Microfy: transforming honey analysis with AI-powered microscopy
With support from the EIT Food Accelerator Network, Microfy sharpened its market focus and transformed its product, HoneyAI, from an R&D concept into a commercial system now used across international markets.
Imagine getting complex, critical information in less than an hour using just a microscope and AI. Microfy, a Spanish startup established in 2019, is making this possible for honey.
Taking the sting out of honey analysis
Traditional honey pollen analysis is a painstaking process. A trained specialist examines the prepared sample under a microscope, visually identifying and counting hundreds of pollen grains.Â
Research into manual pollen analysis has estimated that extraction and identification can take between two and ten hours, while established methods can require 500 to 1,000 pollen grains to be counted per honey sample. For organisations handling multiple samples, that represents a significant amount of specialist time.[1]
Microfy is helping simplify the process with HoneyAI, using automated microscopy and artificial intelligence to make analysis faster, easier and more accessible for honey industry stakeholders, including cooperatives, traders, packers, laboratories, associations, and public bodies.
Creating a buzz beyond the lab
Since the programme, Microfy has transformed HoneyAI from an R&D concept into a commercial product. The first HoneyAI system launched commercially by the end of 2023.Â
The company has since introduced the technology into multiple international markets, reached customers in 11 countries and achieved adoption and validation by public laboratories. In 2026, three industrial clients also invested directly in Microfy, providing a further signal of confidence from users of the technology.
Digitalising honey analysis from lab to market
Microfy – Honey.Ai’s automated pollen analysis can deliver results in situ in as little as three minutes and within an hour, removing the need to send samples to an external laboratory and wait several days for results.Â
According to Microfy, analysis costs an average of €10 per test, making it five to ten times cheaper than using an external laboratory.Â
By automating the process, the company also aims to reduce human error and variability between laboratory results, while improving reproducibility and giving users greater control over product monitoring.
Honey.AI saving money and time
Helping innovation take flight
EIT Food has supported Microfy’s journey from technology development towards market adoption through the EIT Food Accelerator Network (FAN). The programme helped the team sharpen its value proposition, better understand its target customers and take a more systematic approach to validation, scalability and investment readiness.Â
Support continued beyond the accelerator, with HoneyAI receiving the Global Reach Innovator Award as part of the EIT Food Marketed Innovation Prize, recognising its international deployment and helping give Microfy further visibility within the agrifood ecosystem.
FAN came when we needed to move from a technology-driven mindset to a market-driven strategy.
What Microfy learned beyond the hive
- Start with the customer problem, not the technology.
- Validate each new application before scaling it.
- Adapt the message to each audience.Â
- Join an accelerator with clear objectives for validation, partnerships, pricing, investment or market access.
More than honey - technology for a more resilient food system
The potential goes well beyond honey. Microfy plans to apply the same combination of affordable microscopy, automation and AI to areas such as fungal spore monitoring, fermentation and bee health, where easier access to biological information could support better and faster decision-making.Â
But expansion will not happen simply because the technology can be applied elsewhere. Microfy’s next step is to validate each new application against a clear customer need and build a strong commercial case before scaling - applying the same market-driven approach that helped turn HoneyAI from an R&D concept into a commercial product.
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References
- Viertel, P., & König, M. (2022). Pattern recognition methodologies for pollen grain image classification: A survey. Machine Vision and Applications, 33, Article 18. https://link.springer.com/article/10.1007/s00138-021-01271-wÂ
