☰

IOC

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

Strategic priorities

IOC operates across 4 stated priorities, with the most concrete near-term plan anchored on ai-driven safety ("algorithm in a capsule").

Development and deployment of proprietary AI models for toxicological prediction and ingredient interaction analysis, positioning IOC as the first CDMO to use machine learning for formulation safety at the DNA level.

Integration of pharmacogenetics with predictive analytics to eliminate formulation errors before production begins, reducing batch failures and accelerating time-to-market for new supplements.

Vertical integration through the Indian subsidiary (Eklavya Biotech) ensuring raw material security and quality control from plantation to capsule, reducing dependency on volatile global commodity markets.

Challenges we see

  • Operations Manufacturing

    High-Complexity Softgel Manufacturing Process

    The rotary die softgel encapsulation process requires simultaneous shell formation and ingredient injection within a single machine cycle, making it one of the most complex processes in the CDMO industry with extreme sensitivity to temperature and pressure fluctuations.

    Without autonomous monitoring systems, vacuum deaeration deviations can lead to oxidized fill or seam failure, risking catastrophic batch loss and threatening the company's "Zero Complaint" record.

  • Digital Integration

    IT/OT Validation Gap in AI-Driven R&D

    IOC's proprietary AI model optimizes formulations for bioavailability in 48 hours, but physical validation still relies on traditional lab processes disconnected from the digital predictions.

    The fundamental disconnect between "Top-Floor" AI models (IT) and "Shop-Floor" manufacturing equipment (OT) prevents the AI from being "manufacturing-aware," limiting the ROI on their innovation investment.

  • Digital Operations

    Paper-Based Shadow Processes in Pharmacogenetics

    Despite advanced scientific capabilities, the transition to a truly paperless lab remains incomplete, with scientists maintaining paper workarounds to ensure audit-readiness in high-compliance environments.

    Fragmented data across different lab instruments requires manual aggregation, increasing cognitive load on the R&D team and introducing transcription errors that could threaten formulation integrity.

  • Compliance Regulatory

    Multi-Jurisdictional Regulatory Compliance

    IOC exports to over 30 countries requiring navigation of FDA (USA), EFSA (EU), TGA (Australia), UK FSA, and local authorities like GIS (Poland), each with evolving and sometimes conflicting requirements.

    Manual label review and notification processes create bottlenecks that extend time-to-market and increase the risk of compliance penalties when regulations change rapidly across jurisdictions.

  • Operations Manufacturing

    India-Poland Supply Chain Data Synchronization

    The Indian extraction facility (Eklavya Biotech) operates at 1.8 MT/day capacity, but coordination with Poland relies on ERP data that is often days or weeks old, creating reactive rather than predictive operations.

    Without real-time visibility into Indian extraction processes, the Gdynia plant cannot adjust formulations based on actual ingredient potency variations, reducing the precision of AI-optimized formulas.

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. Production Quality Risk from Manual Monitoring

    The softgel deaeration process in vacuum reactors is monitored by skilled operators who must detect temperature, pressure, and viscosity deviations manually, creating risk of human error during critical manufacturing windows.

    Deploy AI-powered "Watchdog" systems using computer vision to monitor gelatin ribbon formation and seam integrity in real-time, predicting the exact moment deaeration is complete and triggering automated quality interventions.

  2. AI Model Disconnected from Manufacturing Reality

    The proprietary AI toxicological model operates in a digital silo, unable to validate predictions against real-time manufacturing parameters or simulate how formulations will behave in rotary die machines.

    Create a Digital Twin of the production environment that connects the AI model directly to machine parameters, enabling simulation of new formulations before physical production and closing the IT/OT gap.

  3. Data Silos Between Poland and India Operations

    Raw material extraction data from Eklavya Biotech in India reaches the Polish R&D hub with significant latency, preventing real-time formulation adjustments based on actual ingredient characteristics.

    Implement a unified Industrial Data Platform using cloud-native technologies to synchronize the two hubs, allowing the Polish AI model to "see" raw materials as they are harvested and adjust final formulation parameters for natural potency variations.

  4. Manual Regulatory Notification Processes

    Every new product requires formal notification to multiple regulatory bodies and rigorous label review across 30+ export markets, currently handled by a dedicated team through labor-intensive manual processes.

    Automate extraction of formulation data from the Industrial Data Platform to generate compliant labels and submission dossiers, drastically shortening time-to-market while reducing compliance risk.

  5. ESG and Carbon Footprint Tracking Gap

    IOC has committed to sustainable manufacturing including solar power and PCR plastics, but lacks the data infrastructure to track and report carbon footprint metrics across their international supply chain in real-time.

    Deploy IIoT energy monitoring integrated with the Industrial Data Platform to provide 100% transparency on the environmental impact of every capsule produced, meeting demands from ESG-conscious global brands.

What we'd propose

  • Digital CDMO

    Industrial Watchdog for Softgel Quality Assurance

    A retrofit computer vision and sensor system that autonomously monitors the softgel encapsulation process, detecting deviations in vacuum levels, temperature, and gelatin ribbon integrity to prevent batch failures and maintain zero-complaint quality.

    • 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

    Digital Lab Integration for Pharmacogenetics R&D

    A comprehensive lab digitalization program that connects diverse instruments (HPLC, bioprocess monitors, analytical devices) into a unified scientific workspace, eliminating paper workarounds and enabling smooth validation of AI-optimized formulations.

    • 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

    Global Industrial Data Platform for Supply Chain Synchronization

    A cloud-native data platform that creates a "Single Source of Truth" across IOC's Poland headquarters and Indian extraction facility, enabling real-time visibility and predictive coordination of the vertical supply chain.

    • 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
  • Enterprise AI

    Automated Regulatory Compliance Engine

    An intelligent system that extracts formulation data from the Industrial Data Platform to automatically generate compliant product labels and regulatory submission dossiers across IOC's 30+ export markets.

    • 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 CDMO

    ESG & Sustainability Monitoring Platform

    An IIoT-enabled monitoring system that tracks energy consumption, carbon emissions, and sustainable material usage across IOC's manufacturing operations, providing real-time ESG metrics for client reporting and regulatory compliance.

    • 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 IOC's own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
IT/OT Integration 35 → 85
AI models operate in digital silos disconnected from shop-floor equipment; no Digital Twin capability for manufacturing simulation
Lab Digitalization 40 → 90
Paper-based shadow processes persist despite advanced pharmacogenetics capabilities; fragmented data across instruments
Data Platform Maturity 30 → 80
No unified data platform connecting Poland and India operations; ERP provides delayed rather than real-time visibility
Process Automation 45 → 85
Softgel monitoring relies on skilled operators; regulatory processes are manual and labor-intensive
Cybersecurity & Compliance 50 → 80
Basic security measures in place, but increasing attack surface from smart manufacturing requires Zero Trust architecture
Sustainability Analytics 25 → 75
Solar infrastructure exists but lacks energy monitoring; no real-time carbon tracking across supply chain

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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 IOC, 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].