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Curtis Health Caps S.A.

Making every batch and every dossier inspectable

Industry
Pharmaceutical and Nutraceutical Contract Manufacturing (CDMO)
Headquarters
Wysogotowo, Poland
Public information as of
January 2026

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

Strategic priorities

Curtis Health Caps S.A. produces 1.4 billion softgel capsules and 8 million liquid packages a year from a single Wysogotowo site near Poznań, drawing on a 1,944-raw-material database and a 2,009-SKU portfolio for pharmaceutical and nutraceutical clients. The company marks its 35th year of operation in 2026 and has reframed its public positioning around a trademarked 'CDMO 4.0' programme that frames contract manufacturing as a digital-first service. The scale is mid-sized — 101 to 250 employees — and the model is pure-play, meaning every improvement in quality and throughput returns to the client rather than to a proprietary brand.

Operating priorities are tied to what that scale implies. Softgel drying occupies 24 to 72 hours and runs through humidity-controlled tunnels where the biology of the gelatin shell is the bottleneck. The rotary die lines and liquid packaging lines sit on the shop floor producing some of the throughput, but the data they generate is not yet openly available to the quality and planning teams. The HVAC load for drying is the largest single energy user, and the rising focus of multinational pharmaceutical clients on documented ESG performance puts energy-use evidence alongside product-release evidence on the same shelf.

The route through the SMARTER-CDMO 4.0 programme runs through three connected threads: live environmental and equipment data captured from the drying tunnels and the rotary die lines, a unified laboratory and registration layer that connects the 1,944 raw materials to the 2,009 SKUs, and a digital quality system that keeps the audit trail readable end to end across EU GMP, FDA and EMA scopes. Doing all three on the same data path is what distinguishes a digital CDMO from one that has acquired a series of disconnected tools.

This page reads the situation from publicly available sources and Curtis Health Caps' own publications in late 2025 and early 2026. It sets out five priority workstreams, the data and integration shape they take, and the maturity the company is working from today toward the 2030 horizon.

Challenges we see

  • Operations Manufacturing

    Keeping drying conditions inside the gelatin shell's moisture window

    Softgel drying occupies 24 to 72 hours per batch in humidity-controlled tunnels and is the longest, most energy-intensive step in the production cycle. Gelatin's hygroscopic character means the shells change dimension with relative humidity, and clumping or brittleness losses are the visible failures when the air conditioning drifts.

    Where the drying tunnel runs on fixed setpoints and manual checks, the population under inspection is the whole batch. Reading the actual moisture equilibrium of the shells as the batch runs narrows the inspection to the units that actually deviated.

  • Digital Integration

    Joining the production floor to the planning and quality view

    Rotary die machines, tumble dryers and the liquid packaging lines together produce 1.4 billion softgel capsules and 8 million liquid packages a year, and the production data they generate is not openly available to the enterprise systems that compute OEE (Overall Equipment Effectiveness) or compare raw material variability to line performance.

    Where the line view stays inside the controller, manufacturing, quality and engineering each assemble their own extract from the same data. Streaming the line data into one time-series model lets those three functions compare against the same record.

  • Quality Regulatory

    Closing the laboratory and registration digital gap

    Quality control manages 1,944 raw materials and 2,009 SKUs through paper-based or semi-digital records, and stability study timelines directly affect when product revenue can be recognised. EU GMP and FDA inspectors now treat data integrity as a standalone inspection finding.

    Where laboratory results move between the analyst and the batch record by hand, each transfer is a separate step to perform and to verify. Capturing the result at the instrument and writing it into the batch record as data shortens the release path and tightens the audit trail.

  • Operations Manufacturing

    Capturing changeover knowledge across 2,009 SKUs

    Rotary die machine configuration for 2,009 SKUs draws on the working knowledge of long-tenured technicians, and the changeover load on the lines is one of the constraints on output. Knowledge held by individuals is also knowledge that walks out at the end of a shift change.

    Where the configuration recipe for a SKU lives in one operator's notebook, the same recipe is re-discovered every time a new shift picks it up. Writing the recipe into a MES (Manufacturing Execution System) work order and presenting it with the changeover makes the recipe the artefact, not the person.

  • ESG Sustainability

    Documenting energy and supply chain performance for multinational clients

    The drying tunnel HVAC system is the largest single energy user on the Wysogotowo site, and multinational pharmaceutical clients now require documented ESG performance as part of their supplier qualification. Rising energy and raw material costs over 2022 and 2023 have kept both topics on the board's risk register.

    Where energy and raw material data are reconciled monthly from utility bills and supplier invoices, the published number is the result of a reconstruction. Reading the data continuously exposes the same numbers to operations, finance and the client report on the same day.

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. Reading drying tunnel conditions as data, not as a setpoint

    Drying tunnels run on fixed HVAC setpoints with manual checks, and the gelatin shell's hygroscopic character means that small deviations in humidity or temperature produce visible batch losses. The risk is documented as the largest operational exposure on the site.

    Instrumenting the tunnels with humidity, temperature and air-velocity sensors and streaming the data into a drying model lets the end of drying be predicted from the actual moisture equilibrium, reducing the 24-72 hour cycle and the brittleness or clumping losses.

