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AIO Laboratories GmbH

Scaling single-cell protein production

A Berlin single-cell protein producer with a 2026 EFSA deadline, scaling to 100,000 litres across CMO partners

Industry
Single-Cell Protein Biomanufacturing
Headquarters
Berlin, Germany
Public information as of
January 2026

A4BEE prepared this analysis from publicly available sources. It reflects our own reading of AIO Laboratories GmbH's published strategy and is not endorsed by, or produced in cooperation with, AIO Laboratories GmbH. Company website

Strategic priorities

AIO is a German biomanufacturing company producing single-cell protein from agricultural side-streams — sawdust, straw, and other lignocellulosic residues — using proprietary engineered yeast strains. The process produces microbial oil and protein for animal feed and nutritional supplements, claiming 97 percent less land use and 90 percent less water than equivalent palm oil production. The company is transitioning from 300-litre pilot batches to 100,000-litre industrial-scale production at CMO sites, targeting 4,000 tonnes per annum by 2029.

The 2026 EFSA Novel Food dossier submission is the dominant near-term business event. The regulatory filing requires demonstrating batch-to-batch consistency and safety across industrial-scale production — which means the data infrastructure must be in place before the industrial batches that will populate the dossier are run. Simultaneously, the technology licensing model requires sharing fermentation intellectual property with CMO partners while maintaining IP protection through a Zero Trust Architecture.

The bioprocess challenge is distinctive: agricultural side-stream feedstocks vary in chemical composition — lignin-to-hemicellulose ratios shift with season and source — and the 100,000-litre scale introduces heat and mass transfer physics that do not exist at 300-litre pilot scale. The process that works at pilot scale requires real-time metabolic monitoring and feed-forward control to maintain performance at industrial scale.

Challenges we see

  • Operations Manufacturing

    Feedstock variability requiring dynamic process adjustment across scales

    The production process uses low-value agricultural side-streams — sawdust, straw, and lignocellulosic residues — as feedstock. These materials vary significantly in chemical composition depending on season, source, and preprocessing method. At 300 litres, operators can manually adjust the process in response to feedstock variability. At 100,000 litres, the response window is shorter and the consequence of a wrong adjustment is larger.

    Where feedstock composition changes at industrial scale and the process is not instrumented to detect and compensate for those changes automatically, the result is batch-to-batch variability that undermines the consistency claims required for the EFSA dossier. A dossier built on inconsistent batches is vulnerable to regulatory challenge.

  • Digital Integration

    Paper notebooks and USB data transfers preventing real-time process comparison

    Critical bioprocess data — fermentation curves, feedstock composition measurements, lipid yield results — is currently captured in paper notebooks and Excel spreadsheets, with data transfers between sites happening via USB drives. There is no real-time visibility into what is happening in the fermentation, and no automated comparison against a Golden Batch profile.

    When batch records exist in paper notebooks and USB transfers, the comparison of any given batch against the best historical performance requires manual data retrieval and re-entry — a process too slow to support real-time process steering. The Golden Batch profile remains a theoretical concept rather than an operational reference.

  • Digital Integration

    CMO partner OT environments disconnected from AIO's digital platform

    Industrial-scale production runs at CMO sites using external fermentation capacity. AIO's proprietary yeast strains and fermentation control algorithms must be shared with CMO operators, but the OT environments at partner sites are not connected to AIO's internal systems — creating a monitoring gap between what AIO knows about its strains and what the CMO can see on their SCADA.

    When a fermentation run is in progress at a CMO site and AIO's process team cannot see the live telemetry, the response to a developing process deviation depends on the CMO operator recognising the issue and communicating it — a communication chain that adds delay and risk compared to direct telemetry access.

  • Compliance Regulatory

    Manual batch documentation creating EFSA dossier risk

    The EFSA Novel Food dossier requires demonstrating batch-to-batch consistency and safety across industrial production. Documentation standards must meet the requirements for a Novel Food submission — data completeness, attributable records, and an auditable paper trail. Manual documentation introduces the risk of gaps, illegible entries, and transcription errors that could be challenged during regulatory review.

