Computational design, manufactured at industrial scale

What we'd propose

• Digital CDMO

  IT and OT convergence across the distributed manufacturing network

  We design and implement an IT and OT (information technology / operational technology) architecture that connects bioreactor controllers, sensors and SCADA (supervisory control and data acquisition) systems at Arzeda's toll manufacturing partners to a centralised namespace, so design teams in Seattle see the same process values the operators on the partner floor see.

  • Unified namespace across partner sites

    One semantic layer for process data

    Define an ISA-95 compliant unified namespace using OPC UA and MQTT so instrument tags, batch records and equipment events move between partner OT networks and the Seattle cloud in a documented, vendor-neutral format rather than living in each partner's historian.

  • Edge gateways at each toll manufacturing site

    Local processing before transit

    Deploy edge computing nodes at each partner facility to buffer, time-stamp and pre-process sensor data, so the link between a partner site and Seattle can drop without losing batch context and the cloud receives a complete local view of every run.

  • Real-time KPI and batch dashboard

    Yield and equipment status at a glance

    Build dashboards for yield, temperature, pH, dissolved oxygen and equipment status with configurable alert thresholds, so deviations surface during the batch rather than in the report that follows it.

  • Design and process teams work from the same view of a running batch.
  • Partner OT systems connect to the Seattle cloud without replacing equipment or inheriting partner-specific data formats.
  • Process deviations surface during the batch rather than being reconstructed from sample shipments afterwards.

  We've done this before

  • Scaling Biotech Manufacturing via Universal IT/OT Architecture
  • Transforming Raw Bioprocess Data into Actionable Insights

  See how we do Digital CDMO
• Digital Lab

  Unifying the Seattle R&D laboratory data environment

  We connect the heterogeneous instruments, robotics, ELN and LIMS systems in the Seattle R&D hub into automated data pipelines that deliver structured datasets directly to the Archytas and Scylax deep-learning platforms, so the time between an experiment and an AI-ready record is minutes rather than the next scientist's day.

  • ELN and LIMS integration layer

    One record per experiment

    Implement middleware that connects the existing ELN and LIMS platforms and the laboratory robotics through standardised APIs, so a single experiment carries the same sample, method and result identifiers across every system that records it.

  • Robotics and instrument APIs

    Results captured at the bench

    Build REST and gRPC (two common API protocols) interfaces for automated liquid handlers, DNA synthesis platforms, next-generation sequencers and mass spectrometers, so results stream into the data lake with instrument identity and method version attached instead of being copied from screen exports.

  • FAIR data pipeline for the AI platform

    Training-ready records, not spreadsheets

    Implement ETL (extract, transform, load) pipelines that validate, normalise and contextualise high-throughput screening data into FAIR-compliant datasets, so the Archytas and Scylax models train on records with documented provenance rather than on whatever the last scientist happened to upload.

  • Scientist hours spent on data wrangling return to design and experimental work.
  • AI training cycles reflect the latest experiments rather than the last manual batch upload.
  • Every record carries the provenance required to support a regulatory or audit question.

  We've done this before

  • Creating a Digital Lab for a Global Biotech Player
  • Bridging the Gap Between Scientists and Algorithms

  See how we do Digital Lab
• Enterprise AI

  Digital twin for bioelectrochemical reactor design

  We build a high-fidelity virtual replica of the cell-free bioelectrochemical reactor under the NSF CFIRE programme, so the engineering team can simulate fluid dynamics, thermal stress, enzyme degradation kinetics and voltage-pH-temperature interactions in software before committing capital to a physical reactor.

  • Multi-physics reactor simulation

    Thermal and mass transfer in software

    Model fluid dynamics, thermal parameters, mass transfer coefficients and shear stress distributions for candidate reactor geometries, so the design team can compare performance against the >70 percent energy efficiency target across many configurations before any reactor is built.