    • Curtis Health Caps 'CDMO 4.0' programme presentation, 2024
    • CHC company materials on the 1.4 bln softgel capsule annual production

  2. Putting the 1,944 raw materials and 2,009 SKUs onto one data path

    The raw material master data and the SKU registry live in siloed databases, and the registration records that link a SKU to its bill of materials, process settings and stability data are re-typed as the recipe moves from R&D to production.

    An ontology-based data platform that connects raw material, SKU, batch, process setting and stability record once lets the registration, planning and quality functions read the same record, and lets the rotary die configuration be retrieved from the same source.

    • CHC company materials on 1,944 raw materials and 2,009 SKUs
    • CFO board commentary on raw material variability in the 2024 reporting period

  3. Bringing the rotary die and liquid packaging lines onto the enterprise network

    Rotary die machines and liquid packaging lines operate disconnected from the enterprise IT layer, so OEE and other capacity metrics are reconstructed from samples and shift reports rather than read from the line.

    An OPC UA gateway (Open Platform Communications Unified Architecture, a vendor-neutral machine-to-machine protocol) on each line streams process values out of the controller into a time-series model, so OEE, batch genealogy and predictive maintenance signals are generated from the same data the engineers already trust.

    • CDMO 4.0 IT/OT convergence pillar, CHC programme documentation
    • CHC board reporting on the 2024 manual OEE reconstruction process

  4. Connecting laboratory instruments to the batch record

    Paper-based and semi-digital laboratory records slow product release, and re-typed analytical results are exactly the kind of data integrity finding EU GMP and FDA inspectors now flag most often.

    Connecting balances, dissolution testers and chromatography systems to a LIMS (Laboratory Information Management System) with electronic signatures and audit trails per 21 CFR Part 11 (the US rule on electronic records and signatures) cuts the manual transfer out of the release path and makes the evidence chain readable during an inspection.

    • CHC company materials on the 1,944 raw material database and stability study workflows
    • FDA and EMA guidance on data integrity in pharmaceutical quality control

  5. AI agents for EU GMP, FDA and EMA document work

    Deviation reports, change controls, stability summaries, supplier change assessments and periodic reviews all consume specialist time across the Wysogotowo site, and the same document types are re-assembled for each standard change.

    Narrow AI agents draft deviation and change-control summaries from the source records, check a document against its template before it enters the review queue, and find every controlled document a standard change touches, with a named reviewer approving each output.

    • CHC company materials on the EU GMP, FDA and EMA documentation scope
    • Curtis Health Caps R&D and regulatory affairs function description

What we'd propose

  • Digital CDMO

    Drying tunnel instrumentation and end-of-drying prediction

    We instrument the drying tunnels, stream the humidity, temperature and air-velocity data into one time-series model, and run an end-of-drying prediction so the HVAC system responds to the actual moisture equilibrium of the shell rather than to a fixed schedule.

    • Wireless environmental sensing

      Sensors that go on the tunnel

      Install industrial-grade humidity, temperature and air-velocity sensors across the tunnel section, with battery life and radio range sized for the 24-72 hour cycle, so the data is captured at the point of measurement rather than sampled by hand.

    • End-of-drying prediction

      When to stop the cycle

      Build a model of the drying curve for each formulation (standard gelatin, vegan pullulan, fish-oil-filled), so the end of drying is predicted from the actual moisture equilibrium of the shell rather than a fixed 72-hour countdown.

    • Dynamic HVAC adjustment

      Air conditioning that responds to the shell

      Close the loop between sensors and the building automation system so the HVAC setpoints adjust to the actual batch, reducing the brittleness and clumping losses and the energy used in over-conditioning.

    • Drying cycle time comes down toward the lower end of the 24-72 hour window for the formulations that finish early.
    • Brittleness and clumping losses are detected against the running batch rather than the next morning's report.
    • HVAC energy per capsule drops with the shorter cycle, and the same energy data feeds the ESG report.

    We've done this before

    See how we do Digital CDMO
  • Enterprise AI

    Ontology-based data platform for raw materials, SKUs and batches

    A data platform that defines raw material, SKU, batch, process setting, stability record and registration record once, and lets the R&D, planning, production and quality functions read the same record from the same source.

    • Shared CDMO data ontology

      One agreed set of terms

      Define raw material, SKU, batch, process setting, stability record and registration identifier as explicit entities with agreed relationships, so a query written once returns comparable answers across R&D, planning, production and quality instead of four dialects of the same table.

    • Pipelines from LIMS, ERP and the line

      Loading the four systems

      Build ingestion for the laboratory, registration, enterprise and shop-floor systems so the raw material master, the SKU registry, the bill of materials and the line data are written into the shared model with schema validation at the boundary.

    • Analytics and retrieval on top of the model

      Questions answered without an extract

      Expose the model through dashboards and a retrieval layer so R&D, planning, quality and client teams can ask questions of the combined data set without commissioning a new extract for each inquiry.