    When batch documentation is compiled retrospectively from paper records, the completeness and accuracy of the dossier depends on the quality of handwritten records kept during the production run — a standard that regulators increasingly consider insufficient for Novel Food submissions.

  • Digital Operations

    IP protection requirements for shared fermentation intelligence

    AIO's intellectual property is concentrated in its engineered yeast strains and the fermentation 'recipes' — the specific parameter combinations that produce target yields from specific feedstocks. Sharing these recipes with CMO partners for production requires a data sharing architecture that allows partners to operate the process without exposing the underlying intellectual property to extraction or theft.

    When fermentation recipes are shared with CMO partners over standard network connections, the intellectual property in those recipes is exposed to interception. The technology licensing model depends on partners being able to operate the process, which requires sharing the recipe — without a Zero Trust Architecture, that sharing necessarily exposes the IP to theft.

Opportunities, by urgency and business impact

Each bubble is one opportunity, numbered to match the list below. Further right means it bites sooner; higher means a bigger effect on the business. A bigger bubble means a bigger implementation effort.

Source: A4BEE analysis of public sources

  1. Digital lab platform for EFSA Novel Food compliance

    Critical bioprocess data is scattered across paper notebooks, USB drives, and Excel spreadsheets, making it impossible to generate the unified audit trail required for EFSA Novel Food approval — the 2026 submission deadline requires this data infrastructure to be operational before the industrial batches are run.

    Implement a Digital Lab platform that automatically captures data from fermentation equipment and analytical instruments, creating an immutable digital record from feedstock receipt to lipid extraction — the batch-to-batch consistency evidence that EFSA reviewers require.

    • EFSA Novel Food dossier requirements and 2026 submission timeline
    • AIO batch documentation workflow analysis

  2. Secure IT/OT architecture for technology licensing via CMO partners

    AIO's technology licensing model requires sharing fermentation intellectual property with CMO partners, but current systems lack secure integration between proprietary control algorithms and partner SCADA and PLC infrastructure.

    Establish a Zero Trust IT/OT architecture with VLAN segmentation that securely connects AIO's digital platform with CMO manufacturing systems — enabling remote monitoring and shared process visibility while maintaining IP protection through policy-enforced access controls.

    • AIO technology licensing business model
    • CMO partner OT environment assessment

  3. Real-time bioprocess intelligence and metabolic modelling for scale-up

    Heat and mass transfer physics change fundamentally when scaling from 300L to 100,000L, causing batch variability that manual monitoring cannot detect or prevent in time — threatening the consistency claims required for the EFSA dossier.

    Deploy real-time KPI visualisation with predictive metabolic modelling to calculate Oxygen Uptake Rate and Carbon Evolution Rate, enabling proactive adjustments before thermal events affect lipid yields — providing the process telemetry data that makes scale-up predictable.

    • AIO fermentation scale-up from 300L to 100,000L technical plan
    • Microbial oil scale-up challenges from lignocellulosic feedstocks

  4. Automated downstream processing for lipid extraction at scale

    Extracting lipids from yeast biomass at tonne-scale requires complex centrifugation, drying, and extraction steps with significant manual intervention — increasing labour costs, contamination risk, and limiting throughput compared to what automated closed-loop processing could achieve.

    Implement closed-loop automation for downstream processing with computer vision monitoring and automated orchestration to achieve continuous, high-throughput DSP operations — increasing extraction yield while reducing the manual intervention that limits throughput.

    • AIO downstream processing current state assessment
    • Tonne-scale lipid extraction technology review

  5. IoT-enabled ESG data hub for B2B partnership evidence

    AIO claims 97 percent less land use and 90 percent less water compared to palm oil, but lacks automated systems to ingest resource consumption data and generate auditable ESG reports for B2B partners and investors.

