IN Brief:
- DigiFoundry 2.0 has secured up to €1.94 million through Estonia’s Applied Research Programme within a €2.53 million project.
- ÄIO and TFTAK will integrate strain development, fermentation, automation, sensory analysis, and data-driven process control.
- The programme runs to June 2029 as ÄIO develops larger-scale manufacture of fermentation-derived fats and oils.
ÄIO and the Center of Food and Fermentation Technologies, TFTAK, have secured up to €1.94 million for DigiFoundry 2.0, a three-year programme designed to improve microbial oil process development through fermentation, automation, digitalisation, and biotechnology. The total project budget is approximately €2.53 million and the work runs from 1 July 2026 to 30 June 2029.
Funding comes through the Applied Research Programme of the Estonian Business and Innovation Agency, with ÄIO’s project ranked first in the programme’s tenth funding round. The new programme builds on the original DigiFoundry collaboration between ÄIO and TFTAK, which ran from 2023 to 2026 and developed a prototype platform for automated microbial strain design alongside a Design-Build-Test-Learn development cycle.
The earlier project also covered pilot-scale precision fermentation, sensory analysis, and techno-economic assessment. DigiFoundry 2.0 extends that work across the production process, linking strain development with fermentation performance, automated control, and data analysis rather than treating biological design as a separate step from manufacturing.
ÄIO develops oils and fats using specialised yeast fermentation, with side streams from food, agriculture, and wood processing among the potential feedstocks. The approach replaces extraction from conventional oil crops or animal fats with cultivation of microorganisms under controlled conditions, followed by recovery and preparation of the resulting lipids for food, cosmetics, and other applications.
Automation is central to the project because fermentation performance depends on several variables changing together over time. Temperature, pH, nutrient availability, agitation, oxygen transfer, cell growth, and product formation all affect yield and consistency. Collecting comparable data manually across many development runs is slow and vulnerable to variation, while automated sampling and control can make the results easier to compare.
The Design-Build-Test-Learn model is intended to shorten that cycle. Strains are designed or selected, built and tested under controlled conditions, and the resulting process data are fed back into the next development round. Connecting sensory results and product characteristics to fermentation conditions should also help identify whether a higher-yield process creates unwanted flavours, aromas, or functional changes that would reduce its usefulness as a food ingredient.
ÄIO says the new phase will support cost reduction as well as technical development. Fermentation-derived fats have to compete with ingredients supplied through mature oil and animal-fat markets, where enormous production volumes have already driven down unit costs and created established logistics, refining, and quality systems.
A microbial alternative therefore needs more than a favourable sustainability profile. Melting behaviour, flavour neutrality, oxidation stability, texture, processing compatibility, and price all affect whether a formulation team can use it. A technically promising oil that varies from batch to batch or requires expensive purification is unlikely to displace an established ingredient at commercial scale.
ÄIO has already moved beyond laboratory quantities. Its development work has reached tonne-scale production, and the company is building partnerships for larger-scale manufacturing. DigiFoundry 2.0 is intended to make that scale-up more predictable by generating a tighter relationship between strain performance, operating conditions, product quality, and manufacturing cost.
Precision fermentation development is increasingly centred on vessel utilisation, contamination control, feedstock flexibility, downstream recovery, energy use, cleaning time, and repeatability rather than biological yield alone. Those factors determine whether a microorganism that performs well in development remains commercially useful once it enters an industrial cost model.
Local side streams add another variable. Using food or wood-industry residues as fermentation feedstocks could reduce reliance on conventional agricultural inputs, but side streams have to be sufficiently consistent for microorganisms and process-control systems to handle normal variation. A platform that can adapt operating conditions to changing feedstock composition could improve both resilience and yield.
TFTAK brings bioprocess development, analytics, sensory work, and synthetic biology into the programme. Its role is not confined to strain engineering; the research organisation supports development from laboratory work through pilot-scale and industrial testing, providing a route to check whether digital improvements remain useful as equipment and batches become larger.
By June 2029, the programme is intended to produce an integrated manufacturing method rather than a standalone software platform. ÄIO needs fermentation runs that deliver stable oil quality, repeatable yields, faster development cycles, and lower cost, with enough process data to transfer those operating conditions into larger production assets.
