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Adamo Foods Ltd.

Whole-cut protein from fermentation

A UK company making mycelium protein from brewer's spent grain across London, Nottingham, and a Ghent pilot facility

Industry
Precision Fermentation
Headquarters
London, UK
Public information as of
January 2026

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

Strategic priorities

Adamo Foods is a UK precision fermentation company developing whole-cut mycelium protein that replicates the texture and nutritional profile of red meat — using liquid-state fermentation of fungal mycelium rather than animal agriculture. The company operates across three geographic nodes: London HQ, Nottingham R&D, and the BBEPP manufacturing partner facility in Ghent with 1,000L+ pilot-scale bioreactors. The technology is based on research from the University of Nottingham, and the company is preparing Novel Food dossiers for UK FSA and EU EFSA submission, targeting market entry in the alternative protein market projected to reach USD 24 billion by 2030.

The core challenge is the scale-up from 10L laboratory fermenters to 1,000L+ commercial-scale bioreactors — a scale transition where mass transfer limitations in mixing, oxygen delivery, and shear stress behave unpredictably. Each failed pilot batch at the BBEPP facility costs tens of thousands of euros and delays the Series A funding readiness. Meanwhile, the three-node geographic structure creates data silos between quality data, process data, and formulation data that prevent data-driven optimisation.

The company is led by its COO and uses Brewer's Spent Grain from the brewing industry as its primary feedstock — a circular economy approach that reduces costs and strengthens the sustainability narrative, but introduces batch-to-batch feedstock variability that requires dynamic recipe adjustment during fermentation.

Challenges we see

  • Operations Manufacturing

    Scale-up from 10L to 1,000L introducing mass transfer unpredictability

    Scaling from 10L laboratory bioreactors to 1,000L+ pilot tanks at BBEPP introduces mass transfer limitations — mixing, oxygen delivery, and shear stress behave unpredictably at scale. A process that produces consistent results at 10 litres may fail at 1,000 litres because the fluid dynamics are fundamentally different.

    When a batch fails at the 1,000L pilot scale, the direct cost — raw materials, BBEPP facility time, lost product — is measured in tens of thousands of euros. More significantly, each failed batch delays the timeline for demonstrating manufacturing feasibility to Series A investors.

  • Digital Integration

    Three-node data silos preventing cross-site optimisation

    Adamo Foods operates across London HQ, Nottingham R&D, and BBEPP in Ghent with quality data, process data, and formulation data siloed in disconnected systems — PDFs, Excel, CSV dumps. The geographic distribution of the data mirrors the organisational structure, preventing the cross-node data correlation that would enable process optimisation.

    When Nottingham's formulation team adjusts a process parameter based on sensory analysis, that adjustment is not visible to BBEPP's process engineers until a physical batch is run and CSV data is shared — the feedback loop between formulation and manufacturing is broken by the data silo.

  • Operations Manufacturing

    Brewer's Spent Grain variability requiring dynamic recipe control

    Using Brewer's Spent Grain as feedstock introduces batch-to-batch chemical variability depending on beer type and brewing efficiency. Each incoming feedstock shipment may have different sugar composition, nitrogen content, and contaminant profiles — requiring dynamic recipe adjustment that the current manual process cannot provide.

    When feedstock variability is not compensated for by recipe adjustments, fermentation outcomes become inconsistent — batches stall or produce off-target protein profiles, and the texture reproducibility that the product requires is not achieved.

  • Compliance Regulatory

    Novel Food dossier data traceability gaps

    Assembling Novel Food dossiers requires collating data from multiple partners over 18-36 months. Any traceability failure — a batch where the supply chain provenance cannot be demonstrated or the process data cannot be linked to the final product — resets the regulatory clock and delays market entry.

    When a data traceability gap is discovered during the EFSA dossier review, the clock reset means months of delay and burning cash while competitors advance — the Series A funding is contingent on regulatory milestone timelines that a dossier reset would jeopardize.

  • Operations Energy

    Energy-intensive drying operations threatening unit economics

    The drying phase for mycelium biomass is the most energy-intensive step in the production process — directly impacting the unit economics of a product that must achieve price parity with conventional meat. The company's COO has identified continuous processing as the solution, but moving from batch to continuous processing introduces contamination risk and complex control logic.

    When the energy cost per kilogram of dried mycelium protein is too high, the product cannot achieve the retail price point that mainstream consumer adoption requires — the market access depends on the energy efficiency of the drying process.

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. Fermentation process digital twin for scale-up prediction

    Adamo Foods pays BBEPP for expensive pilot slots where batch failures due to improper mixing or oxygen starvation represent sunk costs. They lack the ability to simulate scale-up effects before physical trials, making each pilot batch a high-stakes experiment rather than a calibrated data point.

