☰

Tethis

Updating the operating model

Industry
Biotechnology
Public information as of
January 2026

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

Strategic priorities

Tethis operates across 4 stated priorities, with the most concrete near-term plan anchored on standardized sample preparation.

Delivering reproducible, automated liquid biopsy sample processing through the See.d instrument to eliminate variability across global clinical sites

Developing comprehensive cellular and molecular analyses combining CTC capture with plasma genomics for oncology decision support

Transitioning from laboratory prototyping to high-volume manufacturing of nanocoated SBS slides and See.d instruments

Challenges we see

  • Operations Manufacturing

    Nanomanufacturing Yield Variability

    SBS slide production involves depositing nanostructured titanium dioxide onto glass substrates, a process highly sensitive to environmental fluctuations including temperature, humidity, and particulate matter.

    Scaling from small R&D batches to commercial volumes introduces yield inconsistency, threatening clinical trial validity and regulatory submissions.

  • Digital Integration

    See.d Instrument Connectivity Gap

    The See.d instrument processes blood samples in hospital pathology labs but hospital IT environments are notoriously restrictive, creating barriers to smooth LIMS integration.

    Without standardized IT/OT connectivity, the instrument cannot associate physical samples with digital patient IDs, breaking the diagnostic workflow and limiting adoption.

  • Digital Operations

    Joining records across systems

    Tethis generates three distinct data types: process logs from See.d instruments, high-resolution imaging data from SBS slides, and NGS genomic data from plasma analysis, each residing in separate systems.

    Manual correlation of instrument performance with image quality slows AI algorithm training and holds back end-to-end patient analysis.

  • Compliance Regulatory

    RUO to IVDR Regulatory Transition

    The See.d platform and SBS slides are currently Research Use Only but the company targets commercialization under the new EU IVDR, which imposes drastically higher standards for clinical evidence and post-market surveillance.

    Without automated compliance infrastructure, the regulatory pathway will consume excessive resources and delay market entry.

  • Digital Operations

    IP Security for Connected Manufacturing

    Tethis holds proprietary nanocoating technologies and microfluidics IP that represent the company's core competitive advantage.

    As manufacturing equipment connects to cloud systems for data analysis, the risk of IP theft through cyberattack rises significantly.

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. Manufacturing Process Digitalization

    The current R&D-focused manufacturing relies on manual assembly, paper-based batch records, and tribal knowledge, creating quality risks as production scales up.

    Implementing IIoT sensor arrays and digital batch records to correlate environmental parameters with slide quality, enabling a "Golden Batch" model for predictive quality control.

  2. Connected Medical Device Infrastructure

    The See.d instrument lacks durable connectivity layers to integrate with hospital LIMS and transmit secure, bidirectional data between OT and IT systems.

    Deploying IT/OT convergence solutions with OPC UA and HL7/FHIR protocols to make See.d a plug-and-play device in any hospital environment.

  3. Unified Data Platform for AI Development

    Process data, imaging data, and genomic data reside in disconnected silos, preventing the training of end-to-end AI models and slowing diagnostic development.

    Building a centralized Industrial Data Platform that creates a "Digital Patient Twin" for each sample, enabling comprehensive AI training on unified datasets.

  4. Automated IVDR Compliance System

    IVDR requires manufacturers to proactively collect field performance data and maintain ALCOA+ data integrity, overwhelming small teams with documentation burden.

    Configuring See.d instruments to automatically report post-market surveillance data and implementing validated electronic batch records to satisfy regulatory requirements with minimal human intervention.

  5. Cybersecurity for OT Assets

    Connecting proprietary manufacturing equipment and the See.d instrument fleet to cloud systems exposes critical IP and patient data to security threats.

    Implementing Zero Trust security architecture and closed connectivity solutions to create secure wrappers around OT assets while enabling safe cloud data transmission.

What we'd propose

  • Digital CDMO

    Smart Factory Implementation for Nanomanufacturing

    Deploy IIoT sensor infrastructure and digital twin capabilities on SBS slide production lines to achieve predictive quality control and manufacturing consistency at scale.

    • OT/IT convergence

      Pull sensor and controller data off the line into a shared data plane in real time.

      DETAIL

    • Batch intelligence

      Golden-batch comparison and deviation detection running on the same data plane.

      DETAIL

    • Production release flow

      Closed-loop between QA, MES, and ERP so batch record review and release follow the data, not the paperwork.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    Connected Instrument Platform for See.d Fleet

    Engineer a secure IT/OT convergence layer enabling the See.d instrument to integrate smooth with hospital IT networks while supporting remote fleet management and predictive maintenance.

    • Unified data backbone

      Connect instruments and LIMS into a single data spine so QC and CDMO records are queryable across sites.

      DETAIL

    • Paperless workflows

      Move lab execution from paper to instrument-captured records with full audit trail.

      DETAIL

    • Continuous QC release

      Review-by-exception dashboards that flag only the records needing scientist attention.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital Lab
  • Enterprise AI

    Unified Data Platform for Multi-Modal Analytics

    Design and build an Industrial Data Platform that ingests process, imaging, and genomic data streams into a centralized Data Lake, enabling end-to-end AI model development and regulatory-compliant analytics.

    • Ontology layer

      A shared semantic model so lab, process, and quality data describe the same things the same way.

      DETAIL

    • Predictive models

      Models trained on the historical data plane that flag deviations before they become scrap.

      DETAIL

    • Decision surfaces

      Single pane of glass that surfaces model output to the right role at the right moment.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Enterprise AI
  • Digital Lab

    IVDR Compliance Automation Framework

    Implement automated validation workflows, electronic batch records, and post-market surveillance data collection to streamline the regulatory pathway from RUO to full IVDR certification.

    • Unified data backbone

      Connect instruments and LIMS into a single data spine so QC and CDMO records are queryable across sites.

      DETAIL

    • Paperless workflows

      Move lab execution from paper to instrument-captured records with full audit trail.

      DETAIL

    • Continuous QC release

      Review-by-exception dashboards that flag only the records needing scientist attention.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital Lab
  • Digital CDMO

    OT Cybersecurity Architecture

    Implement Zero Trust security principles and closed connectivity solutions to protect proprietary nanocoating IP and patient data while enabling secure cloud integration for advanced analytics.

    • OT/IT convergence

      Pull sensor and controller data off the line into a shared data plane in real time.

      DETAIL

    • Batch intelligence

      Golden-batch comparison and deviation detection running on the same data plane.

      DETAIL

    • Production release flow

      Closed-loop between QA, MES, and ERP so batch record review and release follow the data, not the paperwork.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital CDMO

Digital maturity: today and target

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

Source: A4BEE analysis of public sources
Manufacturing Digitalization 30 → 75
Paper-based batch records and manual assembly; needs IIoT sensors, digital twins, and electronic documentation for scale-up
Data Integration 25 → 80
Three separate data silos (process/imaging/genomic); requires unified data platform with ontology-based harmonization
Instrument Connectivity 35 → 85
See.d lacks standardized hospital IT integration; needs IT/OT convergence layer with OPC UA/HL7 protocols
Regulatory Compliance Infrastructure 40 → 90
Moving from RUO to IVDR requires automated PMS, CSV, and ALCOA+ systems beyond current manual processes
AI/ML Operations 20 → 70
Reliance on external partners for AI; needs internal MLOps infrastructure for SaMD-compliant model lifecycle
Cybersecurity Posture 35 → 80
IP at risk as systems connect to cloud; requires Zero Trust architecture and secure OT connectivity wrappers

Check this yourself

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Related reading

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 Tethis, 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].