  • Enzyme degradation predictor

    Cofactor lifetime under operating conditions

    Integrate machine learning models with the thermodynamic simulation to predict how non-natural cofactors and AI-designed enzymes degrade under different voltage, temperature and pH profiles, so cascade longevity is a property the model optimises for rather than a measurement that surprises the team mid-run.

  • Scenario exploration across design parameters

    Thousands of runs before building one reactor

    Provide a scenario tool that varies voltage, temperature, pH and enzyme loading across thousands of configurations, ranks them against the efficiency target and feeds the top candidates back into the engineering design, so the first physical reactor is informed by a parameter sweep rather than a single prototype.

  • Engineering decisions for the CFIRE reactor are taken against a tested design rather than a single physical prototype.
  • Grant milestones for the NSF programmes can be evidenced with simulation results alongside laboratory data.
  • The same simulation framework carries forward to the next reactor generation rather than being rebuilt each project.

  We've done this before

  • Creating a Digital Twin to Simulate Biopharma Processes
  • Real-Time Process Intelligence & KPI Visualization

  See how we do Enterprise AI
• Enterprise AI

  Continuous traceability for the Non-GMO Project Verified supply chain

  We implement a cloud-based traceability system that gives every batch of ViaLeaf Reb M and every toll-manufactured ingredient a cryptographically continuous chain of custody from stevia leaf feedstock through biocatalytic processing to finished ingredient delivery, so the Non-GMO Project audit is a read against the system rather than a document search.

  • Cryptographic chain of custody

    Tamper-evident batch history

    Issue signed records for each material transfer and quality checkpoint from feedstock receipt through biocatalytic processing to finished ingredient release, so any auditor can reconstruct a batch's history from a single record and verify that it has not been altered.

  • Inline quality analytics at toll sites

    Purity visible during the run

    Integrate inline analytical instruments at partner sites so 95 percent finished-ingredient purity and sensory profile targets are measured continuously during processing rather than at the end, with deviation alerts that reach the operations team before the batch is sealed.

  • Partner compliance portal

    One place for supplier documentation

    Provide a web portal where toll manufacturing partners and raw material suppliers submit certifications, quality records and audit documents, with automated validation against FDA, EFSA (European Food Safety Authority) and Non-GMO Project Verified requirements, so the documentation is reviewed once and reused for every jurisdiction.

  • The Non-GMO Project annual audit is a read against the system rather than a request for documents.
  • Purity deviations at partner sites are visible during the run rather than at batch release.
  • FDA, EFSA and partner compliance documentation is generated from the same record set.

  We've done this before

  • Ontology-Driven Ecosystem for Biomanufacturing Data
  • Digital Transformation of QC Labs: From Paper-Based Legacy to Integrated LES

  See how we do Enterprise AI
• Agents

  AI agents for grant, regulatory and sustainability documentation

  Narrow, reviewable AI agents that draft recurring documents from source records and check completeness before review: NSF grant progress reports, FDA and USDA filings, EFSA dossiers, Non-GMO Project audit packs, tech-transfer summaries for toll partners, and partner-facing sustainability reports for Unilever, W.L. Gore and AAK. A named person approves every output.

  • Drafting from source records

    First drafts assembled from system data

    Generate the first draft of an NSF progress report, a regulatory filing, a tech-transfer summary or a partner sustainability report directly from the underlying system records, so the author edits and judges rather than assembling from emails and shared drives.

  • Template and completeness checking

    Gaps found before review

    Check a submitted document against the relevant template and the site's own checklist, returning missing or inconsistent sections before the document enters the human review queue, so reviewers receive complete drafts rather than outlines.

  • Change impact search across the document set

    Which documents a standards change touches

    When a standard, partner requirement or feedstock specification changes, retrieve every controlled document that references it and rank them by how directly they are affected, so the update scope is known at the start of the change rather than discovered as it lands.

  • NSF grant reports, partner sustainability reports and regulatory filings arrive in review complete rather than in pieces.
  • Tech-transfer summaries for toll partners are drafted from the same records the design team works against.
  • Every output is traceable to the source 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