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Better Dairy Limited

Connecting strain, process, and product data

Industry
Precision fermentation (food ingredients)
Headquarters
London, United Kingdom
Public information as of
January 2026

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

Strategic priorities

Better Dairy is a London precision-fermentation company producing animal-free dairy proteins, with Osteopontin — a highly phosphorylated bioactive protein used in infant formula — at the centre of its current product focus. The company has said it is working through scale-up trials and aiming for Self-Affirmed GRAS (Generally Recognized As Safe) status in the United States by Q1 2026.

Production happens through a network of contract development and manufacturing organizations rather than on a Better Dairy site. The internal lab at International House, Holborn Viaduct covers strain engineering and bench-scale fermentation, while larger-scale fermentation runs at external partners. The most recent published funding round was a $22 million Series A in February 2022 led by Redalpine and Vorwerk Ventures with participation from Happiness Capital.

The operating model — bench-scale R&D in London, larger-scale runs at partners, and a US regulatory dossier — leans on the same capability: a data layer that moves strain design, fermentation parameters and quality results together, so an experiment at 5 litres can be compared honestly with a run at 50,000 litres and the evidence for a regulatory submission can be assembled without retyping.

Better Dairy is participating in the UK Food Standards Agency Regulatory Sandbox for precision-fermentation products, adding a UK evidence path to the US GRAS work. Both submissions require the same per-batch traceable data, so good data plumbing serves both routes at once.

Challenges we see

  • Operations Manufacturing

    Predicting how a fungal process behaves at commercial scale

    Filamentous fungi grow as branching hyphae that increase broth viscosity and change the non-Newtonian behaviour of the fermentation, which affects oxygen transfer (kLa, the volumetric mass-transfer coefficient) and heat dissipation in large bioreactors. Process parameters optimised in 5-litre glass vessels in London have to be re-validated at 50,000-litre or larger steel tanks at external partners.

    At larger scales, the variables that can be controlled at bench scale no longer behave the same way, so the qualification of a new scale depends on models and data that capture the rheology of the fungal broth rather than on intuition carried over from the lab.

  • Digital Integration

    Correlating strain design with fermentation outcome in real time

    Strain engineering data lives in Benchling, fermentation parameters live in the bioreactor controllers, and downstream quality results (HPLC, high-performance liquid chromatography; mass spectrometry) live in separate instruments. The three data sets are difficult to bring together at the moment a scientist is reviewing a run.

    The feedback loop between designing a strain and seeing how it performed in the fermenter is set by how quickly the data from each step can be brought together, so the iteration rate of strain development depends on the data plumbing rather than on the biology alone.

  • Operations Manufacturing

    Maintaining visibility into CDMO fermentation runs

    Better Dairy transfers its biological recipes to external contract manufacturers through standard operating procedures, and currently receives batch performance as post-run documents rather than live data.

    As more commercial commitments depend on production at external sites, the ability to see and discuss a run as it is happening becomes part of the qualification work, rather than something left to the post-run report.

  • Compliance Regulatory

    Producing regulatory dossiers for multiple jurisdictions

    Self-Affirmed GRAS in the US, the UK FSA Regulatory Sandbox, and potential EU EFSA (European Food Safety Authority) engagement each require different structures of evidence. The data needed for the US submission differs from the data needed for the UK process, and the same underlying record has to be reformatted for each.

    Where the same record has to be projected into different submission structures, the cost of the submission is set by how much of the formatting can be done from a single source of data rather than by the size of the underlying experiment.

  • Digital Operations

    Verifying phosphorylation patterns on the release path

    Osteopontin's commercial value depends on the protein carrying the correct phosphorylation pattern. Verification relies on offline mass spectrometry, which sits between production and a batch release decision.

    When the analytical result that defines a batch's fitness is produced by an instrument downstream of the fermenter, the timing of the release decision is set by how quickly that result can be turned into a clear verdict rather than by the fermentation itself.

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. Modelling fungal fermentation before paying for a CDMO run

    Filamentous fungi change the rheology of the broth at scale, so parameters that work in 5-litre glass bioreactors can fail in 50,000-litre steel tanks or at the intermediate pilot scales. Better Dairy has said it is iterating on this transition through CDMO trials.

    A digital twin that combines computational fluid dynamics with biological kinetic models lets Better Dairy explore agitation, aeration and feeding strategies in software before committing a physical batch, shortening the path to a scale where the organism performs as designed.

    • Better Dairy targets high-margin nutrition market with precision-fermented osteopontin, AgFunderNews, 2025
    • Conversation with Jevan Nagarajah of Better Dairy, FoodTech Weekly, 2024

  2. Connecting strain, process and product data in one place

    Strain engineering records, fermentation parameters and analytical results live in separate systems, and the scientist designing a strain cannot see the fermentation data until summary reports arrive days or weeks later.

