Industrializing brain organoid discovery at single-organoid precision

What we'd propose

• Enterprise AI

  Ontology-based data platform for the organoid estate

  An ontology-based data platform that defines assay, specimen, organoid, instrument and lot as explicit entities, with automated ingestion for high-content imaging, omics and assay output and per-organoid traceability from source cell through final readouts.

  • Automated ingestion from instruments

    Data captured at the source

    Connect liquid handlers, plate readers, high-content imagers and omics instruments so each result lands in the platform with instrument identity, method version and timestamp attached, rather than being moved between systems as CSV files.

  • Shared ontology for organoid data

    One agreed set of entities

    Define assay, specimen, organoid, instrument and lot as explicit entities with agreed relationships, so an AI model trained on a 15,000-organoid estate can find every read-out tied to a specific source cell rather than reconciling across file names.

  • Single-organoid traceability view

    Lineage per organoid

    Build the lineage from source cell through cultivation, microglia co-culture, assay and final result into one queryable view, so the data behind an IND-enabling claim or a pharma partner deliverable can be retrieved without manual reconstruction.

  • Every organoid's lineage and readouts are queryable from one model, which is the data estate single-organoid traceability depends on.
  • AI quality control trains on complete, contextualised data instead of reconciling across disconnected file moves.
  • New instruments and new assays attach to the ontology instead of triggering a parallel data path.

  We've done this before

  • Ontology-Driven Ecosystem for Biomanufacturing Data
  • Real-Time Process Intelligence & KPI Visualization

  See how we do Enterprise AI
• Digital Lab

  OPC UA and MTP architecture for organoid equipment

  An OPC UA and MTP-based equipment integration architecture that connects BrainZell's liquid handlers, maturation incubators, imagers and downstream analytics to the central AI platform through vendor-neutral protocols.

  • OPC UA gateway per instrument family

    Open protocol at the equipment

    Deploy OPC UA servers in front of each vendor's proprietary controller so process values leave each instrument in a documented, vendor-neutral form that the AI platform and downstream analytics can both read.

  • MTP profiles for new equipment

    Plug-and-produce onboarding

    Write Module Type Package profiles for the equipment classes BrainZell uses most, so adding a new maturation incubator or imaging station becomes a configuration task rather than a custom integration project.

  • Segmentation and security baseline

    IEC 62443 zones from day one

    Define the IEC 62443 zones, conduits and remote-access rules that separate the IT estate from the OT estate before commissioning, so the platform's security posture does not have to be re-argued against a live production line later.

  • Adding a new instrument becomes a configuration rather than an integration project.
  • Equipment process data is available to the AI quality control layer in real time, not at the next manual extract.
  • The same architecture carries through to the UK and US expansion sites without re-engineering.

  We've done this before

  • Restoration: Establishing or Re-establishing a Complex Lab Ecosystem
  • Scaling Biotech Manufacturing via Universal IT/OT Architecture

  See how we do Digital Lab
• Digital Lab

  Laboratory execution system and electronic batch records

  A laboratory execution system and electronic batch record that captures each cultivation step, assay and release decision with 21 CFR Part 11 signatures and ALCOA+ data integrity, replacing paper logbooks and unstructured spreadsheets.

  • Electronic batch record per organoid cohort

    Batch record assembled as work runs

    Capture each cultivation step, media change, microglia co-culture event and assay read into a per-cohort batch record with method version, analyst identity and timestamp attached, so the evidence behind a release decision is generated by the system rather than reconstructed for an inspection.

  • 21 CFR Part 11 compliant electronic signatures

    Records that hold up in inspection

    Implement electronic signature, versioning and audit-trail handling to FDA 21 CFR Part 11, so the batch record stands on its own during an FDA inspection of an IND-enabling study or a pharma partner audit.

  • Sandbox view of the AI quality control layer

    Scientist-readable AI decisions

    Expose the AI quality control model's reasoning alongside the batch record, so a scientist can see why the model accepted or flagged a particular organoid before approving the result, which is what makes the platform's automation trustworthy enough to use fully.

  • ALCOA+ evidence is generated as the work runs, not assembled the week before an inspection.
  • A scientist can read why the AI quality control layer accepted an organoid, which is the trust basis for using the platform fully.
  • The same electronic batch record satisfies a UK partner's MHRA (Medicines and Healthcare products Regulatory Agency) expectation and a US partner's FDA expectation.

  We've done this before

  • Digital Transformation of QC Labs: From Paper-Based Legacy to Integrated LES
  • Bridging the Gap Between Scientists and Algorithms

  See how we do Digital Lab
• Digital Lab

  Computer vision and predictive monitoring for organoid health

  Camera-based morphology monitoring and predictive maintenance models that watch the 15,000-organoid estate continuously, surface the small subset of organoids and instruments that need a scientist's attention, and make lights-out operation feasible for a seven-person team.

  • Camera-based morphology monitoring

    Non-invasive organoid inspection

    Mount imaging systems on maturation incubators and run computer vision models that score organoid morphology, growth rate and health indicators continuously, so the team sees the few organoids that need attention rather than the whole estate at once.

  • Predictive maintenance for cultivation equipment

    Equipment failure caught early

    Train models on incubator, liquid-handler and imager sensor data so pump wear, environmental drift and lamp ageing show up as scheduled maintenance rather than as a batch that crashed overnight.

  • Smart alerting routed to the right person

    Alerts that name the organoid

    Categorise alerts by severity and route the small number that need a scientist's eye directly to the on-call rotation, with the affected organoid or instrument identified so the scientist walks to the right workstation rather than triaging a queue.

  • A seven-person team supervises a 15,000-organoid estate because the alerts that reach them name the organoid that needs them.
  • Deviations are caught at the morphology shift rather than at the failed assay.
  • Unplanned incubator and imager downtime is reduced because pump wear and lamp ageing are scheduled, not discovered.

  We've done this before

  • Bioreactor Control: Computer Vision for Non-Invasive Foam Management
  • Proactive Support & Change Management Framework for a Digital Lab

  See how we do Digital Lab
• Agents

  AI agents for IND-enabling and tech-transfer document work

  Narrow AI agents that take the repetitive part of IND-enabling study documentation, pharma partner tech-transfer summaries and controlled-document updates: drafting from source records, checking against a template before review, and finding every document a standards change touches. A named reviewer approves every output.

  • Drafting from source records

    First drafts from platform data

    Generate the first draft of an IND-enabling study section, a tech-transfer summary for a pharma partner or a controlled-document update directly from the underlying platform records, so the author edits and judges rather than assembles.

  • Template and completeness checking

    Gaps found before review

    Check a submitted document against the partner's template and BrainZell's own checklist before the document enters the human review queue, returning missing or inconsistent sections so the queue moves faster.

  • Change-impact search across the controlled document set

    Which documents a change touches

    When a standard, method or 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 of the change.

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

  We've done this before

  • Process Transfer Reporting System

  See how we do Agents