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NoMy

Updating the operating model

Industry
Biotechnology
Public information as of
January 2026

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

Strategic priorities

NoMy operates across 4 stated priorities, with the most concrete near-term plan anchored on bio-digital platform scaling.

Transform MycoPrime from a consultancy service into a scalable SaaS platform capable of licensing fermentation optimization technology to 500+ partners globally, moving beyond the current 5-partner manual capacity.

Successfully deploy industrial-scale fermentation at Nitten's Hokkaido facilities to validate the technology for the Japanese market, use the €1.25M investment to prove commercial viability before broader expansion.

Position as the enabling infrastructure for the Circular Bioeconomy by converting food industry waste streams (sugar beet pulp, molasses) into high-value protein, aligning with EU Green Deal and Japan's Moonshot R&D programs.

Challenges we see

  • Operations Manufacturing

    Bioreactor Scale-Up "Valley of incident"

    Transitioning from 10L lab fermenters to 10,000L+ industrial tanks creates non-linear physics challenges including heat transfer limitations, mass transfer problems, and shear stress that can destroy filamentous fungi.

    Without predictive simulation, capital-intensive industrial deployments risk batch failures that could consume the entire €1.25M funding and undermine credibility with Nitten.

  • Digital Integration

    Legacy OT Integration at Nitten

    Nitten's century-old sugar factories run on legacy PLCs and SCADA systems that are likely air-gapped, while NoMy needs real-time data extraction to feed the MycoPrime AI platform.

    Connecting modern cloud systems to Nitten's brownfield infrastructure without disrupting 24/7 sugar campaign operations risks production downtime and security breaches.

  • Digital Operations

    Joining records across systems

    Oslo scientists running strain optimization experiments likely record data in paper notebooks, Excel files, and proprietary vendor formats, creating "Dark Data" that cannot train MycoPrime AI models.

    The MycoPrime AI promise is marketing vapor without structured, machine-readable training data from Oslo lab operations.

  • Compliance Regulatory

    Regulatory Traceability for Novel Foods

    Japan's Food Sanitation Act and EU Novel Food Regulation require complete batch traceability from agricultural input to final product, proving safety from pesticide or heavy metal contamination.

    Manual QA documentation compilation is slow and error-prone, and hard to scale to support the food-grade market entry required for the Kagome partnership.

  • Operations Operations

    Distributed Team Collaboration (Oslo-Sapporo)

    The 8-hour time zone gap between Oslo R&D and Sapporo operations means working days barely overlap, creating critical delays when pilot runs show anomalies requiring expert intervention.

    Reliance on asynchronous communication for synchronous process control problems could result in lost batches and delayed learning cycles.

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. MycoPrime Platform Infrastructure Gap

    NoMy markets MycoPrime as an "AI-driven upcycling service" but with 11-15 employees and seed-stage funding, they likely lack the cloud data platform, MLOps pipelines, and frontend dashboards to deliver it as a scalable SaaS product.

    Build the actual software backend of MycoPrime—data ingestion pipelines, metabolic modeling engine, and partner visualization dashboards—transforming marketing claims into a revenue-generating digital product.

  2. Industrial-Scale Process Simulation

    NoMy cannot afford failed industrial deployments at Nitten, but they have no way to virtually test bioreactor designs, mixing strategies, or impeller configurations before committing to expensive steel tank construction.

    Deploy CFD-integrated Digital Twins that simulate multiphase flow, heat transfer, and shear stress in proposed industrial tanks, enabling virtual prototyping that de-risks the €1.25M CapEx.

  3. Secure OT Data Extraction from Brownfield Factories

    Nitten's legacy control systems are siloed and air-gapped, preventing NoMy from accessing real-time process data needed to optimize fermentation and validate MycoPrime AI models.

    Deploy non-intrusive overlay sensors and Industrial Edge Gateways that extract data without touching existing PLC logic, creating operational visibility without operational risk.

  4. Lab Digitalization and Data Standardization

    Oslo R&D generates critical strain optimization data in paper notebooks and disconnected Excel files, creating "Dark Data" that cannot feed the MycoPrime AI learning loop.

    Implement cloud-native Electronic Lab Notebook (ELN) and LIMS ecosystems integrated with bioreactors, automating data pipelines so every experiment automatically updates MycoPrime training sets.