    A single data layer that pulls Benchling, bioreactor controllers and analytical instruments into a shared store makes the design-to-outcome loop visible in hours rather than weeks, and gives the strain engineering team a queryable history of every run.

    • Better Dairy targets high-margin nutrition market with precision-fermented osteopontin, AgFunderNews, 2025
    • Better Dairy recruitment activity, 2024–2025

  3. Seeing a CDMO run while it is still running

    Better Dairy transfers its process to external manufacturers through documents and receives batch performance as a post-run report, so underperforming batches are observed only after the run has ended.

    A remote monitoring view of partner fermentation runs — reading pH, dissolved oxygen, temperature and agitation as the run progresses — gives the London process team early sight of a deviation and a starting point for a conversation with the partner.

    • Better Dairy Deep Research, organisational cartography, 2026
    • Redalpine leads $22M Series A in Better Dairy, 2022

  4. Turning the regulated record into a structured submission

    Self-Affirmed GRAS, the UK FSA Sandbox and any EU EFSA work each require the same underlying data in different shapes, and the formatting is currently done by hand from the source records.

    A regulatory information management layer that holds a single, validated set of records and emits them in the structure required for each jurisdiction cuts the time scientists spend reformatting evidence and reduces the chance of a finding against data integrity.

    • Better Dairy targets high-margin nutrition market with precision-fermented osteopontin, AgFunderNews, 2025
    • FSA launches pioneering regulatory programme for cell-cultivated products, 2024

  5. Speeding up the phosphorylation check on a finished batch

    The phosphorylation pattern that defines Osteopontin's commercial value is measured by offline mass spectrometry, which sits between production and the release decision for a batch.

    Pattern-matching analysis on the mass spectrometry output shortens the time from sample to verdict, so the batch release decision moves from days to hours without changing what the verification shows.

    • Better Dairy targets high-margin nutrition market with precision-fermented osteopontin, AgFunderNews, 2025

What we'd propose

  • Digital Lab

    A digital twin for fungal fermentation at bench and pilot scale

    A modelling environment that combines computational fluid dynamics with biological kinetics so the team can predict how a filamentous-fungi process will behave at bench, pilot and commercial scale before committing a physical batch to a CDMO.

    • Rheology and mass-transfer coupled to kinetics

      Modelling the physics of the broth

      Build a CFD model (Computational Fluid Dynamics — numerical simulation of fluid flow) that captures the non-Newtonian behaviour of the fungal broth and couples it to growth and product-formation kinetics, so the model predicts oxygen transfer and shear stress rather than assuming a well-mixed tank.

    • Virtual experiments at scale

      Scenarios before a CDMO run

      Run hundreds of virtual experiments per week to test agitation, aeration and feeding strategies, then carry the best candidates forward as physical runs.

    • Soft sensors for unmeasured variables

      Estimate what the probe cannot

      Deploy inferential estimators (software models that produce a real-time estimate from other available measurements) that calculate biomass and product concentration from off-gas, agitator power, pH and feed, so the process is visible even when physical sensors foul in viscous broths.

    • Process parameters are chosen from modelled outcomes, not from empirical CDMO trials.
    • The same model travels with the process into the regulatory dossier, where the rationale for a chosen operating range is documented.
    • Shear-sensitive fungal strains are characterised against the geometry of the receiving vessel before tech transfer.

    We've done this before

    See how we do Digital Lab
  • Enterprise AI

    A unified data layer for strain, process and product

    A data platform that ingests strain engineering records from Benchling, fermentation parameters from the in-house bioreactors and analytical results from quality instruments, and presents them as one queryable history per experiment.

    • Multi-source ingestion

      Strain, process, product in one stream

      Pipelines pull records from Benchling, from the lab bioreactor controllers and from the analytical instruments (HPLC, mass spectrometry), with the schema for each source made explicit at the boundary so a downstream consumer does not have to know which instrument produced a value.

    • Contextualised experiment view

      One record per experiment

      A scientist designing a strain can see the fermentation parameters and the analytical results for the same experimental unit in a single view, with the relationships between the three sources kept consistent.

    • Reference run comparison

      Today's run against the golden batch

      A current run can be overlaid against a curated set of reference runs so deviations in titer, yield or phosphorylation efficiency are visible during the experiment rather than after a summary report.

    • Iteration time on strain design moves from days to hours.
    • Historical runs become a queryable asset for the next engineering decision rather than a file archive.
    • The same data set feeds the regulatory submission, the strain engineering review and the CDMO tech transfer.

    We've done this before

    See how we do Enterprise AI
  • Digital CDMO

    Remote monitoring for CDMO fermentation runs

    A secure, read-only monitoring path from the partner control system to a London view, so the Better Dairy process team can follow a CDMO run as it is happening and discuss a deviation the same day.

