founders managed operations spanning more than 30 different ERP systems and over 200 software applications simultaneously. In that environment, we saw firsthand how fragmented tools created data silos, delayed decision-making and frustrated teams on the factory floor.

What became clear was that existing MES solutions were either too rigid, too expensive or simply not built for the realities of food production – where recipes change daily, lines switch between products constantly and compliance requirements are non-negotiable. The food and beverage industry was stuck with paper checklists and Excel spreadsheets not because they preferred it, but because digital alternatives failed to deliver.

So we set out to build something different: a cloud-native, fully containerised manufacturing execution system/manufacturing operations management (MES/MOM) platform running on Amazon Web Services (AWS) that could unify every part of a food operation, from raw material intake to finished goods dispatch, regardless of complexity or scale. Our mission is to become the digital 'brain' of the food industry, giving every factory the same level of intelligence and agility that tech companies take for granted.

Your platform aims to build the digital brain for every food factory. What does that mean in practice and how does it transform day-to-day operations on the factory floor?

Brainr started by solving a pain we knew intimately: the hours wasted every day manually entering production data into SAP. Operators would finish a shift, then spend another hour filling out spreadsheets and SAP transactions. We built Brainr to capture that data digitally in real time, cutting data entry time by over 90% at our first client.

But we didn't stop at data capture. We added intelligence. Brainr now track yield deviations in real time, flags quality issues as they occur, and automatically generates traceability reports that used to take days. On the shop floor, supervisors see live dashboards showing every line's status, every batch's progress and every operator's activity. If a CCP threshold is breached or a product needs to be held, the system alerts the right person instantly.

What this means in practice is that a production manager at one of our poultry clients can now track 600,000 birds per day across eleven different species, manage Halal and Organic certifications simultaneously, and pull a full traceability report in under 60 seconds, something that previously required a team of three people and half a day of work.

Brainr has achieved remarkable traction in just two years, now managing 25% of Portugal's meat production. What do you think has been the key to scaling so quickly in such a conservative industry?

The key is that we've been on the other side of the table. Before Brainr, we were the ones being pitched software that promised the world but broke during peak production hours. We were the ones managing projects that took two years to implement and still didn't work properly. We know exactly what goes wrong and why factories are sceptical.

That experience shaped how we built Brainr. It's cloud-native and containerized, which means we can deploy a new client in weeks, not years. It integrates seamlessly with existing ERP systems like SAP, so factories don't have to rip and replace. And because it's designed for the realities of food production, where lines run 24/7 and downtime costs thousands per minute, it's built for resilience and uptime from day one.

But the real accelerator has been results. When Campoaves reduced their quality reporting time by 95%, word spread. When AviSabor cut dispatch errors by over 95% and halved warehouse holding time, their competitors noticed. In a conservative industry, nothing sells like proof.

Many food manufacturers still rely on paper-based systems. What are the biggest barriers to digital adoption you've encountered, and how do you convince companies to make the leap?

The biggest barrier isn't resistance to change. it's bad experiences with previous attempts. Many of our clients have tried digital transformation before. They invested hundreds of thousands of euros in MES systems that took two years to implement, required constant IT support, and still didn't give them the visibility they needed. So when we show up, the first question is always: "Why will you be different?"

Our answer is simple: we show them. We don't start with a 12-month implementation plan. We start with one pain point, maybe it's digitising a paper HACCP checklist, or eliminating manual yield calculations, and we solve it in a week. The quality manager pulls up their phone, sees real-time CCP monitoring, and realizes they no longer need to walk the floor with a clipboard.

Once they see that first win, adoption accelerates naturally. At one client, we started with just the slaughter line. Within three months, they asked us to roll out to cutting, deboning, and packaging. Within six months, we were managing their entire operation. People don't resist change when the change makes their job easier.

You've seen good results across partners like Campoaves and AviSabor. Could you share a specific example of how Brainr's technology improved efficiency or traceability in measurable terms?

At Campoaves, a leading free-range chicken producer, the challenge was scale and complexity. They process over 100,000 birds daily across several different species, with multiple certifications including Halal, Organic and IFS Food. Before Brainr, their team spent hours every day manually entering production data into SAP, and generating a traceability report for an audit could take up to two days.

With Brainr, we automated 92% of that data entry.

Every bird that enters the facility is tracked digitally from live reception through slaughter, processing, and dispatch. CCP monitoring is automated, quality checks are mobile, and traceability reports are generated instantly. When a retailer requests a batch trace, Campoaves can now pull the full chain, including supplier, lot numbers, temperatures, operator IDs and quality results, in under 60 seconds. That's a 95% reduction in reporting time.

