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Acies Bio d.o.o.

Scaling fermentation to industrial volume

A Slovenian biomanufacturer running the STOL fermentation and OneCarbonBio platforms with an EU-funded bioprocess digital twin

Industry
Biomanufacturing
Headquarters
Ljubljana, Slovenia
Public information as of
January 2026

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

Strategic priorities

Acies Bio is a Slovenian biomanufacturing company based in Ljubljana, operating the STOL platform for microbial biotechnology and developing the OneCarbonBio platform for the upcycling of CO2 and methanol into chemicals and proteins. The company serves pharmaceutical and chemical partners including BASF and LG Chem, and is expanding from 10 cubic metre pilot scale to 30 cubic metre industrial production — a scale-up challenge that introduces fundamental changes in fluid dynamics, heat transfer, and oxygen availability compared to the pilot bioreactors that the current data infrastructure was designed around.

The immediate challenge is the data gap between the STOL pilot facility and the new biomanufacturing plant: bioprocess data from the STOL facility is air-gapped from R&D designs in laboratory notebooks or disconnected ELNs, preventing the data-driven process optimisation that the 30 cubic metre scale requires. At industrial scale, process deviations that would be visible and correctable at 10 litres can ruin a 30,000-litre batch before the operator knows there is a problem.

Acies Bio operates with 100 scientists including 25 PhDs, has secured EU P4D grant funding for bioprocess digital twins, and has partnered with BASF and LG Chem on the OneCarbonBio sustainability platform — creating both the funding and the strategic urgency for a digital transformation that can support cGMP compliance at the new industrial facility.

Challenges we see

  • Operations Manufacturing

    Pilot-to-industrial scale transition requiring real-time process intelligence

    Scaling from 10 cubic metres to 30 cubic metres introduces fluid dynamics, heat transfer, and oxygen availability characteristics that do not exist at pilot scale. At the larger scale, a process deviation that would be visible and correctable at 10 litres can ruin a 30,000-litre batch before the operator knows there is a problem. The current data infrastructure — paper notebooks and disconnected ELNs — cannot provide the real-time visibility that industrial-scale fermentation requires.

    When bioprocess data is not available in real time during an industrial fermentation run, the operator is flying blind — the decisions that prevent a batch failure are made too late or not at all because the data arrives after the deviation has already propagated into the final product.

  • Digital Integration

    STOL facility data air-gapped from R&D designs

    The STOL pilot facility and the new biomanufacturing plant lack integration between bioprocess equipment on the OT side and the enterprise IT systems on the IT side. Data from fermentation runs sits in the STOL facility's local systems while R&D designs are documented in notebooks and disconnected ELNs — two separate information environments that do not communicate.

    When fermentation data from the STOL facility cannot be directly compared with the R&D models that predicted how the process should behave, the feedback loop between pilot operations and strain design is broken — the computational models are calibrated against historical runs that are not being updated with the latest data.

  • Digital Operations

    Technology transfer between Ljubljana R&D and STOL facilities lacking standardisation

    Transferring processes from the Ljubljana R&D laboratory to the STOL pilot facility requires standardised equipment calibration and software version control protocols that are not currently in place. Discrepancies between facilities lead to unexplained batch failures and undermine the Plug and Produce flexibility that Acies Bio claims.

    When a process that was developed in the R&D laboratory behaves differently when transferred to the STOL pilot facility, the troubleshooting required to understand the discrepancy consumes scientist time and delays the programme — the technology transfer is not reliable because the data and calibration standards that would make it reliable are not shared between facilities.

  • ESG Regulatory

    Manual ESG reporting not matching partner requirements

    Partners including LG Chem and BASF require verifiable real-time data on net-zero production status for the OneCarbonBio platform runs. Manual ESG reporting — compiled from utility records and estimated emission factors — cannot provide the granular, auditable data that sustainability-focused contracts increasingly require.

    When ESG reporting is based on estimates rather than measured data, the sustainability claims in Acies Bio's contracts with BASF and LG Chem are only as credible as the estimation methodology — partners who have invested in their own sustainability reporting frameworks will scrutinise the methodology and may find it insufficient.

  • Compliance Regulatory

    cGMP compliance requiring electronic records at industrial scale

    Expansion into biologics and antibody medicines for pharmaceutical partners requires strict adherence to cGMP and EU Annex 1 regulations at the new 30 cubic metre facility. Paper-based documentation in production creates compliance risks that pharmaceutical partners and regulatory inspectors will identify — the documentation practices that were acceptable at pilot scale will not be acceptable at cGMP production scale.

    When cGMP inspectors assess the 30 cubic metre facility and find paper-based batch records that cannot demonstrate complete traceability and contemporaneous documentation, the audit findings can delay product launch, require corrective action plans, or in severe cases, prevent the facility from operating until compliance is demonstrated.

