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Chromologics ApS

Scaling fermented food colors to industrial volume

Industry
Precision Fermentation and Natural Food Colors
Headquarters
Søborg, Greater Copenhagen, Denmark
Public information as of
January 2026

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

Strategic priorities

Chromologics produces Natu.Red, a pH- and heat-stable red pigment made by fungal precision fermentation, and is racing the January 2027 FDA deadline that revokes authorisation for Red 3 in food and ingested drugs. The company is a spin-out from the Technical University of Denmark founded by Gerit Tolborg (CEO) and Anders Ødum (CTO), with approximately 23 employees as of early 2025 and a third Series A / bridge round of €7M in November 2025 taking total funding to roughly €20M.

Commercial production runs through a strategic partnership with Olon Group, an Italian CDMO with industrial fermentation capacity at Settimo Torinese of 4,500 cubic metres and vessels up to 255 cubic metres. Chromologics owns the strain engineering and process development in Søborg, while Olon executes the fermentation at industrial scale, a structure that puts the centre of operational data in Italy and the centre of process knowledge in Denmark.

The investor mix, Novo Holdings, EIFO and Döhler Ventures, sets the next two-year agenda: dual-track FDA and EFSA submissions, ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate plus Complete, Consistent, Enduring and Available) data integrity across R&D and outsourced manufacturing, and unit economics on par with synthetic dyes so that Natu.Red can replace Red 3 and Red 40 at the volumes that global food and beverage customers are asking for.

Three priorities dominate the work: getting live process telemetry from Olon's bioreactors back to the strain and process team in Copenhagen, modelling the non-linear scale-up from 10 litre laboratory reactors to 255,000 litre tanks before committing industrial batches, and compiling a single evidence set that satisfies both FDA and EFSA templates without parallel manual transcription.

Challenges we see

  • Scale-up Manufacturing

    Predicting behaviour at industrial scale before committing batches

    Chromologics' laboratory fermenters in Søborg run at 0.1 to 10 litre scale while Olon's industrial vessels reach 255,000 litres. Mixing times, oxygen transfer rates, shear forces and gradients in pH and dissolved oxygen all change non-linearly between the two scales, and pilot data does not always transfer cleanly.

    Where process parameters are scaled by linear extrapolation rather than by tank-specific modelling, the first industrial batches become the experiment, with each failed 100,000 litre batch carrying an outsized financial and supply chain cost.

  • Data Integration

    Closing the data path between process design in Denmark and process execution in Italy

    The Olon partnership gives Chromologics access to industrial capacity without the capital cost of building it, and the R&D team in Søborg owns strain engineering and process development. Day-to-day fermentation data, however, lives inside Olon's site systems, with Chromologics' team currently receiving post-run reports rather than live telemetry.

    Where process data only leaves the CDMO site after a batch ends, the time window for diagnosing a temperature spike, pH drift or raw material variance is closed by the time the report arrives, so root-cause work happens retrospectively rather than during the run.

  • Compliance Regulatory

    Maintaining ALCOA+ data integrity across two regulatory jurisdictions

    FDA and EFSA submissions require data that is Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring and Available (ALCOA+). Olon records and Chromologics' R&D records are currently compiled by hand into the dossier templates for the two authorities, which have overlapping but not identical requirements for safety data, toxicology and manufacturing consistency.

    Where the same source data is manually transcribed into two dossier formats, every transcription is a candidate for an ALCOA+ finding, so the cost of compliance work grows with the number of submissions rather than with the underlying scientific content.

  • Data Operations

    Unifying R&D and manufacturing data around a single record

    As a Technical University of Denmark spin-out, Chromologics grew up with Electronic Lab Notebooks and Excel files for strain engineering and process development, while manufacturing data arrives from Olon in PDF batch records and CSV exports. There is no shared record that links a genetic variant developed in 2024 to a production batch outcome in 2026.

    Where strain lineage, process parameters and finished product data live in separate stores, the correlation work that improves future batches is done by hand each time, and the institutional knowledge that should compound with every run is reconstructed for it.

