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Algama

Translating microalgae bench science into industrial throughput

Industry
Alternative Protein and Microalgae Ingredients
Headquarters
Paris, Île-de-France, France
Public information as of
March 2026

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

Strategic priorities

Algama is a Paris-region food-technology company that turns microalgae such as Chlorella and Spirulina into functional ingredients for the bakery and seafood sectors. Its principal B2B brands are Tamalga, an egg-replacement ingredient, and OLaLa, a plant-based seafood range. In January 2023 the company closed a roughly 13 million euro Series A led by Thai Union Group, Grupo Bimbo, Noshaq and Newtree Impact, and the funding footprint shifted its investor base from impact capital to corporate strategics that need batch-level visibility into production.

The company opened a pilot biorefinery in the Paris region in November 2024, with support from the Île-de-France Region, and is now building its first industrial site on a 10,000 m2 plot in Liege, Belgium. The stated 2030 target is to operate what Algama describes as the world's first dedicated algae biorefinery for food applications. The Liege facility is the bridge between Paris-region bench and pilot work and B2B-volume production.

Three pressures converge on the digital stack. EFSA Novel Food authorisation takes on average 2.56 years from submission to scientific opinion, and 2024 to 2025 updates tighten requirements for toxicology, allergenicity and nanomaterial characterisation. Foam spikes in 5,000 L-plus photobioreactors can cause overflows, and conventional invasive sensors foul quickly in sticky algae biomass. Investor-side reporting from Thai Union and Grupo Bimbo asks for batch consistency and traceability data that the current Excel-based workflow cannot produce at scale.

Approaching 70 percent of Algama's workforce is made up of engineers and technicians from scientific backgrounds, and the transition from bench-side observation to remote digital supervision is itself a planned change. The digital work ahead is to put the IT and operational technology layers in place before the Liege ramp-up rather than retrofitted afterwards.

Challenges we see

  • Operations Manufacturing

    Moving cracking and extraction protocols from pilot to industrial volumes

    Algama must replicate precise laboratory-level cracking and extraction protocols in 5,000 L-plus photobioreactors at the Liege facility, where biological process non-linearity and environmental sensitivity create yield variability at scale. The Paris-region pilot opened in November 2024 is the bridge to Belgian industrial production.

    Where the same protocol has to perform across two orders of magnitude in vessel volume, the operating envelope learned at pilot scale has to travel with the process rather than being re-discovered at industrial scale, which makes a calibrated reference model for the move a prerequisite rather than an optional extra.

  • Digital Integration

    Joining pilot data to industrial data across Paris and Liege

    R&D and pilot data is tracked in disconnected Excel spreadsheets rather than as live process trends, producing data latency where experimental insights are available days later. The Liege site has to land as a greenfield facility, and IT and operational technology systems are not yet integrated.

    When a validated Golden Batch profile exists in one place and the line that needs to reproduce it sits in another, transferring the profile as data rather than as institutional memory changes what the second site starts from, and the IT/OT decisions taken at greenfield design set the ceiling for that transfer for the next decade.

  • Compliance Regulatory

    Producing EFSA-grade batch evidence across two sites and three jurisdictions

    EFSA Novel Food authorisation averages 2.56 years from submission to scientific opinion. The 2024 to 2025 EFSA updates impose stricter requirements for toxicology, allergenicity and nanomaterial characterisation. Algama also files in parallel through partners in US and Asian jurisdictions.

    Where regulatory evidence is compiled by hand from Excel sources, the bottleneck on review cycles sits in the assembly step rather than in the underlying science, and a structured evidence pipeline lets the dossier reach EFSA complete on first submission rather than after stop-the-clock rework.

  • Operations Manufacturing

    Keeping bioreactor sensing reliable in fouling-prone algae cultures

    Conventional invasive pH and dissolved-oxygen sensors foul quickly in sticky algae biomass, and foam spikes in high-density cultivation can cause liquid overflows and equipment damage. Operators fall back on manual sampling, which introduces contamination risk.