At AviSabor, the challenge was different: integration with automated equipment. They run high-speed Marel lines that process thousands of kilograms per hour, and any disconnect between the factory floor and the ERP creates chaos, mislabelled products, wrong shipments, inventory discrepancies. We integrated Brainr directly with their Marel systems, creating a real-time bridge between production and SAP. The result? Dispatch errors dropped by over 95%, and average warehouse time for fresh products was cut in half. They now ship to major European retailers with near-zero mistakes.

The €11 million seed round you raised is the largest of its kind for food manufacturing digitalisation. How will this funding accelerate your next phase of growth and product development?

The funding allows us to do three things: deepen the product, scale the team, and expand internationally.

On the product side, we're already aggressively working the most factory relevant AI products while enlarging our third-party ecosystem, ie. standard integrations with ERPs, equipment manufacturers and other softwares. Right now, Brainr tells you what's happening in real time. Soon, it will predict yield deviations before they happen, recommend optimal production sequences based on demand forecasts, and automatically adjust schedules when equipment fails or raw material quality shifts.

On the team side, we're scaling our implementation and customer success functions. As we grow, we're committed to maintaining the same level of proximity and attention that made our first clients successful. That means hiring more engineers who understand food production, more project managers who've worked on factory floors, and more support specialists who can solve problems in real time.

And internationally, we're ready to move. The challenges we solve in Portugal, fragmented systems, paper-based processes, compliance complexity, are universal. We're already in conversations with producers in Spain, France and beyond. The funding gives us the runway to build local teams and partnerships in those markets.

With AI playing a growing role in manufacturing, how do you balance automation with the need for human expertise and oversight in food production?

We believe in augmented intelligence, not replacement. Food production is too variable, too contextual, and too dependent on human judgment to be fully automated. A machine can tell you that yield dropped by 3% on Line 2, but it takes an experienced supervisor to know whether that's because of raw material quality, equipment calibration, or operator training.

What BRAINR does is give that supervisor better information, faster. Instead of discovering the yield drop at the end of the shift when it's too late, they see it in real time and can intervene immediately. Instead of guessing which batches might be affected, they see the full trace instantly. Instead of manually calculating optimal product mix, the system recommends the best allocation based on current demand and inventory.

We've also built safeguards into the system. Critical decisions, like releasing a hold, approving a deviation, or overriding a CCP alert, always require human confirmation. AI can suggest, but people decide. That balance is essential in an industry where a single mistake can result in a product recall or food safety incident.

You're now preparing to expand beyond meat into sectors like bakery, confectionery, and beverages. What similarities or differences do you anticipate between these industries when it comes to digital transformation?

The core challenges are universal: variable processes, strict compliance, constant product changes, and the need for full traceability. Whether you're making sausages or cookies, you need to know which ingredients went into which batch, who operated the line and whether every quality checkpoint was met.

What changes is the production rhythm. Meat processing tends to be continuous, once a line starts, it runs for hours with high-speed throughput. Bakery and confectionery are more batch-oriented, with distinct mixing, proofing, baking and cooling stages. Beverages add another layer: liquid handling, CIP cycles and fill precision.

But Brainr was designed for this variability from the start. Our architecture handles both continuous and batch production, supports recipe-based manufacturing, and adapts to different quality control workflows. When we onboard a bakery client, we configure the system to track dough batches, fermentation times, and oven temperatures. When we onboard a beverage client, we track tank levels, blend ratios and fill weights. The underlying platform is the same; the configuration is tailored.

We're also learning from early pilots. We recently ran a proof-of-concept with a confectionery producer, and the feedback was immediate: "This is exactly what we've been looking for." That validation tells us the expansion will be faster than expected.

Sustainability and food safety are major challenges for global manufacturers. How does Brainr's technology contribute to reducing waste and ensuring compliance with food safety standards?

Both sustainability and compliance start with visibility and visibility starts with data. Brainr captures every data point: raw material intake weights, process temperatures, operator actions, quality test results, in real time.

On the food safety side, this means full traceability and automated HACCP monitoring. Every CCP is tracked digitally, every deviation is logged and every corrective action is documented. When an auditor asks for proof that temperatures were maintained during a specific production run three months ago, we can pull the full record in seconds, including the exact time, the operator on duty and the equipment calibration status. What used to take days of digging through paper logs now takes a single query.