    • Secure edge connection at the partner

      Read-only, agreed scope

      An edge device at the partner site publishes an agreed set of process tags outward only, with no inbound control path. Scope, retention and access are set by the data agreement with each manufacturer.

    • Live batch view in London

      Same page as the partner operator

      Fermentation parameters — pH, dissolved oxygen, temperature, agitation — are shown against the recipe and the last reference run, so the London team can see what the partner operator sees.

    • Documented recipe handoff

      Digital record, not a PDF

      The process recipe is held in a machine-readable structure rather than in a document, so the parameters the partner runs against are the same parameters the London team sees, with the differences between the two sites made explicit.

    • Process questions are raised during the run, not at the post-run review.
    • Recipe differences between sites are visible to both organisations rather than implicit.
    • Yield and quality work starts from the run data instead of from a reconstructed timeline.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    Regulatory information management for GRAS and the FSA Sandbox

    A structured, validated record of the data that supports the US Self-Affirmed GRAS submission and the UK FSA Sandbox work, with the regulatory information management layer emitting the structure required for each jurisdiction from the same source.

    • Validated, audit-ready record

      ALCOA+ by construction

      The lab systems (LIMS, ELN) are validated to 21 CFR Part 11 (the US FDA rule on electronic records and electronic signatures), with the attribution, legibility, contemporaneity, originality and accuracy (ALCOA+) principles built into the data capture so the regulatory record is the system record.

    • Submission-shaped outputs

      One record, more than one format

      The same validated record is emitted in the structure required for Self-Affirmed GRAS, the UK FSA Sandbox and any future EFSA engagement, with each output traceable to the source data point.

    • Chain-of-custody for GMM removal

      Documented purification chain

      A traceable chain of custody records the purification steps that remove recombinant DNA from the protein product, supporting the nature-identical claims the regulatory submission depends on.

    • Scientists spend less time reformatting the same data for different jurisdictions.
    • The submission arrives with a data integrity story, which is the part of a GRAS audit most exposed to findings.
    • Future jurisdictions connect to the same source record rather than triggering a new collation.

    We've done this before

    See how we do Digital Lab
  • Agents

    AI agents for strain development documentation and regulatory drafting

    Narrow, reviewable agents that draft the first version of deviation reports, change-control summaries and periodic review documents from the source records, and check a submission draft against the template before it enters the human review queue. A named person approves every output.

    • Drafting from source records

      First drafts from the data

      Generate the first draft of a strain development summary, a change-control document or a periodic review directly from the underlying system records, so the author edits and judges rather than assembles.

    • Template and completeness check

      Gaps before review

      Check a document against the template and the site's own checklist before the document enters the human review queue, returning the missing or inconsistent sections so the reviewer sees the document complete.

    • Change-impact search across the document set

      What a standards change touches

      When a regulatory standard, a method or a specification changes, retrieve every controlled document that references it and rank them by how directly they are affected, so the update scope is known on day one.

    • Review queues move faster because documents arrive complete.
    • The scope of a standards 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

Where QB Systems fits

Alongside our services we build QB Systems, hardware and software for bioprocess control. QB Systems is a product brand of A4BEE Sp. z o.o.

Applications
Bioreactors

Software-defined control for a bioreactor — a new QB vessel, an upgrade to one you have, or a retrofit of the existing PLC.

Automated sampling

Automated sampling from 4–18 sources, aseptic-capable and up to 72 hours unattended. Works with any vendor's bioreactor.

Deployment
Retrofit

Existing equipment keeps running; QB takes over the PLC, or reads from it without touching control.

Scale
Benchtop (1–8 L)

Glass vessels with the complete hardware and software stack. This is the core range for development work.

Digital maturity: today and target

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

Source: A4BEE analysis of public sources
Strain to process data 22 → 75
Strain engineering records, fermentation parameters and analytical results sit in separate systems — Benchling, bioreactor controllers and HPLC or mass spectrometry — with no single view of one experiment.
Bioreactor process control 28 → 80
The London R&D line runs bench-scale bioreactors for strain validation; the published scale-up work is described as iterative rather than predictive, and the move to fungal hosts adds a rheological dimension the current control layer does not explicitly handle.
Visibility into CDMO production 18 → 70
CDMO batches reach London as post-run documents rather than as live data, so the partner runs are reviewed after the batch has ended.
Quality data integrity 30 → 85
Self-Affirmed GRAS and the UK FSA Sandbox both require a 21 CFR Part 11-style record with the ALCOA+ principles intact; the published work does not describe a validated lab system in place today.
Digital batch record 20 → 78
A batch at Better Dairy spans strain engineering, fermentation and downstream analytical steps at different sites, with no shared record joining the three parts of the batch.
Scale-up modelling 15 → 72
The migration from yeast to filamentous fungi is being worked through scale-up trials at CDMOs, and the published commentary on the fungal host implies the qualification is empirical rather than modelled.

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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 Better Dairy Limited, 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].