    When sensors cannot be trusted in the medium they are meant to measure, the measurement itself has to move outside the medium, and computer vision and non-contact monitoring turn the fouling problem from an instrumentation problem into a calibration problem.

  • Digital Integration

    Bringing the Liege facility onto a single IT/OT architecture from day one

    The Liege facility is a greenfield site, and equipment selection, network design and the choice of integration standards are all open. Corporate investors Thai Union and Grupo Bimbo expect real-time batch visibility and verifiable sustainability metrics.

    A greenfield plant offers the rare chance to design the data path before equipment is procured, and the standards chosen now determine whether plant data is reachable from enterprise systems in five years' time or whether every new interface is another integration project.

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. Putting Liege's IT and operational technology onto one data platform

    The new Liege facility lacks a unified data infrastructure connecting operational technology equipment data with IT business systems, so production metrics, quality data and ESG indicators would otherwise sit in disconnected systems across Paris and Belgium.

    An ontology-based industrial data platform with automatic data pipelines captures real-time process data from the biorefinery, enables Golden Batch comparisons against the Paris pilot, automates quality reporting for B2B customers, and carries integrated ESG metrics for investors.

    • Algama, Industrial site of 10,000 m2 located in Liege, Belgium, 2023 press communication
    • Algama Deep Research, January 2026

  2. Reading photobioreactor state without fouling-prone sensors

    Conventional invasive sensors used to monitor photobioreactors suffer from fouling in sticky algae biomass, while foam spikes in high-density cultivation can cause overflows and equipment damage. Manual sampling introduces contamination risk and delays batch decisions.

    Camera-based foam detection on photobioreactors and non-contact monitoring provide continuous, reliable bioprocess data without sensor fouling, enable automated anti-foam intervention, and feed a real-time Bio-KPI dashboard for remote supervision.

    • Algama Deep Research, January 2026
    • Algama Foods, vision-systems-and-foam-detection case context

  3. Simulating industrial cracking and extraction before physical runs

    The Paris-region pilot opened in November 2024 is the only bridge to the Belgian industrial site, and there is no digital mechanism to translate bioprocess parameters from pilot-scale to industrial-scale. The risk is months of physical trial-and-error during Liege ramp-up.

    A calibrated digital simulation of the aqueous-lipid phase separation process lets Algama test industrial-scale parameters and run optimisation digitally before committing to physical production, while also generating synthetic training data for downstream process models.

    • Algama, Paris Region Pilot Biorefinery opening, November 2024
    • Digital Twin Maturity Model article, A4BEE

  4. Drafting validated EFSA dossiers from structured lab and pilot data

    EFSA Novel Food dossiers demand exhaustive evidence of safety, composition and production stability, and manual compilation from Excel-based workflows cannot scale to the volume and validation rules required, with stop-the-clock requests adding years to authorisation timelines.

    Digitising lab and pilot workflows into validated data pipelines, and pairing them with narrow AI agents that draft dossier sections from those records, reduces manual compilation effort, surfaces missing evidence before submission, and lets parallel filings across EU, US and Asian markets draw from the same source data.

    • EFSA, Novel food scientific opinion process, 2024 to 2025 updates
    • PMC, Novel food evaluation process delays access to food innovation in the EU

  5. Designing scientist-facing interfaces for the Liege control room

    Around 70 percent of Algama's workforce are engineers and technicians from scientific backgrounds, accustomed to bench-side observation and Excel workflows. Traditional industrial software reads as foreign to that workflow, and a poorly designed interface pushes operators back to manual workarounds.

    A control-room interface designed against the physical P&ID layout of the biorefinery, with role-specific onboarding paths and a sandbox for testing automated controls, builds trust in remote digital supervision and lets the shift from bench-side to remote monitoring happen without losing operational rigour.

    • Algama Deep Research, workforce composition, January 2026
    • Algama Foods, Bridging the Gap Between Scientists and Algorithms case context

What we'd propose

  • Enterprise AI

    IT/OT data platform for the Liege algae biorefinery

    We design and deploy an ontology-based data platform for the Liege facility that connects bioprocess equipment, quality systems and business intelligence into one model, with automatic data pipelines from controllers and enterprise systems so Golden Batch comparisons, B2B customer reporting and ESG metrics all draw from a single source.