    • The recipe for a SKU is the same for R&D, planning, production and quality, so a change is reflected once.
    • Raw material variability can be related to line performance in the same model, supporting the board's standing concern.
    • New formulations and new clients attach to the model rather than triggering another migration.

    We've done this before

    See how we do Enterprise AI
  • Digital CDMO

    IT/OT convergence for the rotary die and liquid packaging lines

    An OPC UA gateway architecture for the rotary die machines and the liquid packaging lines, with the equipment data contracts and the network segmentation agreed before the next round of line upgrades.

    • OPC UA gateway deployment

      Connecting the controllers

      Install an OPC UA gateway on each rotary die machine and liquid packaging line so process values, alarms and recipe download all leave the controller in a documented, vendor-neutral form.

    • MES data integration

      From the line to the planning view

      Stream the line data into the MES so batch records, genealogy and OEE are populated as the line runs, supporting the real-time visibility the CDMO 4.0 programme names.

    • Network segmentation and security baseline

      IEC 62443 zones from the start

      Define zones, conduits and remote-access rules to IEC 62443 (the international standard for industrial network security) before the next line upgrade, so the line network is segmented by design and the supplier's remote support path is documented.

    • OEE on the 1.4 billion capsule production is read from the line rather than reconstructed from shift reports.
    • The recipe download and the genealogy flow are properties of the design, not a later integration project.
    • Predictive maintenance is built on the same data the operators already see.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    LIMS connection and electronic batch record for the laboratory

    A LIMS connection that brings QC laboratory instruments into the batch record as data, with electronic signatures and audit trials per 21 CFR Part 11 (the US rule on electronic records and signatures) so the analytical result reaches the release decision as a record rather than a transcription.

    • Instrument integration

      Results captured at source

      Connect balances, dissolution testers, HPLC (High-Performance Liquid Chromatography) systems and plate readers so the analytical result is captured with instrument identity, method version and timestamp attached instead of being read from a screen and re-typed.

    • LIMS to batch record data flow

      Lab result to batch release

      Map the laboratory's sample and result records onto the batch record so the release decision can be traced back to the specific analytical run that produced each number, and the audit trail is generated by the instruments rather than assembled for the inspector.

    • 21 CFR Part 11 audit trail

      Electronic records that hold up

      Implement electronic signature, versioning and audit trail handling to 21 CFR Part 11, the US rule on electronic records and signatures, so the evidence chain stands on its own during an EU GMP, FDA or EMA inspection.

    • Fewer manual transfers between the laboratory and the batch record, and fewer of them to verify.
    • Product release waits on the analytical result, not on the paperwork that follows it.
    • Inspection questions are answered from the record itself rather than from a reconstruction.

    We've done this before

    See how we do Digital Lab
  • Agents

    AI agents for EU GMP, FDA and EMA document work

    Narrow, reviewable agents that take the repetitive part of the documentation load: drafting deviation and change-control summaries from source records, checking a document against its template before review, and finding every controlled document a standard change touches. A named person approves every output.

    • Drafting from source records

      First drafts from the system

      Generate the first draft of a deviation report, change-control summary, periodic review or supplier change assessment directly from the underlying LIMS, MES and registration records, so the author edits and judges rather than assembles.

    • Template and completeness checking

      Gaps caught before review

      Check a submitted document against the EU GMP, FDA or EMA template and the site's own checklist, returning missing or inconsistent sections before it enters the human review queue.

    • Change impact search across the document set

      Which documents a change touches

      When a pharmacopoeia monography, a supplier specification or a process setting 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.

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

    We've done this before

    See how we do Agents

Digital maturity: today and target

Scored out of 100 across six dimensions. The target is what Curtis Health Caps S.A.'s own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Drying process data 25 → 75
The drying tunnel runs on fixed HVAC setpoints and manual checks, and the 24-72 hour drying cycle is reflected in the published operating metrics. Continuous monitoring of humidity, temperature and shell moisture is the named programme area.
Raw material data 30 → 80
The 1,944 raw material database and the 2,009 SKU registry exist in siloed form, and the connection to the registration and production data is reconstructed by hand. The CDMO 4.0 programme names data integration as the foundation.
Equipment to enterprise connectivity 28 → 78
Rotary die machines and liquid packaging lines run disconnected from the enterprise IT layer, so OEE is reconstructed from samples. The IT/OT convergence work is the named programme area.
Laboratory digitalization 35 → 85
Paper-based and semi-digital laboratory records are documented in the v2 source, and the EU GMP, FDA and EMA inspection scope puts data integrity on the critical path. Connecting instruments to the batch record is the named programme area.
Documentation and change control 40 → 80
EU GMP, FDA and EMA dossier work is recurring, and the standing board topic of supplier change and raw material variability puts the same document types in the queue every quarter. The terms-of-trade move every time the regulator publishes.
Energy and ESG reporting 30 → 75
The drying tunnel HVAC is the largest single energy user on the site, and the documented evidence flowing to multinational pharmaceutical clients has to be readable on the same day the data is produced. Reading the energy data continuously is the underlying requirement.

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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 Curtis Health Caps S.A., 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].