  • Sustainability Energy

    Reducing energy intensity in downstream processing

    After fermentation, fungal biomass is filtered and the red liquid is dried, both energy-intensive steps that affect cost of goods sold and the sustainability case Chromologics presents to investors and customers. EIFO and Novo Holdings have framed sustainability as a board-level priority alongside commercial returns.

    Where filtration and drying parameters are run at historical defaults rather than against an energy-per-kilogram model, the sustainability premium the pigment can command is eroded by the operating cost of producing it.

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. Bringing live process telemetry from Olon's bioreactors into the Copenhagen team

    Process ownership sits in Søborg and process execution sits inside Olon's vessels in Italy. The strain and process team works from post-run reports rather than from the live state of the fermenter, so deviations are diagnosed after the batch ends.

    A secure vendor data integration layer that pulls process values from Olon's site systems into a Chromologics-side dashboard returns live visibility to the people who own the recipe, while leaving Olon's other client data segregated behind the same boundary.

    • Novo Holdings, Chromologics raises €7M, November 2025
    • Chromologics 2025 deep research, CDMO data visibility

  2. Modelling scale-up from laboratory reactors to Olon's 255 cubic metre vessels in advance

    Mixing times, oxygen transfer and shear forces do not scale linearly from 10 litre laboratory reactors to 255,000 litre industrial tanks, and the first industrial batches run with parameters extrapolated rather than predicted.

    Tank-specific computational fluid dynamics models of Olon's vessels let agitation, aeration and feeding strategies be tested in silico against the actual geometry, so the parameter set for the first full-scale runs is chosen on modelled behaviour rather than on linear extrapolation.

    • Olon Group brochure and CDMO specifications, 2024
    • Chromologics 2025 deep research, scale-up gap

  3. Compiling FDA and EFSA dossiers from a single source dataset

    FDA and EFSA submissions require overlapping but distinct sets of safety, toxicology and manufacturing-consistency evidence, and Olon records and Chromologics records are currently transcribed by hand into each template.

    A structured content layer that loads source records once and publishes to both FDA and EFSA templates reduces parallel manual work and gives each submission an end-to-end audit trail from the instrument that produced each value.

    • FDA, FD&C Red No. 3, revocation of authorisation, January 2025
    • Novo Holdings, Chromologics raises €7M, November 2025

  4. Overlay live fermentation runs against the best historical batch profile

    Industrial food customers will not accept variation in colour hue or intensity, and small drifts in critical process parameters accumulate into finished-product variation that becomes visible only after the batch is complete.

    A real-time overlay that compares the current batch's trajectory against the closest historical Golden Batch profile, with deviation alerts routed to the operators in Italy and the process team in Denmark, turns batch consistency from a release-time measurement into an in-run signal.

    • Chromologics 2025 deep research, Golden Batch analytics
    • Olon Group CDMO specifications, 2024

  5. Optimising yield and downstream energy use across the fermentation dataset

    Precision fermentation is capital- and energy-intensive compared with synthetic colour manufacture, and the cost gap to Red 3 and Red 40 narrows only as titer (grams of pigment per litre per hour) rises and downstream energy use falls.

    Multivariate analysis across the fermentation dataset identifies the parameter combinations that move titer, while a paired model for filtration and drying finds the operating window that minimises energy per kilogram of pigment, both fed back into Olon's run plans.

    • Chromologics 2025 deep research, yield optimisation and DSP
    • EIFO press release on Chromologics Series A / bridge, 2025

What we'd propose

  • Digital CDMO

    Vendor data bridge between Chromologics and Olon

    A secure integration layer that reads process values from Olon's site systems for Chromologics-only batches, normalises them against the strain engineering record in Søborg, and surfaces them as a live dashboard to the process owners in Denmark, with Olon's other client data segregated behind the same boundary.

    • Secure segregated telemetry pipeline

      Chromologics-only batch data, nothing else

      Establish a one-way data path that extracts only the process values tied to Chromologics batches from Olon's SCADA (Supervisory Control and Data Acquisition) and historian systems, with tenant isolation enforced at the network and application layers so that Olon's other client data stays on Olon's network.