    • OPC UA data acquisition layer

      Standardised data off the equipment

      Connect photobioreactor controllers, extraction equipment and drying systems through OPC UA (Open Platform Communications Unified Architecture) or MQTT so process values leave the equipment in a documented vendor-neutral form rather than staying inside closed controllers.

    • Golden Batch analytics engine

      Comparing runs as data

      Build automated batch comparison dashboards that overlay current production against validated Golden Batch profiles from the Paris-region pilot, so deviation against the operating envelope is visible to operators within the batch rather than after it.

    • B2B customer data portal

      Quality certificates as outputs

      Generate batch-level quality certificates and traceability documents for B2B customers such as Thai Union and Grupo Bimbo automatically from the platform, replacing manual Excel-based reporting with documents that carry their own audit trail.

    • One data path serves Paris-region pilot work and Liege industrial production, so Golden Batch profiles travel as data.
    • B2B customers receive batch certificates that are produced by the platform rather than compiled from it.
    • ESG reporting for corporate investors is generated continuously rather than assembled at quarter end.

    We've done this before

    See how we do Enterprise AI
  • Digital CDMO

    Computer-vision monitoring for fouling-prone photobioreactors

    We deploy camera-based foam detection and non-contact monitoring on Algama's photobioreactors and extraction vessels, replacing fouling-prone invasive sensors with vision systems that produce continuous, reliable bioprocess data and trigger automated anti-foam responses before overflow events occur.

    • Foam detection vision system

      Automated anti-foam intervention

      Install camera-based foam detection trained on photobioreactor operating footage, identifying foam formation patterns in real time and triggering anti-foam dosing before overflow events and equipment damage occur.

    • Bio-KPI real-time dashboard

      KPIs matched to the physical layout

      Design intuitive dashboards that display critical bioprocess KPIs such as viable cell density, growth rate and extraction yield in real time, mapped against the P&ID layout of the biorefinery so operators see the process as it sits physically.

    • Anomaly detection engine

      Early warning on contamination and drift

      Run machine learning models trained on historical bioprocess data to detect early signs of contamination, nutrient depletion and environmental drift, so intervention happens before yield is lost rather than after.

    • Sensor fouling and the manual sampling it triggers are taken out of the batch loop.
    • Foam-related product loss and equipment damage are caught before overflow events.
    • Operators supervise the biorefinery remotely with the same fidelity as on the floor.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    Adaptive digital twin for algae cracking and extraction

    We build a calibrated digital simulation of Algama's proprietary aqueous-lipid phase separation and downstream extraction processes, enabling virtual scale-up testing, predictive process optimisation, and accelerated transfer of validated protocols from the Paris-region pilot to the Liege industrial site.

    • Process simulation engine

      Virtual scale-up before physical runs

      Build mechanistic models of the aqueous-lipid phase separation calibrated against Paris-region pilot data, so industrial-scale parameters can be tested virtually before committing to physical production runs at the Liege site.

    • Synthetic data generator

      Training data without extra physical runs

      Generate high-fidelity synthetic bioprocess datasets from digital twin simulations to train downstream predictive models, so model development does not have to wait for costly physical experimental runs at industrial scale.

    • Predictive yield optimiser

      Continuous refinement of extraction parameters

      Run optimisation algorithms that continuously refine extraction parameters such as temperature, pressure and agitation against real-time production data, so the operating envelope tightens as the Liege plant produces more batches.

    • Physical trial-and-error at the Liege ramp-up drops because candidate parameters are pre-screened in simulation.
    • Time-to-production shortens because Paris-region operating envelopes carry over to Liege as calibrated models.
    • The 20x cost advantage is preserved through industrial-scale yields that have been optimised, not discovered.