    Deploy an IoT-enabled Sustainability Data Hub that automatically tracks energy, water, and feedstock consumption from the manufacturing floor to generate real-time carbon footprint dashboards — providing auditable ESG evidence for B2B partnership contracts and investor reporting.

    • AIO sustainability claims and B2B partnership requirements
    • EU Corporate Sustainability Reporting Directive applicability

What we'd propose

  • Digital Lab

    Digital Lab Platform for EFSA Novel Food Compliance

    We implement an end-to-end laboratory digitalisation platform connecting fermentation equipment, analytical instruments, and quality systems — creating an immutable digital record from feedstock receipt to lipid extraction that generates the batch-to-batch consistency evidence required for the EFSA Novel Food dossier submission.

    • Electronic batch record for fermentation runs

      Complete batch record generated automatically

      Deploy an electronic batch record system linked to fermentation equipment that captures all process parameters — temperature, pH, dissolved oxygen, feed rates, gas evolution — automatically as each batch runs, producing a complete and attributable batch record without manual transcription from paper notebooks.

    • Feedstock composition auto-capture

      Lignin-to-hemicellulose ratio measured and logged automatically

      Integrate near-infrared spectroscopy or wet chemistry analysers at the feedstock intake to automatically measure and log the chemical composition of each feedstock batch — lignin content, cellulose content, hemicellulose ratio — correlating composition with process performance in the batch record.

    • EFSA dossier data package generator

      Dossier sections compiled from structured batch records

      Build a dossier data package generator that extracts batch records, analytical results, and consistency metrics from the digital platform and formats them into the structure required by EFSA for a Novel Food submission — producing the consistency chapter from data rather than from a retrospective narrative.

    • The EFSA dossier consistency chapter is populated from structured digital records rather than assembled retrospectively from paper notebooks — the submission is more defensible because the evidence is auditable and complete.
    • Batch records are complete and attributable from the first industrial-scale production run, providing the evidence base for the dossier while also enabling process optimisation based on the data that is being collected.
    • The digital lab platform is the foundation for all downstream data initiatives — the real-time bioprocess intelligence, ESG reporting, and CMO integration all depend on the data that the lab platform captures.

    We've done this before

    See how we do Digital Lab
  • Enterprise AI

    Real-Time Bioprocess Intelligence Platform for Scale-Up

    We deploy an industrial data platform providing live KPI visualisation, Golden Batch comparison, and predictive metabolic modelling for the AIO fermentation process — enabling process teams to detect and respond to metabolic deviations before they affect lipid yields as production scales from 300L to 100,000L.

    • Real-time metabolic KPI dashboard

      OUR and CER calculated and displayed in real time

      Deploy soft sensor calculations for Oxygen Uptake Rate and Carbon Evolution Rate from the gas analysis data, presenting them alongside temperature, pH, and dissolved oxygen traces on a real-time dashboard — giving the process team a live view of the metabolic state of the fermentation rather than relying on periodic manual sampling.

    • Golden Batch benchmarking

      Every batch compared to the best historical performance

      Build a Golden Batch analysis layer that continuously compares the current batch's process trajectory against the best historical batches, generating a deviation index that highlights when and how the current run is diverging from optimal performance — enabling targeted process adjustments before yield is compromised.

    • Scale-up metabolic model

      100,000-litre performance predicted before the batch runs

      Develop a scale-up model that predicts how the AIO fermentation process will behave at 100,000-litre scale based on the heat and mass transfer parameters that differ from the 300-litre pilot — enabling process window optimisation in simulation before the first industrial-scale run is commissioned.

    • Metabolic deviations are caught and corrected before they propagate into yield loss — each avoided batch failure preserves the feedstock, energy, and labour cost of a production run.
    • The scale-up model reduces the risk that the first 100,000-litre batches will underperform — avoiding the plant-scale equivalent of a pilot failure that would delay the EFSA dossier and the commercial timeline.
    • Golden Batch analysis builds an institutional memory of what good looks like, encoded as data rather than stored in the experience of individual operators.

