Clinical trials
- EDC
- CRFs by visit, queries, SDV, locks
- Workspace
- Scanned medical records, lab result PDFs, correspondence with sites
From source data to review,a unified EDC and workspace.
Link source data by subject and sample to reduce duplicate entry and manage reviews and change history in one place.
Organize source databy subject and sample.
Keep scans and original files in the workspace, then use OCR and AI to extract values for EDC entry. In the NS-1 example, subject IDs and culture lots link the source records to the EDC records.
Study values in the EDC,source data for review in the workspace.
Each study sets its CRF items and the rules for keeping source data, and links the records it needs to subjects and samples.
Preclinical and cell research
- EDC
- Culture conditions, dosing and medium changes, MEA measurements
- Workspace
- Scanned culture records, microscope photos, time-lapse video
Medical device and robotics research
- EDC
- Function assessments per subject, session results
- Workspace
- Joint, torque and gait logs, session video, test plans
Research references
These external research examples illustrate data types. They do not represent deployments or integrations of our product.
Neural cell research and CL1
Cortical Labs · Melbourne, Australia
Cortical Labs developed CL1 to record signals from neurons grown on a chip and deliver electrical stimulation.
- Data produced
- Electrode signals · culture condition · stimulation settings
Cultured muscle experiments
Institute of Industrial Science, The University of Tokyo
A University of Tokyo team used electrical stimulation to move finger joints with cultured muscle tissue.
- Data produced
- Tissue contractile force · stimulation settings · joint motion video
Brain organoid and robot research
Tianjin University, China
A Tianjin University team introduced MetaBOC to test robot obstacle avoidance and grasping using brain organoid signals.
- Data produced
- Organoid signals · robot motion logs · trial video
Bring scattered records together.Reduce repeated entry and checks.
Read handwritten records and printouts with OCR, and use vision-language models (VL) to describe microscopy images for search. We propose automating EDC entry using the source records and evidence gathered in the workspace, with staff reviewing the results.
- Microscope
- Data tables and graphs
- Lab notes
- Printed graphs
Proposed workflow
- Read with OCR and VL
- Extract values and evidence
- Automate EDC entry
- Staff review and history
Keep source records in the workspaceand link them to the study record.
Use descriptions generated by VL to search microscopy images alongside the originals. Organize photos and videos by research subject and capture date, and instrument logs by experiment or session number and success or failure tags.
Use values and evidence from source recordsto automate EDC entry.
We propose a workflow in which OCR reads record sheets in the workspace and AI extracts values and sources for the CRF fields and enters them in the EDC. The DM, CRC or CRA reviews the entered values against the originals.
- Collect source records
- Extract values and evidence
- Automate EDC entry
- Staff review
Check automatically entered values,and record reviews and changes.
Define CRF fields and the scope of automated entry for each study. Automatically identify values outside the reference ranges, manage Queries, SDV and change histories, and apply Freeze/Lock once review is complete.
Analyze EDC data and source recordsin one dashboard.
Build a research dashboard with Webspace. Link EDC assessments, photos and instrument logs by research subject to compare changes and check the evidence.
EDC assessment trends
D7 · D14 · D28Mean spike rateRecommended: 5–12 Hz
- Week 4 cell viability
- 90.8%
- Week 4 responding channels
- 26ch
Gait success rate
Session 36–4288%ok 37 · fail 5
Keep research data on your local server.Choose what AI can access.
Set access by institution and role, and record AI entries and staff reviews.
- Folders AI can read
- Access by institution and role
- AI entries and staff reviews
- Previous value, reason, author, time
- Local AI
Start fresh or connect your existing EDC.Simplify records, entry and review.
Define automation around your research records and workflow, then plan setup, integration and migration together.
- Test your workflowTest automated entry and review with de-identified records and representative CRFs.
- Set up or connect your EDCConfigure a new EDC for the study, or connect through existing data formats and APIs while keeping your current workflow.
- Migrate selected studiesCheck study status, then run a trial migration and data comparison before moving the data.