    Deploy a process digital twin that models bioreactor hydrodynamics and predicts scale-up behaviour in simulation — reducing physical trial failures, accelerating the path to Series A, and enabling the scale-up from 10L to 1,000L+ with predictive confidence.

    • Adamo Foods scale-up characterisation plan and BBEPP pilot slot cost analysis
    • University of Nottingham mycelium fermentation hydrodynamic data

  2. Unified R&D data platform connecting Nottingham, BBEPP, and London

    Sensory profiles and texture data from Nottingham exist in static PDF reports while process data from BBEPP comes as CSV dumps. London HQ cannot correlate fermentation parameters with sensory outcomes across sites, preventing data-driven optimisation and machine learning applications.

    Implement a unified LIMS/ELN platform connecting Nottingham sensory data, BBEPP process data, and London formulation data into a single searchable repository — creating the cross-node data correlation that enables Golden Batch identification and ML-driven process optimisation.

    • Adamo Foods data silo audit across London, Nottingham, and BBEPP
    • Current data transfer methods and quality of process data received from BBEPP

  3. GxP regulatory data fabric for Novel Food dossier assembly

    Assembling Novel Food dossiers requires collating data from multiple partners over 18-36 months. Manual data compilation creates traceability risks — any gap resets the regulatory clock and delays market entry by months while investors are waiting for the approval milestone.

    Deploy a GxP-compliant data management system that automatically aggregates batch records, safety data, and supply chain certificates into audit-ready dossier components — ensuring that the Novel Food submission is complete, traceable, and defensible when EFSA reviewers assess it.

    • Adamo Foods Novel Food dossier requirements and current data compilation process
    • UK FSA and EU EFSA digital register reform requirements

  4. AI feed-forward control for dynamic feedstock adjustment

    Brewer's Spent Grain varies chemically batch-to-batch, making precision fermentation difficult without dynamic recipe adjustment based on incoming feedstock analysis. Manual recipe preparation cannot respond fast enough to maintain consistent fermentation outcomes.

    Implement AI feed-forward control that analyses incoming feedstock composition and automatically adjusts fermentation parameters in real-time — maintaining consistent product quality across variable feedstock batches without manual recipe reformulation.

    • Adamo Foods feedstock variability analysis and current recipe adjustment process
    • AI feed-forward control technology feasibility for precision fermentation

  5. Energy monitoring and optimisation for drying operations

    The drying phase for mycelium biomass is energy-intensive, directly impacting unit economics. Continuous processing is the identified solution but requires maintaining sterile steady-state for extended periods with complex control logic that manual monitoring cannot manage.

    Deploy smart energy monitoring and predictive analytics for the drying process — optimising energy consumption, supporting the batch-to-continuous processing transition, and improving the unit economics that price parity with conventional meat requires.

    • Adamo Foods energy consumption analysis and cost breakdown by process step
    • Current drying process control and continuous processing technology assessment

What we'd propose

  • Digital CDMO

    Fermentation Process Digital Twin

    We build a computational process digital twin for Adamo Foods' liquid-state mycelium fermentation — physics-based models of bioreactor hydrodynamics, oxygen transfer, and shear stress that predict scale-up behaviour from 10L to 1,000L+ in simulation, enabling the Series A demonstration of commercial manufacturing feasibility without the cost and delay of repeated pilot batch failures.

    • Bioreactor hydrodynamic simulation

      Fluid dynamics predicted for any bioreactor scale and geometry

      Build computational fluid dynamics models of Adamo Foods' fermentation bioreactors across the 10L to 1,000L+ scale range — predicting the mixing patterns, oxygen transfer coefficients, and shear stress profiles that determine process success at each scale.

    • Scale-up parameter prediction and optimisation

      Scale-up parameters calculated before the pilot batch is run

      Configure the digital twin to generate scale-up parameter recommendations — impeller speed, aeration rate, power input — for any target bioreactor scale, enabling the BBEPP team to run the 1,000L fermentations with the parameters predicted to succeed rather than with the parameters that worked at 10L.

    • Digital twin validation with physical batch data

      Digital twin predictions verified against every completed pilot batch

      Build the model validation pipeline that compares digital twin predictions against actual BBEPP batch outcomes — continuously recalibrating the model parameters as more pilot data accumulates, improving prediction accuracy with each batch.