    We've done this before

    See how we do Enterprise AI
  • Digital CDMO

    Secure Zero Trust IT/OT Architecture for Technology Licensing

    We implement a Zero Trust IT/OT architecture with VLAN segmentation and policy-enforced access controls that enables AIO's digital platform to connect securely with CMO partner manufacturing systems — providing the remote visibility and process data access that the technology licensing model requires without exposing fermentation intellectual property to unauthorised access.

    • Zero Trust network architecture

      Fermentation IP visible only to authorised parties

      Implement a Zero Trust network architecture — identity-aware proxies, device authentication, and microsegmentation — between AIO's platform and each CMO partner's OT network, so that AIO's process team can monitor fermentation runs in real time without exposing fermentation recipes to any party that is not explicitly authorised for that specific data.

    • Secure remote fermentation monitoring

      AIO sees CMO fermentation data without exposing own systems

      Configure secure remote access to CMO fermentation telemetry — AIO's process team sees the live fermentation data without requiring VPN access to the CMO's internal network — protecting both AIO's IP and the CMO's existing OT security posture.

    • IP-preserving technology transfer workspace

      Recipe knowledge shared for operation, not extraction

      Build a technology transfer workspace that presents fermentation recipes and operating parameters in the operational context needed for CMO operators to run the process correctly, without exposing the underlying biological design rationale or optimisation history that constitutes AIO's core IP.

    • The technology licensing model becomes technically viable — AIO can share the operational knowledge required for a licensee to run the process without sharing the intellectual property that makes the process valuable.
    • AIO's process team gains real-time visibility into fermentation runs at CMO sites, enabling faster response to deviations and a deeper evidence base for continuous process improvement.
    • The Zero Trust architecture satisfies both AIO's IP protection requirements and CMO partners' network security policies — removing a common blocker to the partnership agreements that the licensing model requires.

    We've done this before

    See how we do Digital CDMO
  • Enterprise AI

    Sustainability Intelligence Hub for ESG Reporting

    We deploy an IoT-enabled Sustainability Data Hub that automatically tracks energy, water, and feedstock consumption across all production sites — generating real-time carbon footprint dashboards, automated ESG reports for B2B partnership evidence, and the auditable data trail that substantiates the 97 percent land use and 90 percent water reduction claims.

    • IoT energy and water metering at production sites

      Every production site metered at process level

      Install sub-metering on major energy consumers — fermenter heating and cooling loops, centrifuges, dryers, extraction equipment — and connect to the Sustainability Data Hub, providing per-batch energy and water consumption data rather than facility-level utility totals.

    • Automated carbon footprint calculation

      Scope 1 and 2 emissions per batch calculated automatically

      Configure automated carbon footprint calculation using the metered energy data, applying the GHG Protocol methodology for Scope 1 and 2 emissions — generating a carbon passport for each production batch that B2B partners can use to calculate the embodied emissions of their end products.

    • ESG report generator for B2B partners

      Partner ESG reports sent automatically each quarter

      Build an ESG reporting engine that generates quarterly sustainability reports for B2B partners and investors, covering land use, water consumption, energy intensity, and carbon footprint — providing the auditable evidence for AIO's environmental claims that procurement teams and investors increasingly require.

    • The 97 percent land use and 90 percent water reduction claims are substantiated by auditable per-batch data rather than estimated from facility-level averages — B2B partners can cite AIO's environmental performance in their own sustainability reporting.
    • The EU Corporate Sustainability Reporting Directive compliance burden is reduced because the data is collected continuously rather than compiled retrospectively for the annual sustainability report.
    • AIO's ESG evidence base becomes a sales tool — procurement teams at food and feed companies increasingly require supplier ESG data, and AIO's automated reporting capability differentiates it from competitors who provide estimates rather than audited figures.

