One data path from cathode to enterprise

What we'd propose

• Enterprise AI

  Industrial data platform connecting SCADA, MES, LIMS and SAP

  A production data architecture and reference pipeline that defines the entities the business shares once — lot, batch, sample, result, material — and loads every source system against the same model, so pCAM yield, energy, reagent and lifecycle-assessment reporting draw on the same numbers the operators see.

  • Production data architecture

    One agreed set of entities

    Define lot, batch, sample, result, material, equipment, and energy as named entities with the relationships between them, so a query written once returns comparable answers across SCADA, MES, LIMS and the SAP S/4HANA layer instead of three dialects of the same table.

  • SCADA, MES and LIMS ingestion

    Connectors into the line

    Build vendor-neutral ingestion from controllers (OPC UA, MQTT), from MES batch records and from the laboratory's LIMS, with schema validation at the boundary so an instrument config change surfaces as a failed record rather than a silent drift.

  • Lifecycle assessment reporting

    Reagent, energy and yield in one view

    Produce the inputs the Ascend Sustainability Report uses — reagent consumption, electricity, natural gas, process yield — from the same model, so an annual filing and a monthly operational read come from the same numbers.

  • Yield, energy and reagent data are computed once and reused across pCAM, ESG and LCA reporting.
  • Each new plant inherits the same connectors and the same model instead of requiring its own integration project.
  • An audit or a tax-credit review draws on the production record rather than a separately-maintained spreadsheet.

  We've done this before

  • Ontology-Driven Ecosystem for Biomanufacturing Data
  • Scaling Biotech Manufacturing via Universal IT/OT Architecture

  See how we do Enterprise AI
• Digital CDMO

  Digital project controls and virtual commissioning for Apex 1

  A project-controls layer tied to the same equipment model the plant will use in steady state, so commissioning evidence, deviation logs and acceptance records remain readable through handover instead of being re-keyed.

  • Equipment model linked to project controls

    One model from construction to operation

    Carry the same equipment identifiers and acceptance criteria through design, construction, commissioning and operation, so the operator inherits a populated model rather than starting from a freshly imported spreadsheet.

  • Commissioning simulation in the line controller

    Validate before physical installation

    Run the line-controller logic against the simulated equipment model, so a control change is exercised on the digital twin before it is loaded onto the physical controller, reducing on-site rework.

  • Cost and deviation reporting through handover

    Project and operating records aligned

    Connect commissioning milestones, deviation logs and acceptance records into a single reporting view, so the same evidence closes out the project and feeds the first quarter of operations.

  • Construction and operations see the same picture of equipment readiness.
  • Commissioning evidence arrives as a by-product of the project, not as a separate retrofit.
  • Lessons captured during commissioning feed directly into the next plant design.

  We've done this before

  • Creating a Digital Twin to Simulate Biopharma Processes
  • An Adaptive Digital Twin for Bioprocess Autonomy

  See how we do Digital CDMO
• Digital CDMO

  AR/VR onboarding and procedure support on the production line

  An immersive training and procedure-support system that lets a new operator rehearse startup, normal running, and the most common deviation scenarios on a virtual replica of the line, and steps an operator through a maintenance task on first encounter with AR overlays.

  • Process line digital twin for training

    Operate a virtual line

    Build a virtual replica of the Hydro-to-Cathode® sequence — leaching, solvent extraction, crystallization — that runs the operator interface and exposes the same trend screens as the SCADA layer, so a trainee behaves the same way on the simulator as on the line.

  • AR-guided procedure support

    Hands-free step-by-step

    Overlay procedure steps and instrument readings on a wearable so a technician receives the next action in their field of view while both hands stay on the task, with the option to escalate to a remote expert when an unexpected condition appears.

  • Procedure authoring and updates

    One source for every version

    Maintain a single procedure definition that feeds both the simulator and the AR overlay, so a procedure change updates both surfaces at once and the training record reflects what operators actually do.

  • New operators reach competent operation on a virtual line before touching reagents.
  • Maintenance and deviation procedures are followed the way they are written.
  • The same procedure content drives both training and live operations.

  We've done this before

  • Transforming Biotech Manufacturing with VR/AR
  • Support Framework & Assisted Reality for Global Biotech Manufacturer

  See how we do Digital CDMO
• Digital CDMO

  Condition-based maintenance for hydrometallurgy equipment

  An instrumentation and analytics layer on the critical equipment — leaching reactors, solvent-extraction mixers, crystallizers, dryers — that turns the maintenance programme from calendar-based visits into condition-based intervention.

  • Equipment-side instrumentation

    Vibration, temperature, current

    Add vibration, temperature and electrical sensing on the critical equipment so a continuous record of each asset's health is available alongside the process record, regardless of equipment vendor.

  • Reliability model

    From sensor data to intervention timing

    Build the reliability model from the recorded signatures, with a clear separation between the equipment health score and the process condition, so the maintenance team sees the reason for an alert alongside the alert itself.

  • Maintenance scheduling and parts planning

    Plan from the same data

    Hand scheduled interventions and the parts list back to the maintenance workflow rather than to an inbox, so the calendar and the workload plan stay aligned with the model's output.

  • Interventions are scheduled when equipment signals drift, not when the calendar says so.
  • Unplanned stops on continuous chemistry equipment fall as condition coverage grows.
  • Maintenance, process-engineering and operations teams read the same equipment state.

  We've done this before

  • Real-Time Process Intelligence & KPI Visualization
  • Proactive Support & Change Management Framework for a Digital Lab

  See how we do Digital CDMO
• Agents

  AI agents for FEOC, 45X and ESG documentation

  Narrow, reviewable agents that take the repetitive part of Ascend's documentation work: drafting supplier declarations and quarterly FEOC files from source records, checking a document against its template before it enters review, and finding every controlled document a standards change affects. A named reviewer approves each output.

  • Drafting from supplier and lot records

    First drafts from system data

    Generate the first draft of a supplier declaration, a Section 45X filing, and a quarterly Section 30D trace directly from the procurement and assay records, so the author edits and judges rather than assembles.

  • Template and completeness checking

    Gaps found before review

    Check a submitted document against the FEOC five-step template and Ascend's own submission checklist, returning missing or inconsistent sections before the document enters the human review queue.

  • Change-impact search across the controlled document set

    Which documents a change touches

    When a tax-credit rule, an OEM expectation or an LCA category 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.

  • FEOC and 45X review queues move faster because documents arrive complete.
  • The scope of a regulatory 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

  • Process Transfer Reporting System
  • Ontology-Driven Ecosystem for Biomanufacturing Data

  See how we do Agents