    • Pilot batch failures are reduced because scale-up parameters are validated in simulation before the expensive BBEPP slot is consumed — each prevented failure saves tens of thousands of euros and months of delay in the Series A timeline.
    • The Series A investor demonstration is more credible because the digital twin provides evidence of scale-up predictability — investors can see that the manufacturing risk has been characterised rather than simply asserted.
    • The digital twin accelerates the scale-up timeline because parameter optimisation that would require dozens of physical batches is compressed into simulation cycles — the path from 1,000L pilot to commercial production is faster.

    We've done this before

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  • Digital Lab

    Unified R&D Data Platform Across Three Nodes

    We implement a unified LIMS/ELN platform connecting Adamo Foods' three geographic nodes — Nottingham R&D sensory data, BBEPP Ghent process data, and London HQ formulation data — into a single searchable repository with automated correlation analysis, enabling Golden Batch identification and ML-driven process optimisation across the distributed R&D organisation.

    • Cross-site LIMS/ELN integration

      Nottingham, BBEPP, and London on the same data platform

      Deploy the cross-site LIMS/ELN integration that connects Nottingham's sensory analysis data, BBEPP's fermentation process data, and London's formulation data — every data point from every node is captured in the same platform with full traceability to source.

    • Automated correlation analysis between formulation and process

      Formulation changes linked to process outcomes automatically

      Build automated correlation analysis that links Nottingham formulation adjustments to BBEPP fermentation outcomes — when the formulation team changes a parameter, the system tracks the resulting batch quality automatically rather than requiring manual data comparison.

    • Golden Batch identification and process benchmarking

      Best historical batches identified and used as process references

      Configure the Golden Batch identification pipeline that scores every completed batch against the best historical performance — giving the process team the quantitative basis for understanding what makes the difference between a good batch and an exceptional batch.

    • The feedback loop between Nottingham's formulation work and BBEPP's manufacturing results is closed because the data is on the same platform — formulation decisions are informed by manufacturing outcomes rather than by guesswork.
    • The data foundation for ML-driven process optimisation is established — the unified platform provides the labelled, contextualised data that machine learning models require, enabling the next phase of AI-driven fermentation optimisation.
    • The cross-node data correlation identifies the parameters that drive texture reproducibility — the product quality attribute that differentiates Adamo Foods' whole-cut protein is systematically characterised rather than empirically maintained.

    We've done this before

    See how we do Digital Lab
  • Enterprise AI

    GxP Regulatory Data Fabric for Novel Food Compliance

    We deploy a GxP-compliant data management system for Adamo Foods that automatically aggregates batch records, safety data, and supply chain certificates from Nottingham, BBEPP, and feedstock suppliers into audit-ready Novel Food dossier components — ensuring complete traceability from feedstock provenance to final product for UK FSA and EU EFSA submissions.

    • Automated batch record aggregation from BBEPP

      Every fermentation batch record compiled automatically

      Build automated pipelines that ingest fermentation batch records from BBEPP — temperature profiles, oxygen transfer data, nutrient feeding logs — into the dossier data platform with full traceability to the specific batch and the feedstock lot used.

    • Supply chain traceability for Brewer's Spent Grain provenance

      Feedstock provenance documented from brewery to fermentation batch

      Configure the supply chain traceability system that tracks Brewer's Spent Grain from the originating brewery through the logistics chain to the BBEPP reception — linking each fermentation batch to the specific feedstock lot and providing the provenance data that Novel Food safety assessments require.

    • Dossier component generation and review workflow

      Dossier sections compiled automatically with QA review workflow

      Build the dossier component generation system that compiles the aggregated data into the section formats required by UK FSA and EU EFSA — with QA review workflow, electronic signatures, and version control that meet the regulatory submission standards.

    • The Novel Food dossier is complete and traceable when submitted — the risk of a traceability gap resetting the regulatory clock is eliminated because the data fabric maintains traceability continuously rather than compiling it retrospectively.
    • The regulatory submission is accelerated because dossier components are generated automatically from the data platform rather than being assembled manually from scattered files — the 18-36 month dossier preparation timeline is compressed.
    • The GxP-compliant data platform provides evidence of operational rigour that investors find compelling — the same data infrastructure that satisfies regulators demonstrates to Series A investors that the company operates with manufacturing discipline.

    We've done this before

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  • Digital CDMO

    AI Feed-Forward Control for Dynamic Feedstock Adjustment

    We implement AI feed-forward control for Adamo Foods' fermentation process — real-time feedstock composition analysis that automatically adjusts fermentation parameters before each batch, maintaining consistent product quality across variable Brewer's Spent Grain feedstock batches without manual recipe reformulation.