    • Legacy protocol translation at the boundary

      OPC UA bridge to the cloud

      Translate Olon's industrial protocols (Siemens PCS 7, Rockwell or DeltaV) into OPC UA (Open Platform Communications Unified Architecture) or MQTT (a lightweight messaging protocol widely used for industrial telemetry) at a hardened edge gateway, so the data path into the Chromologics-side platform is vendor-neutral and documented.

    • Process dashboard for the Søborg team

      Live view of the run

      Build a process dashboard that shows current batch trajectory, critical process parameters and deviation alerts for the strain and process team in Copenhagen, with role-based access for Olon operators on the same data.

    • The strain and process team diagnoses deviations during the run rather than after it.
    • Olon's other client data stays on Olon's network by design, not by policy.
    • Process knowledge accumulates on the Chromologics side rather than leaving with each report.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    Bioreactor digital twin for scale-up risk reduction

    Tank-specific computational models of Olon's 1 to 255 cubic metre vessels that let agitation, aeration and feeding strategies be tested in silico against the actual vessel geometry, so the parameter set for the first full-scale runs is chosen on modelled behaviour.

    • Vessel-specific hydrodynamic model

      CFD for Olon's tanks, not generic curves

      Build a computational fluid dynamics (CFD) model of Olon's specific tank geometry and impeller configuration, including mixing time, oxygen transfer coefficient (kLa) and shear rate distributions, so scale-up predictions are anchored to the vessels the batches actually run in.

    • In-silico parameter sweeps

      Test agitation and aeration without committing batches

      Run parameter sweeps across agitation speed, aeration rate and feeding strategy inside the model, with outputs ranked against yield, mixing time and energy draw, so the parameter set sent to Olon is narrowed before any physical batch is started.

    • Gradient map overlay for critical process parameters

      Where dead zones would form

      Layer pH, dissolved oxygen and temperature gradients on top of the hydrodynamic model to identify the regions of the vessel where fungal stress would accumulate, so those regions can be targeted with sensor placement and feeding corrections.

    • The first industrial-scale runs start from a modelled parameter set, not a linear extrapolation.
    • Dead zones and gradients are identified before they cost a batch.
    • The model becomes a shared reference between R&D in Søborg and operations in Settimo Torinese.

    We've done this before

    See how we do Digital Lab
  • Agents

    AI agents for FDA and EFSA dossier work

    Narrow, reviewable agents that draft the repetitive part of regulatory document work for the dual-track submissions: pulling tables and figures from source records, checking a section against its template, and finding every controlled document a standards change touches. A named regulatory scientist approves every output.

    • Drafting from source records

      First drafts from system data

      Generate the first draft of a safety, toxicology or manufacturing-consistency section directly from Olon's batch records and Chromologics' R&D records, with every figure and table linked back to the source record it came from so the regulatory scientist edits and judges rather than assembles.

    • Dual-track template completeness check

      Gaps found before review

      Compare a draft section against the FDA and EFSA templates in parallel and return the missing elements, formatting issues and inconsistent terminology before the document enters the human review queue, with both templates referenced in the same pass.

    • Change-impact search across the dossier

      Which sections a new guidance touches

      When the FDA or EFSA publishes a guidance change, retrieve every controlled section of the dossier that references the affected topic and rank them by how directly they are affected, so the scope of the update is known on day one.

    • Review queues move faster because documents arrive complete against both templates.
    • The scope of a guidance change is established by search rather than by recollection.
    • Every output is traceable to the source records it came from and signed off by a named reviewer.

    We've done this before

    See how we do Agents
  • Enterprise AI

    Real-time Golden Batch monitoring for pigment consistency

    A process intelligence layer that overlays the current fermentation run against the closest historical Golden Batch profile, with deviation alerts routed to operators in Italy and the process team in Denmark, so colour consistency becomes an in-run signal rather than a release-time measurement.

