☰

RESOLVE

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 RESOLVE's published strategy and is not endorsed by, or produced in cooperation with, RESOLVE. Company website

Strategic priorities

RESOLVE operates across 4 stated priorities, with the most concrete near-term plan anchored on lab 4.0 automation excellence.

Transition MC2 platform to a fully automated benchtop solution eliminating manual interventions, enabling "true multi-modality" combining RNA detection (330 targets) with cyclic immuno-fluorescence for protein markers.

Build a "Digital First" organizational model with dedicated Chief Data AI Officer to position the informatics pipeline as a core product, use deep learning algorithms for cell segmentation and spatial analysis.

Scale dual-hub operations (Monheim R&D/manufacturing, San Jose commercial services) to serve pharmaceutical R&D hubs globally, with strategic EDBI investment signaling Asia-Pacific expansion intent.

Challenges we see

  • Operations Manufacturing

    Cyclic Workflow Operational Bottleneck

    The smFISH process is inherently iterative—each detection round involves fluidic steps (hybridization, washing, stripping) followed by high-resolution imaging, creating throughput constraints governed by physical chemistry rather than digital processing.

    Progressive loss of RNA molecules over multiple cycles (estimated ~40% over 10 cycles) requires precise optimized chemistry; system throughput is fundamentally limited by the number of targets and fluidics speed.

  • Digital Integration

    Data Deluge and Computational Overhead

    The imaging-based nature of Molecular Cartography generates massive volumes of high-resolution data—a single experiment can produce terabytes of raw image files requiring state-of-the-art deep learning algorithms for cell segmentation.

    Computationally intensive workloads demand significant local or cloud-based hardware; any "dropped images" or "imperfections in transcript detection probability" can undermine final data quality.

  • Digital Operations

    Informatics Pipeline Fragmentation

    Users must independently manage "Image processing and decoding," "Cell segmentation and read assignment," and "Cell type identification" steps across disconnected toolsets, with limited standardization.

    Integrating specialized spatial datasets with existing customer data architectures (e.g., scRNA-seq references) remains a significant challenge requiring advanced bioinformatics support unavailable to many customers.

  • Compliance Regulatory

    Clinical Regulatory Transition

    As spatial biology moves from "Discovery-focused" research toward "Clinical laboratory use," requirements for IVDR (Europe) and CLIA (US) compliance become increasingly stringent for cancer diagnostics and pathology applications.

    The move to clinical use requires digital traceability, quality control, and standardized analysis workflows currently still in development across the entire spatial omics industry, creating a barrier to market entry.

  • Operations Operations

    Multi-Site Service Coordination

    Managing global sample logistics and data delivery across San Jose and Monheim service laboratories creates operational complexity as the company scales from research services to pharmaceutical screening contracts.

    Without unified visibility into sample work through the Molecular Cartography workflow—from intake to "My Resolve" portal delivery—operational friction increases and customer trust becomes harder to maintain.

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. Disconnected Global Laboratory Operations

    Resolve's San Jose and Monheim service laboratories operate as primary revenue drivers but lack unified digital visibility into sample status, workflow progress, and data delivery timelines across geographically distributed sites.

    Implement a unified digital "Control Tower" providing real-time visibility into every sample's work through the Molecular Cartography workflow, enabling proactive customer communication and operational optimization.

  2. Manual MC2 Workflow Interventions

    Despite the MC2 being marketed as "fully automated," the platform still requires significant human intervention for sample preparation and data hand-off, limiting throughput for large-scale pharmaceutical screening contracts.

    Develop an "Automated Lab-in-a-Box" orchestration layer that links MC2 fluidics with external robotic sample preparation units, enabling higher throughput and reduced operational constraints.

  3. Clinical AI Validation Gap

    Cell segmentation—a computationally intensive task critical to Molecular Cartography data quality—relies on deep learning algorithms that lack GxP validation frameworks required for clinical diagnostics adoption.

    Create a "Validated AI" platform for cell segmentation and transcript decoding designed specifically for GxP compliance, featuring explainable AI (XAI) models to satisfy clinical regulatory requirements.

  4. IT/OT Manufacturing Silos

    As Resolve scales manufacturing of Molecular Cartography reagents and hardware, the shop-floor operational technology (fluidics and assembly lines) remains disconnected from enterprise-level information technology (ERP, supply chain management).

    Integrate manufacturing OT with enterprise IT systems using Industry 4.0 standards, enabling predictive quality control, supply chain optimization, and smooth scaling of global production capacity.

  5. Cloud-Native Informatics Scalability

    The computational demands of spatial transcriptomics data processing require scalable infrastructure that can handle terabytes of image data, yet Resolve's informatics pipeline lacks cloud-native architecture for elastic scaling.

    Develop cloud-native bioinformatics infrastructure following the OMAPiX/Oracle Cloud model, enabling "Smart Segmentation" tools with A4BEE machine learning expertise to automate cell segmentation across diverse tissue types.

What we'd propose

  • Digital Lab

    Unified Laboratory Control Tower Platform

    A comprehensive digital orchestration platform providing end-to-end visibility and control across Resolve's distributed laboratory operations, from sample intake through data delivery to the "My Resolve" portal.

    • 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 Lab

    Automated Workflow Orchestration Architecture

    An "Automated Lab-in-a-Box" solution that integrates MC2 instrument control with robotic sample preparation, automated scheduling, and closed-loop quality monitoring to maximize throughput for pharmaceutical screening contracts.

    • 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 Lab

    GxP-Validated AI Analysis Platform

    A regulatory-compliant artificial intelligence platform for cell segmentation and transcript decoding, featuring explainable AI models designed specifically to meet IVDR and CLIA requirements for clinical spatial diagnostics.

    • 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

    Manufacturing IT/OT Convergence Platform

    A comprehensive integration architecture bridging Resolve's shop-floor operational technology (MC2 fluidics, reagent manufacturing, assembly lines) with enterprise information systems (ERP, supply chain, quality management).

    • 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
  • Enterprise AI

    Cloud-Native Bioinformatics Infrastructure

    A scalable, cloud-native data platform for spatial transcriptomics analysis, providing elastic compute resources for image processing, standardized bioinformatics pipelines, and secure multi-tenant data management.

    • 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 maturity: today and target

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

Source: A4BEE analysis of public sources
Data Integration 45 → 85
Spatial datasets remain siloed from customer reference data; terabyte-scale imaging data lacks unified pipeline to enterprise systems.
Process Automation 55 → 90
MC2 marketed as "fully automated" but requires significant manual intervention for sample prep and data handoff; cyclic workflow not orchestrated end-to-end.
Cloud Architecture 40 → 85
Partner OMAPiX moved to Oracle Cloud, but Resolve's core informatics infrastructure not cloud-native; lacks elastic scaling for computational workloads.
AI/ML Maturity 50 → 90
Deep learning used for cell segmentation but lacks GxP validation; "Black Box" concerns from Chief Data AI Officer indicate need for explainable AI.
Regulatory Digitalization 35 → 80
IVDR/CLIA compliance infrastructure not yet built; clinical market entry requires digital traceability and QMS automation currently absent.
IT/OT Convergence 40 → 85
Manufacturing OT (fluidics, assembly) disconnected from enterprise IT; leadership's operational excellence vision requires unified architecture.

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 RESOLVE, 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].