  5. Regulatory Batch Traceability

    Moving from feed to food applications requires proving complete safety lineage from field to fork, but NoMy and Nitten's datasets are currently disconnected, preventing automated compliance documentation.

    Create Digital Product Passports with blockchain-backed or immutable batch records that trace mycoprotein lineage to specific farms, enabling "Compliance as a Service" that accelerates regulatory approval.

What we'd propose

  • Enterprise AI

    MycoPrime Cloud Platform Engineering

    End-to-end development of the MycoPrime SaaS infrastructure—cloud data platform, automated ingestion pipelines, metabolic modeling engine, and partner-facing visualization dashboards that transform AI marketing into operational reality.

    • Ontology layer

      A shared semantic model so lab, process, and quality data describe the same things the same way.

      DETAIL

    • Predictive models

      Models trained on the historical data plane that flag deviations before they become scrap.

      DETAIL

    • Decision surfaces

      Single pane of glass that surfaces model output to the right role at the right moment.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Enterprise AI
  • Digital CDMO

    CFD-Integrated Digital Twin for Bioreactor Scale-Up

    High-fidelity Digital Twin implementation combining Computational Fluid Dynamics (CFD) with metabolic kinetic models to simulate industrial-scale fermentation before physical deployment, de-risking the €1.25M CapEx investment.

    • OT/IT convergence

      Pull sensor and controller data off the line into a shared data plane in real time.

      DETAIL

    • Batch intelligence

      Golden-batch comparison and deviation detection running on the same data plane.

      DETAIL

    • Production release flow

      Closed-loop between QA, MES, and ERP so batch record review and release follow the data, not the paperwork.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital CDMO
  • Digital CDMO

    Secure Industrial Edge Architecture for Brownfield Integration

    Design and deployment of IT/OT convergence infrastructure that enables secure data extraction from Nitten's legacy SCADA systems without disrupting 24/7 sugar production operations or exposing critical control systems to cyber threats.

    • OT/IT convergence

      Pull sensor and controller data off the line into a shared data plane in real time.

      DETAIL

    • Batch intelligence

      Golden-batch comparison and deviation detection running on the same data plane.

      DETAIL

    • Production release flow

      Closed-loop between QA, MES, and ERP so batch record review and release follow the data, not the paperwork.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    Connected Lab Ecosystem for Oslo R&D

    Implementation of cloud-native Electronic Lab Notebook (ELN) and Laboratory Information Management System (LIMS) integrated with bioreactors, automating the data pipeline from strain optimization experiments to MycoPrime AI training sets.

    • Unified data backbone

      Connect instruments and LIMS into a single data spine so QC and CDMO records are queryable across sites.

      DETAIL

    • Paperless workflows

      Move lab execution from paper to instrument-captured records with full audit trail.

      DETAIL

    • Continuous QC release

      Review-by-exception dashboards that flag only the records needing scientist attention.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital Lab
  • Enterprise AI

    Digital Product Passport & Regulatory Traceability Platform

    Development of blockchain-backed or immutable batch record system that traces mycoprotein lineage from agricultural input to final product, automating regulatory compliance documentation for Japan and EU Novel Food approval.

    • Ontology layer

      A shared semantic model so lab, process, and quality data describe the same things the same way.

      DETAIL

    • Predictive models

      Models trained on the historical data plane that flag deviations before they become scrap.

      DETAIL

    • Decision surfaces

      Single pane of glass that surfaces model output to the right role at the right moment.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Enterprise AI

Digital maturity: today and target

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

Source: A4BEE analysis of public sources
Data Infrastructure 25 → 80
Lab data exists in disconnected Excel files and paper notebooks; needs unified cloud data platform for MycoPrime AI
IT/OT Convergence 15 → 75
No visible industrial connectivity infrastructure; Nitten integration requires secure brownfield edge architecture
Process Analytics 30 → 85
MycoPrime AI is marketing promise; requires actual MLOps pipelines and metabolic modeling capabilities
Regulatory Digitalization 20 → 70
Manual QA documentation; needs automated Digital Product Passports for Novel Food compliance
Cross-Site Collaboration 35 → 80
8-hour time zone gap managed through asynchronous channels; needs real-time data sharing and alert systems
Simulation & Digital Twin 15 → 75
No CFD or scale-up simulation capabilities; critical for de-risking industrial deployments

Check this yourself

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Related reading

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 NoMy, 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].