    We've done this before

    See how we do Digital Lab
  • Digital Lab

    Digital lab and regulatory compliance platform

    We digitise Algama's laboratory workflows and pilot operations into an integrated platform that captures, validates and structures experimental data into regulatory-ready form for EFSA Novel Food submissions and parallel filings across EU, US and Asian markets.

    • Electronic lab notebook integration

      Structured experiment capture

      Replace paper-based and Excel-dependent lab data capture with structured electronic workflows that enforce data integrity rules and link experimental results to regulatory submission requirements automatically, so the record behind an EFSA submission is generated rather than assembled.

    • Automated dossier assembly

      Dossiers compiled from source data

      Build pipelines that compile batch consistency data, toxicology results and compositional analyses into EFSA-compliant dossier formats, with completeness checks against current EFSA templates so missing evidence is surfaced before submission.

    • Multi-jurisdiction compliance tracker

      Parallel filings tracked in one place

      Centralise regulatory submission status across EU (EFSA), US (FDA/GRAS) and Asian markets, with automated alerts for jurisdiction-specific data requirements and submission deadlines, so parallel filings stay aligned without manual coordination.

    • EFSA submission cycles shorten because stop-the-clock rework drops once the data pipeline is structured.
    • Parallel regulatory filings across EU, US and Asia draw from the same validated source data.
    • Manual data compilation errors, the single most common cause of dossier rework, are removed from the path.

    We've done this before

    See how we do Digital Lab
  • Agents

    AI agents for EFSA dossier drafting

    We deploy narrow, reviewable agents that take the repetitive part of regulatory document work for Algama: drafting EFSA dossier sections from validated source records, checking completeness against the current EFSA template before review, and finding every controlled record a regulatory change touches. A named regulatory specialist approves every output.

    • Drafting from validated source records

      First drafts from the data platform

      Generate the first draft of an EFSA dossier section directly from the structured lab and pilot records in the regulatory data platform, so the regulatory specialist edits and judges rather than assembles the document by hand.

    • EFSA template completeness check

      Gaps found before submission

      Run a pre-submission check against the current EFSA Novel Food application template and Algama's own regulatory checklist, returning missing sections or inconsistent entries before the dossier enters internal review.

    • Regulatory change impact search

      Which records a regulatory update touches

      When an EFSA guidance update, a method change or a specification revision lands, retrieve every controlled batch record, dossier section and pilot report that references it and rank them by how directly they are affected.

    • Dossier assembly time drops because the first draft comes from the system rather than from the regulatory team.
    • Stop-the-clock risk falls because completeness is checked against the current EFSA template before submission.
    • Every output is traceable to the source records it came from and signed off by a named regulatory specialist.

    We've done this before

    See how we do Agents

Digital maturity: today and target

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

Source: A4BEE analysis of public sources
Data infrastructure 25 → 80
R&D and pilot data is tracked in Excel spreadsheets, and the Liege site is greenfield, so the data platform work is ahead of the company rather than behind it.
Process automation 30 → 85
Pilot operations rely on manual sampling and subjective observation for foam and sensor monitoring, and industrial-scale photobioreactors will not absorb that approach.
Digital twin and simulation 15 → 75
No digital simulation capability currently exists for scaling cracking and extraction from Paris-region pilot to Liege industrial volumes, so process transfer today relies on physical trial-and-error.
Regulatory data management 20 → 80
EFSA dossier preparation is manual and fragmented across spreadsheets, and multi-jurisdiction compliance tracking is ad hoc without a centralised system.
User experience and adoption 30 → 75
A scientific workforce proficient in bench-side methods needs interfaces designed against the physical P&ID layout and a structured onboarding path to trust remote digital supervision.
Cybersecurity and IT governance 20 → 70
A greenfield Liege facility needs comprehensive operational technology security architecture from the outset, and cross-site data exchange between France and Belgium adds the cross-border dimension.

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This is an independent analysis prepared by A4BEE from publicly available information as of March 2026. It reflects A4BEE's own interpretation and opinion, is not affiliated with, endorsed by, or verified with Algama, 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].