    • Batch trajectory overlay

      Live run versus best historical profile

      Compare the current batch's trajectory on critical process parameters against the closest Golden Batch profile, with the overlay updated as new telemetry arrives from the Olon-side pipeline so the comparison is in real time rather than at end of run.

    • Automated pigment and yield KPI calculation

      Biological metrics as data

      Calculate growth rate, pigment titer and yield efficiency directly from the telemetry stream so the operators see biological performance indicators next to the process parameters rather than after a separate offline analysis.

    • Deviation alerts routed to both sites

      Where the run drifts

      Configure alerts that fire when the current trajectory leaves the acceptable band around the Golden Batch profile, with the alert routed both to Olon operators on the floor and to the process team in Søborg, so both ends see the same signal at the same time.

    • Colour consistency becomes an in-run property of the process rather than a release-time measurement.
    • Operators in Italy and process owners in Denmark see the same trajectory at the same moment.
    • Golden Batch profiles sharpen over time as more compliant batches accumulate.

    We've done this before

    See how we do Enterprise AI
  • Enterprise AI

    Multivariate analytics for yield and downstream energy

    An analytics layer over the fermentation and downstream processing dataset that identifies the parameter combinations that move titer (grams of pigment per litre per hour) and the operating window that minimises energy per kilogram of pigment in filtration and drying, with results fed back into Olon's run plans.

    • Cross-batch parameter correlation analysis

      Which inputs move yield

      Apply multivariate analysis across the fermentation dataset to identify which combinations of media composition, feeding strategy and process parameters correlate most strongly with high pigment titer, with results ranked by statistical strength and reproducibility across batches.

    • Predictive yield model from initial conditions

      Forecast before the run is over

      Train a yield model on the historical dataset that predicts final pigment titer from early-run signals, with the model retrained as new batches arrive so it stays anchored to the current Olon configuration rather than to the laboratory data.

    • Downstream processing energy model

      Energy per kilogram, not per batch

      Pair the fermentation model with an energy model for filtration and drying that maps flow rate, temperature and residence time to energy use per kilogram of finished pigment, so the run plan can be chosen against the energy per kilogram the customer-facing sustainability claim has to defend.

    • Titer moves because the parameter set is chosen against the model, not against intuition.
    • Energy intensity of downstream processing is reported per kilogram, supporting the sustainability case to investors and customers.
    • The model improves with every batch, with each run leaving behind data that sharpens the next one.

    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 Chromologics ApS's own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
IT/OT integration 25 → 75
Process ownership in Søborg and process execution in Settimo Torinese currently exchange post-run reports rather than live data, so the day-to-day operating picture for the strain and process team is reconstructed from documents rather than read from the fermenter.
Data platform maturity 30 → 80
Strain engineering and process development grew up on Electronic Lab Notebooks and Excel, while manufacturing data arrives from Olon in different formats, so strain lineage, process parameters and finished product data live in separate stores that are stitched together by hand for each query.
Process analytics 35 → 85
There is no real-time Golden Batch overlay between the current run and historical best profiles, and multivariate analysis across the fermentation dataset has not yet been applied systematically to yield optimisation, so the parameter set for each new batch is chosen from prior experience rather than from a model.
Regulatory systems 40 → 90
FDA and EFSA submissions are compiled by hand from Olon records and Chromologics records, which puts the burden of ALCOA+ integrity on manual transcription; a structured content layer that publishes to both templates from a single source dataset would move the integrity check upstream of the dossier.
Supply chain visibility 20 → 70
Manufacturing runs through a single CDMO partner with limited live visibility into raw material inflows and finished goods outflows, so the supply chain commitment to Döhler and downstream food and beverage customers rests on post-run reconciliation rather than on live inventory data.
Sustainability tracking 30 → 75
Filtration and drying parameters are tracked at batch level rather than against an energy per kilogram model, so the sustainability premium Natu.Red can command is reported retrospectively rather than managed during the run, and the board-level expectations from EIFO and Novo Holdings are met by statement rather than by measurement.

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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 Chromologics ApS, 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].