    We've done this before

    See how we do Enterprise AI
  • Digital CDMO

    Automated Downstream Processing for Lipid Extraction

    We implement closed-loop automation for the downstream processing of AIO's lipid extraction operations — covering centrifugation, drying, and solvent extraction stages — with computer vision monitoring and automated process orchestration to achieve continuous high-throughput DSP operations at tonne scale.

    • Centrifugation automation and monitoring

      Centrifuge operation automated and monitored continuously

      Implement automated centrifugation control with real-time monitoring of separation efficiency, bowl speed, and feed rate — adjusting parameters automatically to maintain optimal separation as the biomass composition varies with each fermentation batch.

    • Computer vision for extraction process monitoring

      Solvent extraction monitored for completeness automatically

      Deploy computer vision monitoring of the solvent extraction stage to detect incomplete extraction, emulsion formation, and solvent carryover — providing automated quality flags that would otherwise require manual sample analysis at each stage.

    • DSP process orchestration and batch tracking

      Full DSP chain orchestrated with complete genealogy

      Build a DSP process orchestration layer that coordinates the centrifugation, drying, and extraction stages as a continuous sequence — tracking biomass input, lipid output, solvent consumption, and energy use per batch to generate the complete mass balance that the EFSA dossier requires.

    • Throughput at the extraction stage increases because automated closed-loop control maintains optimal parameters continuously as biomass composition varies between batches.
    • The mass balance data required for the EFSA dossier — lipid yield per batch, solvent consumption, energy use — is generated automatically as a by-product of the automated process, rather than requiring manual measurement and recording at each stage.
    • Contamination events and incomplete extractions are detected automatically rather than discovered during final QC — reducing the rework cost and the risk of a failed batch reaching the product inventory.

    We've done this before

    See how we do Digital CDMO

Digital maturity: today and target

Scored out of 100 across six dimensions. The target is what AIO Laboratories GmbH's own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Data Integration 30 → 85
Critical bioprocess data — fermentation curves, feedstock composition, lipid yields — is captured in paper notebooks, USB drives, and Excel spreadsheets. No digital platform exists that connects fermentation equipment, analytical instruments, and quality systems into a unified data model. The comparison of any batch against a Golden Batch requires manual data retrieval.
Process Automation 35 → 80
Fermentation control at 300 litres depends on manual operator adjustment based on periodic sampling. At 100,000 litres, the scale introduces dynamics that make manual adjustment insufficient — the real-time metabolic monitoring and feed-forward control required for consistent industrial-scale production does not exist.
IT/OT Convergence 25 → 85
CMO partner fermentation sites operate on SCADA and PLC systems that are not connected to AIO's internal platform. AIO's process team has no real-time visibility into fermentation runs at CMO sites. The Zero Trust architecture required for secure technology licensing has not been designed or implemented.
Regulatory Readiness 40 → 90
Batch documentation for the EFSA dossier is currently compiled manually from paper records. The EFSA submission deadline in 2026 requires the digital compliance infrastructure to be operational before the industrial batches that will populate the dossier are run — the window for implementation is narrow.
Infrastructure Resilience 30 → 80
No high-availability architecture protects the fermentation data collection infrastructure. The single points of failure in the current data collection setup — a USB drive that is lost, a laptop that fails — represent risks to the EFSA dossier evidence base that a resilient infrastructure would eliminate.
ESG Data Management 25 → 75
Resource consumption is tracked at the facility level through utility bills rather than at the process level through sub-metering. The granularity required for per-batch carbon passports and auditable ESG reports does not exist — the claims of 97 percent land use and 90 percent water reduction are based on estimates rather than measured data.

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This is an independent analysis prepared by A4BEE from publicly available information as of January 2026. It reflects A4BEE's own interpretation and opinion, is not affiliated with, endorsed by, or verified with AIO Laboratories GmbH, and may be incomplete or inaccurate. All company names and trademarks are the property of their respective owners. To request a correction or removal, contact [email protected].