On the sustainability side, visibility enables optimisation. One of our clients discovered through Brainr's analytics that they were overcooking certain products by an average of two minutes per batch, costing them €50,000 per year in energy and reducing yield by 1.5%. Once they saw the data, they adjusted the recipe and recovered both the cost and the yield. Another client used our waste tracking to identify that 80% of their trim waste was coming from a single cutting station. A simple equipment recalibration eliminated the problem.

The result is not only safer food but leaner, more sustainable production. And in an industry where margins are tight and regulations are tightening, that's a competitive advantage.

Your team combines deep industry experience with software and data expertise. How has this cross-disciplinary background influenced the way you approach innovation?

It keeps us grounded. In tech, it's easy to fall in love with elegant architectures or cutting-edge algorithms. But on a factory floor, none of that matters if the system crashes during peak production or if the interface is too complicated for an operator wearing gloves.

Our team includes people who've run factories, managed ERP rollouts, and worked night shifts on production lines. When we design a feature, we don't ask "Is this technically impressive?" We ask "Will this help a supervisor make a better decision at 3am when they're short-staffed and the line is down?"

That discipline shows up in the product. Brainr's interface is simple, large buttons, clear colours, minimal text. It works on tablets and smartphones, even with wet hands. Our onboarding process is fast because we've done it ourselves and we know what works. Our support team includes former production managers who can troubleshoot not just the software but the underlying operational issue.

Every new feature begins with a problem observed on the shop floor and ends with a solution that makes life easier for the people running it. That's why users tell us Brainr feels familiar from day one –because it was built by people who've stood where they stand.

As Brainr expands internationally, what strategies are you adopting to adapt your platform to different regulatory and operational environments?

Flexibility was built into our architecture from the start. Brainr is fully configurable: labelling rules, quality checks, reporting templates, compliance workflows, without requiring new development. When we expand into a new country, we configure the platform to match local regulations rather than forcing clients to adapt to us.

For example, French labelling requirements differ from Portuguese ones. Spanish food safety audits follow a different protocol than German ones. With Brainr, we adjust the configuration, not the codebase. This means we can enter a new market in weeks, not months, and clients get a system that feels local from day one.

We're also building a network of regional implementation partners – consultants, integrators and support specialists who understand each market's operational context and language. In Spain, our partner is a former food safety director at a major meat group. In France, we're working with a systems integrator who's implemented ERP systems at over 50 food facilities. This combination of a global platform and local expertise ensures fast, confident adoption.

And because Brainr is cloud-native, updates and improvements roll out to all clients simultaneously. A feature we develop for a Portuguese client becomes available to a Spanish client the same day. That global-local balance is key to scaling efficiently.

What advice would you give to other start-ups in the food and beverage tech space trying to secure funding or partnerships in such a competitive environment?

Focus on solving real problems, not building cool technology. In industrial tech, investors and partners trust proof more than pitch decks. If you're raising capital and your product is used by three factories that are willing to go on record about the impact, you're in a strong position. If you're raising capital and you have a beautiful demo but no production deployments, you'll struggle.

We didn't raise funding until BRAINR was live in multiple factories, processing millions of kilograms of product per month, and generating measurable ROI. By the time we started conversations with investors, we had data: 92% reduction in manual data entry, 95% faster traceability reporting, zero downtime across hundreds of production shifts. Those numbers made the pitch easy.

The same applies to partnerships. Food companies don't take risks with their production lines. They want to see that your technology works, not in a lab, but in a real factory, under real conditions, with real operators. Build that credibility first. The funding and partnerships will follow naturally.

Finally, for founders entering the F&B sector, what's one lesson you've learned about building trust and long-term value in an industry that's often resistant to change?

Respect the production floor. The people running those lines are the reason food reaches our tables safely every day. They know their processes better than any consultant or software vendor ever will. If you walk in with an attitude of "we're here to fix your outdated systems," you'll fail.

Instead, listen. Spend time on the floor. Understand the constraints they work under, tight margins, labour shortages, equipment breakdowns, last-minute order changes. Then build technology that makes their job easier within those constraints, not technology that assumes ideal conditions.

At BRAINR, we test every feature with real operators before it goes live. We run beta deployments during actual production shifts. We design for the worst-case scenario, a supervisor with wet gloves, bad lighting, and 30 seconds to make a decision, not the best-case scenario.

Reliability earns more trust than vision statements ever will. Every feature, every update must work perfectly in the middle of a busy shift. When people see your technology helping them perform better every day, not disrupting their workflow, not adding complexity, but genuinely making their job easier, they become your best advocates for change. And in this industry, word of mouth from the production floor is worth more than any marketing campaign.