    • Incoming feedstock composition analysis

      Each feedstock batch characterised before fermentation begins

      Deploy rapid composition analysis for incoming Brewer's Spent Grain shipments — sugar profile, nitrogen content, and contaminant screening are measured before the feedstock is used, providing the input data that the feed-forward control model requires.

    • Automated recipe parameter adjustment

      Fermentation parameters adjusted automatically from feedstock analysis

      Build the feed-forward control model that translates feedstock composition data into adjusted fermentation parameters — nutrient feeding rate, oxygen setpoints, pH targets — and delivers the adjusted recipe to the BBEPP control system before the fermentation begins.

    • Feedstock-outcome correlation analysis

      Feedstock characteristics linked to final product quality

      Configure the correlation analysis that tracks the relationship between incoming feedstock composition and final product quality — identifying the feedstock characteristics that drive product outcomes and continuously improving the feed-forward model's accuracy.

    • Product quality is consistent across variable feedstock batches because the fermentation parameters are adjusted for each batch's specific feedstock characteristics — the texture reproducibility that the product requires is maintained even as the feedstock provenance varies.
    • The manual effort of recipe reformulation is eliminated because the feed-forward model handles the adjustment — the process team focuses on improving the model rather than on manually correcting each batch.
    • The feed-forward model improves over time as the feedstock-outcome correlation data accumulates — the more batches that are run, the more accurate the model becomes, and the better the product quality consistency.

    We've done this before

    See how we do Digital CDMO
  • Digital CDMO

    Energy Monitoring and Continuous Processing Optimisation

    We deploy smart energy monitoring and predictive analytics for Adamo Foods' drying and fermentation operations — real-time energy consumption tracking by process step, predictive models for continuous processing optimisation, and the data foundation for the batch-to-continuous processing transition that will define the long-term unit economics.

    • Real-time energy consumption monitoring by process step

      Energy use tracked at equipment level across all three sites

      Deploy smart energy meters at the equipment level across the Nottingham R&D facility and BBEPP Ghent operations — providing real-time visibility into energy consumption by process step and identifying the specific equipment and process windows that drive the highest energy costs.

    • Predictive models for continuous processing transition

      Batch-to-continuous processing parameters optimised in simulation

      Build the simulation models for the batch-to-continuous processing transition — predicting the energy consumption, contamination risk, and process stability characteristics of continuous operation before the physical transition is attempted.

    • Energy optimisation recommendations and alerts

      Energy waste identified and reported automatically

      Configure energy optimisation recommendations driven by the monitoring data — identifying process conditions that increase energy consumption without improving product quality and alerting the process team to the optimisation opportunities.

    • The unit economics of mycelium protein production improve because energy waste is identified and eliminated — the path to price parity with conventional meat is supported by lower production costs.
    • The continuous processing transition is lower risk because the simulation models predict the operational parameters before the physical transition — the investment in continuous processing equipment is informed by predictive analysis.
    • The energy monitoring platform provides the sustainability data that the L'Oreal partnership and other ESG-focused customers require — the sustainability narrative is supported by measured data rather than by estimates.

    We've done this before

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Digital maturity: today and target

Scored out of 100 across six dimensions. The target is what Adamo Foods Ltd.'s own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Data Integration 25 → 75
Quality data, process data, and formulation data are siloed across London, Nottingham, and BBEPP in disconnected systems — PDFs, Excel, CSV dumps. The unified LIMS/ELN platform required to connect the three nodes has not been deployed.
Process Automation 30 → 80
Feedstock composition is not measured in real time and recipe adjustment is manual. The AI feed-forward control system required for dynamic feedstock management has not been deployed. Drying operations and fermentation are monitored manually without automated process control.
Predictive Analytics 15 → 70
Process scale-up relies on physical trial batches at BBEPP rather than on simulation. No digital twin exists for the mycelium fermentation process. Each pilot batch is a high-stakes experiment rather than a calibrated data point in a validated model.
Regulatory Readiness 35 → 85
Novel Food dossier preparation relies on manual data compilation from multiple partners. The GxP-compliant data fabric required for continuous traceability and automated dossier assembly has not been built. The risk of a traceability gap resetting the regulatory clock is unmanaged.
Energy Optimisation 20 → 65
Energy consumption is not monitored at the equipment level. No data exists on energy consumption by process step or batch. The simulation models for the batch-to-continuous processing transition have not been built.
Scale-Up Simulation 10 → 75
The process digital twin for mycelium fermentation scale-up has not been built. Scale-up parameters are not predicted in simulation — they are inferred from the 10L results and applied to the 1,000L scale with the uncertainty that this approach entails.

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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 Adamo Foods Ltd., 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].