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MakeGrowLab

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 MakeGrowLab's published strategy and is not endorsed by, or produced in cooperation with, MakeGrowLab. Company website

Strategic priorities

MakeGrowLab operates across 4 stated priorities, with the most concrete near-term plan anchored on mass production of organic material.

Transitioning from laboratory-scale validation to industrial-scale BNC production through paper mill integration and demo facility development, targeting consistent fiber output quality for global packaging applications.

Establishing "Plug & Produce" modular fermentation capabilities that enable deployment at partner sites worldwide, use local agricultural biowaste streams while maintaining standardized output specifications.

Building automated systems for tracking CO2 savings, biowaste usage, and circularity metrics to maintain certifications and satisfy regulatory reporting requirements under EU PPWR and food-contact standards.

Challenges we see

  • Operations Manufacturing

    Fermentation Process Scalability

    BNC growth through microbial fermentation is inherently sensitive to environmental fluctuations including temperature, pH, and nutrient levels. Moving from 1-liter lab samples to 5,000-liter industrial batches requires extreme precision to maintain yield consistency.

    Batch failures due to environmental deviation represent significant financial risk, with each failed production run consuming valuable feedstock and delaying time-to-market commitments to Fortune 500 partners.

  • Digital Operations

    Biowaste Input Variability

    MGL upcycles agricultural biowaste from diverse sources including roots, fruits, and vegetables from the Lubelskie region. This introduces high variability in feedstock composition affecting fermentation outcomes.

    Without real-time data acquisition and adaptive control systems, SPM fiber consistency cannot be guaranteed, undermining its viability as a standardized industrial ingredient for demanding brand partners.

  • Operations Integration

    Legacy Paper Mill Integration

    The 2024 milestone of producing materials via operational paper mills requires integrating MGL's microbial slurry into traditional manufacturing environments that rely on legacy machinery and siloed control units.

    Paper mills maintain air-gapped hardware and aging mechanical systems that lack modern connectivity, creating integration barriers that could delay commercial production timelines.

  • Digital Integration

    IT/OT Convergence Gap

    MGL's digital infrastructure is nascent, with R&D teams using disconnected data islands including Excel and paper notebooks. The 2025 demo pilot facility requires a unified industrial data platform bridging shop-floor and top-floor systems.

    The inability to provide a unified "Data work" from sensor to scientist impedes process optimization and prevents MGL from monetizing their data insights for enterprise partners.

  • Compliance Regulatory

    Regulatory Compliance Burden

    SPM's food-contact approval brings MGL into the realm of FDA/GxP compliance, requiring rigorous data integrity and audit trails. EU PPWR mandates detailed sustainability reporting on biocontent and recyclability.

    Manual tracking and paper-based processes create audit risks and risk violations of ALCOA+ principles, threatening certifications essential for market access.

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. Manual Fermentation Monitoring

    Traditional fermentation processes involve significant manual sampling and monitoring with labor-intensive growth cycles prone to human error. MGL leadership has expressed frustration with the "years of research" required before market introduction due to slow, manual processes.

    Deploy IoT sensor networks and automated monitoring systems to enable 24/7 autonomous oversight of bioreactor fleets, reducing manual intervention while improving detection speed for process deviations.

  2. Inconsistent Batch Quality

    BNC production is an aerobic process where scaling up volume reduces surface area-to-volume ratio, limiting growth. Moisture and humidity variations during growth affect SPM's critical "impermeable to water" and "insoluble" properties.

    Implement closed-loop environmental regulation using smart sensor integration and automated control systems to maintain precise temperature, humidity, and dissolved oxygen levels across production runs.

  3. Disconnected Data Systems

    R&D teams operate with fragmented data silos between Excel spreadsheets, paper notebooks, and disparate LIMS systems. This prevents unified process analytics and hinders the ability to correlate experimental variables with outcomes.

    Build an industrial data platform with OPC UA backbone connecting all laboratory and production equipment, enabling real-time data streaming from sensors to dashboards and automated KPI calculation.

  4. Manual Contamination Detection

    Microbial contamination can ruin entire BNC batches, yet detection relies on manual microscope counting that is too slow for industrial-scale response times. This represents both a quality and financial risk.

    Deploy AI-powered computer vision systems using edge inference to detect infections and count cell populations in real-time, enabling 1-second anomaly detection and immediate intervention.

  5. Unverified Sustainability Claims

    MGL claims their process uses less energy and water with CO2 savings, but lacks automated systems to quantify and verify these metrics for regulatory reporting and partner audits under EU PPWR requirements.

    Create an ontology-based data platform that automatically tracks biowaste collection through final SPM production, generating immutable audit trails for ESG compliance and sustainability certifications.

What we'd propose

  • Digital Lab

    Smart Bioreactor Monitoring Platform

    Deploy comprehensive IoT sensor networks and data visualization infrastructure to transform MGL's fermentation monitoring from manual sampling to automated, real-time process intelligence with predictive capabilities.

    • 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
  • Digital CDMO

    Closed-Loop Fermentation Control System

    Implement automated environmental regulation infrastructure with precise closed-loop control to ensure consistent BNC production quality across varying biowaste inputs and scaling batch sizes.

    • 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

    Paper Mill OT/IT Integration Package

    Bridge MGL's BNC production process with legacy paper mill infrastructure through secure retrofitting, data connectivity, and standardized communication protocols for smooth industrial integration.

    • 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

    AI Vision Quality Control System

    Deploy computer vision and machine learning infrastructure for automated contamination detection, cell counting, and quality verification throughout the BNC production process.

    • 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

    ESG Compliance Data Platform

    Build an ontology-driven industrial data platform that automatically captures, validates, and reports sustainability metrics for EU PPWR compliance, food-contact certification, and enterprise partner audit requirements.

    • 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 MakeGrowLab's own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Process Automation 25 → 80
Currently relies on manual fermentation monitoring and sampling; 2025 pilot facility requires automated bioreactor fleet management
Data Integration 20 → 85
Fragmented R&D data in Excel and paper notebooks; needs unified industrial data platform with OPC UA connectivity
Real-Time Visibility 15 → 90
Limited process intelligence capability; requires sensor networks and dashboards for live production monitoring
Quality Control 30 → 85
Manual microscopy and visual inspection; needs AI vision systems for automated contamination detection
Compliance & Reporting 25 → 80
Paper-based sustainability tracking; requires automated ESG data platform for EU PPWR and GxP requirements
IT/OT Convergence 15 → 75
Siloed IT and OT systems; 2024 paper mill integration and 2025 demo facility demand unified architecture

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 MakeGrowLab, 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].