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. Real-time bioprocess intelligence for industrial-scale fermentation

    Scientists at Acies Bio compare fermentation runs against the ideal Golden Batch profile across 35-plus laboratory-scale reactors, but the data is retrospective — critical process parameters are calculated after the run rather than available in real time, preventing intervention before a batch deviates beyond correction.

    Deploy unified bioprocess visualisation and KPI dashboards that contextualise live sensor data from the STOL and industrial facilities with calculated biological metrics — enabling immediate process deviation detection and the Golden Batch comparison that industrial-scale fermentation requires.

    • STOL facility current monitoring capabilities assessment
    • 35-plus reactor configuration and data sources

  2. OPC UA and MTP-based IT/OT integration for the STOL and biomanufacturing plant

    The STOL facility and new biomanufacturing plant lack integration between bioprocess equipment and enterprise IT systems, creating data silos that prevent the data-driven process optimisation that industrial-scale fermentation demands.

    Deploy an Industrial Data Platform using OPC UA and MTP standards to enable continuous data flow from bioprocess equipment to business intelligence systems and digital twin simulations — connecting the shop floor to the enterprise without requiring a full SCADA replacement.

    • AciesBio STOL facility OT environment assessment
    • EU P4D 2025 digital twin programme integration requirements

  3. Paperless digital lab transformation with GxP-compliant electronic records

    Despite having 100 scientists and 25 PhDs, Acies Bio relies on manual data extraction, paper-based documentation, and disconnected ELNs for laboratory procedures — creating administrative overhead, compliance risks, and the manual effort that scalable R&D cannot afford to carry.

    Digitise laboratory procedures with integrated Laboratory Execution Systems connecting LIMS, ELN, and analytical instruments — eliminating paper from the laboratory, ensuring automated data capture with ALCOA-plus compliance, and providing the cGMP documentation foundation for the 30 cubic metre facility.

    • AciesBio laboratory current state assessment and paper documentation inventory
    • EU Annex 1 and cGMP electronic records requirements

  4. Bioprocess digital twin foundation for virtual experimentation

    Acies Bio has secured EU P4D grant funding for bioprocess digital twins but lacks the underlying data infrastructure and ontology-based platform required to build accurate predictive simulations — the grant money cannot be deployed until the data foundation is in place.

    Build the semantic data layer and automated pipelines that contextualise strain data with fermentation parameters — enabling virtual experimentation on the digital twin before costly wet-lab runs, and building the simulation capability that makes the P4D investment deliver results.

    • EU P4D 2025 grant programme technical requirements
    • AciesBio digital twin programme data infrastructure assessment

  5. Computer vision fermentation monitoring and autonomous dosing control

    Managing specialised sporogenes and hard-to-express proteins at industrial scale requires micro-aerophilic conditions and narrow stability windows that exceed standard PID control capabilities — requiring continuous human monitoring that cannot scale to a 24/7 unmanned operation.

    Implement computer vision monitoring and autonomous dosing systems for non-invasive process parameter monitoring and foam detection — enabling 24/7 unmanned operation at the 30 cubic metre facility by replacing human supervision with automated monitoring that never tires.

    • AciesBio industrial facility unmanned operations requirements
    • Computer vision fermentation monitoring technology feasibility

What we'd propose

  • Enterprise AI

    Real-Time Bioprocess Intelligence Platform

    We deploy a comprehensive real-time visualisation platform for the STOL and industrial biomanufacturing facilities — P and ID-aligned dashboards, automated Bio-KPI calculations, and Golden Batch comparison capabilities that give operators the real-time process intelligence required to manage industrial-scale fermentation runs proactively.

    • Live fermentation KPI dashboard

      Every reactor visible in real time from a single screen

      Deploy real-time KPI dashboards that display dissolved oxygen, pH, temperature, biomass concentration, and substrate feed rates for every active fermentation vessel — calculated from sensor data and presented in the context of the current batch recipe and the Golden Batch reference profile.

    • Automated Bio-KPI calculation engine

      VCD, OUR, and CER calculated automatically from sensor data

      Build an automated Bio-KPI calculation engine that derives Viable Cell Density, Oxygen Uptake Rate, and Carbon Evolution Rate from the raw sensor data streams — providing the biological metrics that operators use to assess process performance without requiring manual data extraction and calculation.

    • Golden Batch comparison and deviation alerting

      Every batch compared to the best historical performance

      Configure a Golden Batch comparison layer that continuously scores the current batch against the best historical performance and alerts the operator when the trajectory diverges beyond the acceptable window — enabling corrective intervention while the batch is still salvageable.

    • Batch failures at industrial scale are detected early enough for corrective intervention — avoiding the cost of a failed 30,000-litre fermentation run that takes weeks to reprocess and delays the product delivery timeline.
    • The real-time KPI dashboard reduces the cognitive load on operators managing multiple concurrent fermentation runs — the prioritisation of attention is guided by the data rather than by the operator's memory of which vessel was last checked.
    • The Golden Batch reference profile accumulates with every successful run — the operational definition of 'good process' is continuously refined and becomes more protective against deviations as more data is collected.

    We've done this before

    See how we do Enterprise AI
  • Enterprise AI

    IT/OT Convergence and Industrial Data Platform

    We implement an ontology-based Industrial Data Platform that bridges the gap between shop-floor bioprocess equipment and enterprise IT systems using OPC UA and MTP standards — enabling the true Plug and Produce flexibility that Acies Bio requires across the STOL facility, the new industrial plant, and future partner sites.

    • OPC UA server deployment on bioprocess equipment

      Every bioreactor connected via OPC UA

      Deploy OPC UA servers on all bioprocess equipment at the STOL and industrial facilities — connecting the PLC and sensor layer directly to the enterprise data platform without requiring a full SCADA replacement or point-to-point integration for each new device.

    • MTP-compliant equipment module definitions

      New equipment integrated in hours, not weeks

      Define MTP-compliant equipment modules for the key bioprocess equipment types — fermenters, centrifuges, chromatography skids — so that new equipment can be integrated into the platform in hours rather than weeks of custom engineering work.

    • Cross-facility data lake with R&D connectivity

      Ljubljana R&D and STOL facility on the same data platform

      Build a cross-facility data lake that connects the STOL pilot data, the new industrial plant data, and the Ljubljana R&D computational models — enabling the data feedback loop between pilot and production that makes scale-up predictable and the technology transfer between facilities reliable.

    • The technology transfer between the Ljubljana R&D laboratory and the STOL pilot facility becomes reliable because the calibration standards and process data are shared on the same platform — discrepancies are identified and resolved faster because both sides are looking at the same data.
    • New bioprocess equipment can be integrated into the platform quickly because MTP-compliant modules define the integration interface — the facility expansion does not require a custom engineering project for each new piece of equipment.
    • The data platform supports the EU P4D digital twin programme by providing the real-time data feed that the digital twin requires — the grant investment is enabled by the data infrastructure rather than being stranded because the data is not available.

    We've done this before

    See how we do Enterprise AI
  • Digital Lab

    Digital Lab and Paperless QC Transformation

    We transform manual laboratory procedures at Acies Bio into fully digital workflows through integrated Laboratory Execution Systems — automated data capture from analytical instruments, GxP-compliant electronic records replacing paper documentation, and direct LIMS and ELN connectivity that ensures the cGMP compliance documentation required for pharmaceutical partner audits.

    • Analytical instrument integration and automated data capture

      HPLC, spectrophotometer, and cell counter data captured automatically

      Deploy instrument integration for the key analytical equipment — HPLC, spectrophotometer, cell counters — capturing measurement data automatically as results are generated and transferring them directly to the LIMS without manual transcription or USB transfer.

    • GxP-compliant electronic batch record system

      Every batch record complete and audit-ready

      Implement an electronic batch record system for the 30 cubic metre cGMP facility that captures all critical process parameters, quality checks, and operator actions as attributable electronic records — providing the documentation foundation that cGMP inspectors expect and pharmaceutical partners require.

    • ALCOA-plus compliance monitoring dashboard

      Data completeness and attribution verified automatically

      Build a compliance monitoring dashboard that continuously verifies the completeness and attribution of laboratory data across all active studies — flagging gaps before an inspection surfaces them and providing the proactive compliance assurance that cGMP requires.

    • The cGMP audit for the 30 cubic metre facility is passed on the first assessment because the documentation is complete and attributable — avoiding the corrective action delays that paper-based systems typically generate when cGMP inspectors review them.
    • Laboratory efficiency improves because scientists spend time on science rather than on paperwork — the manual transcription of results from instruments to notebooks and from notebooks to LIMS is eliminated for the majority of routine analyses.
    • The ALCOA-plus monitoring dashboard provides the evidence that the compliance posture is maintained continuously — the cGMP documentation is not a scramble before an inspection but a continuous output of the laboratory operations.

    We've done this before

    See how we do Digital Lab
  • Enterprise AI

    Bioprocess Digital Twin Foundation

    We build the semantic data infrastructure and automated pipelines required for the EU P4D bioprocess digital twin programme — connecting strain design data, fermentation parameters, and operational outcomes in an ontology-based platform that enables virtual experimentation and predictive process optimisation.

    • Semantic data layer for bioprocess ontology

      Strain data and fermentation parameters in a unified data model

      Build the semantic data ontology that connects Acies Bio's strain design data — metabolic pathways, genetic modifications, projected yields — with the fermentation parameters from actual production runs — creating the contextualised data layer that makes the digital twin simulations accurate rather than generic.

    • Automated data pipelines from lab to digital twin

      Digital twin updated with every fermentation run automatically

      Configure automated data pipelines that feed the digital twin with the latest fermentation data from every completed run — the simulation model is continuously recalibrated against actual performance and the gap between predicted and actual behaviour is tracked and analysed.

    • Virtual experimentation platform for strain and process optimisation

      Simulation-driven process optimisation before lab work begins

      Build the virtual experimentation interface that allows Acies Bio's strain designers and process engineers to test hypotheses in simulation before committing to wet-lab execution — reducing the experimental cycles required to find optimal process conditions and accelerating the development timeline.

    • The EU P4D grant investment delivers a working digital twin rather than a feasibility study — the data infrastructure enables the grant money to produce operational results within the programme timeline.
    • The development cycles for new strains and processes are reduced because virtual experimentation identifies promising conditions before laboratory resources are committed — the experimental bottleneck shifts from wet-lab execution to simulation capacity, which scales more easily.
    • The digital twin enables risk-free exploration of edge-of-specification conditions — process engineers can see how the process would behave outside the normal operating range without risking a production batch.

    We've done this before

    See how we do Enterprise AI
  • Digital CDMO

    Computer Vision Fermentation Monitoring and Autonomous Control

    We deploy AI-driven computer vision systems for non-invasive bioprocess monitoring and autonomous control of the 30 cubic metre industrial facility — foam detection, cell density estimation, and automated dosing control that enables 24/7 unmanned operation without requiring continuous human supervision.

    • Non-invasive computer vision for bioreactor monitoring

      Cell density and morphology monitored without sampling

      Deploy computer vision monitoring on the 30 cubic metre bioreactors — using calibrated imaging to estimate cell density, morphology, and growth phase without the manual sampling that disrupts the culture and introduces contamination risk.

    • Automated foam detection and anti-foam dosing control

      Foam events managed automatically without operator intervention

      Implement automated foam detection using computer vision and integrate it with the dosing control system — anti-foam addition is triggered automatically when foam reaches the configured threshold, preventing the overfoaming that would otherwise require operator intervention or cause culture loss.

    • Autonomous nutrient feeding based on online sensors

      Nutrient feed rates adjusted automatically from sensor data

      Build autonomous nutrient feeding control that adjusts substrate addition rates based on real-time sensor data — dissolved oxygen, pH, and online metabolite measurements — implementing the feed-forward control strategy that maintains optimal growth conditions throughout the fermentation without requiring manual adjustment.

    • The 30 cubic metre facility can operate 24/7 without continuous human supervision — the autonomous monitoring and control systems maintain process integrity between operator shifts and overnight periods when the facility would otherwise need to be staffed.
    • Cell density monitoring without manual sampling increases the data density of each fermentation run — the digital twin and KPI platform receive continuous imaging data rather than periodic manual sample points, improving the fidelity of both the real-time dashboards and the simulation models.
    • The automated foam control prevents the culture losses that manual foam management cannot keep pace with at industrial scale — every prevented foam-over incident preserves the batch and the weeks of fermentation effort that preceded it.

    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 Acies Bio d.o.o.'s own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Data Integration 35 → 85
Bioprocess data from the STOL facility is air-gapped from R&D designs in notebooks and disconnected ELNs. The OPC UA and MTP infrastructure required to connect shop-floor equipment with the enterprise data platform is not in place. The cross-facility data lake connecting Ljubljana R&D, STOL, and the industrial plant does not exist.
Process Automation 40 → 90
Manual data extraction and paper-based documentation characterise the laboratory workflow. The autonomous dosing and computer vision monitoring required for 24/7 unmanned operation have not been implemented. The digital twin foundation funded by the EU P4D grant has not been built.
Real-Time Analytics 30 → 85
Critical bioprocess parameters are calculated retrospectively after fermentation runs complete rather than available in real time. The real-time KPI dashboards and Golden Batch comparison required for proactive process management do not exist.
Digital Twins 15 → 75
The EU P4D grant for bioprocess digital twins has been awarded but the underlying data infrastructure — OPC UA connectivity, semantic ontology, automated data pipelines — has not been built. The digital twin capability is funded but not yet operational.
Laboratory Digitalization 35 → 80
Laboratory procedures at the STOL facility and the new industrial plant rely on paper-based documentation and manual data transfer. The analytical instruments are not integrated with the LIMS. The ALCOA-plus compliance monitoring required for cGMP has not been implemented.
IT/OT Convergence 25 → 80
The gap between the bioprocess equipment OT layer and the enterprise IT systems is not bridged. The STOL and industrial facilities operate with isolated data systems. The OPC UA and MTP integration required for scalable Plug and Produce flexibility has not been deployed.

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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 Acies Bio d.o.o., 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].