Open the source again and enter it into the CRF.
SetFN EDCResearch data and source documents.
Research data and source documents.
One connected workflow.
From hospital source records to CRF entry, queries, SDV and change history. Keep the journey from data creation to review together in one study workspace.
EDC goes beyond data entry.
It connects the study’s review process.
Collect case report data electronically and connect participants, visits, queries, SDV, status changes and history. SetFN extends this foundation to source records and the research team’s day-to-day collaboration.
eCRF Design and versionsSubject Participants and visitsReview Queries and SDVControl Freeze and lockTraceAudit Trail
Research teams re-enter values
that have already been recorded.
Hospital source records are separate from the CRFs. Even after entry, teams must compare the same records again and trace why values were changed.
Records already created at the hospital
Source records capture what happened and when the patient received care.
What the research team repeats
The CRA compares source records with entered values again.
Search emails and files for the basis of a correction.
Keep source records beside the CRF. Turn repeated checking into focused review.
Bring research material beyond the EDC
into evidence AI can work with.
Keep email, chat, comments, case record photos, source documents and research results in SetFN Workspace. Within study permissions, configure RAG and local AI agents to find and summarize evidence and suggest CRF values and review items with sources attached.
Keep files, email, photos and conversations as project data.
Use only the information each role may access as evidence for study RAG.
Find and summarize evidence, and suggest CRF values with sources attached.
Data managers, CRCs and CRAs check the originals and decide on corrections, approvals and exceptions.
Bring reviewed results and task states into EDC, Mail and Workflow.
Study-specific RAG
Find answers and source locations in permitted workspace documents and conversations.
Controlled local AI
Read permitted data without sending it outside, and prepare summaries, comparisons and candidate values.
EDC extension plugins
Connect human-reviewed suggestions and required task states to the existing EDC workflow.
The core study workflow
is already connected in the product.
Go beyond form design. Open queries from participant data, record SDV status, and revisit subsequent activity and changes.
Turn the study structure into data entry forms.
Configure forms, fields, versions and roles as the starting point for study operations.
From checking every value again
to verifying what needs attention.
Work from the same source records and CRFs, with tasks and access separated by role.
Everyone starts from the same information.
Access source records, study documents and AI analysis according to your role and permissions.
Review suggested values and complete missing source records.
Focus review on low-confidence items, discrepancies and exceptions.
Compare permitted source records with CRFs and record SDV status.
Review site progress, unresolved queries and lock status.
See the flow across data, documents and communications.
Keep research information moving beyond inboxes
and individual PCs.
Keep it with the study.
Save institutional emails and attachments, team conversations, case record photos, analysis and results as project files. Workspace AI can search and summarize the material to help staff find supporting evidence and the next task.
Storage, access, AI read and write scope, and EDC integration are configured and validated for your study environment during enterprise plugin implementation.Email and attachmentsChat and commentsCase record photosStudy results
SetFN research workspaceFrom a folder of files to a connected foundation for your study
File retention · Permissions · Search and summaries · Evidence-linked entry suggestions
As your files grow,
so does the context available to your study.
SetFN Workspace indexes study files and conversations within permission boundaries, making them useful again when you need them.
Research workspaceRAG · Local AI
Case record photosTest resultsEmail and attachmentsChat and commentsProtocolsStudy resultsAsk a question and open the relevant documents and source locations directly.
Bring together site progress, missing records and key points from long conversations in the context of the study.
Find candidate CRF values in source records and attach references for human review.
Before searching through everything,
see what needs a closer look.
Local AI compares permitted source records and CRFs first, gathering possible discrepancies, omissions and review items with source locations. CRAs and data managers can open the prepared originals, make a judgment and record SDV results.
Work people prepared manually
Site visits and file searches
Comparing every source record and transcribed value
Requesting the same records and emails again
Work AI helps prepare
Link source records to candidate CRF values
Identify possible discrepancies, omissions and exceptions
Build a review queue with source locations
AI prepares material and evidence for review. The responsible staff make final confirmations and exception decisions.
Trust comes from being able
to check again.
Which source record supports this suggested value?
Can AI suggestions be distinguished from human corrections?
Who performed queries and SDV, and when?
When a value changes, are the previous value and reason retained?
Can related emails and original attachments be opened again?
Features alone do not establish regulatory compliance. The scope of permissions, electronic records, retention, recovery and validation deliverables is defined according to the customer’s intended use and risk assessment.
Your organization decides
what AI may read and write.
Convenience alone is not a basis for sending research data to external services. Define deployment location, models, access and logging first, then configure study RAG and local AI agents within those boundaries.
SetFN Workspace
Keep study files and conversations on a server your organization chooses.
Study RAG and local AI
Read only permitted projects and folders, and produce results with sources.
Enterprise extension plugins
Connect reviewed results and task states with EDC, Mail and Workflow.
Data boundariesDefine storage location, external transfer rules and available models.
Action permissionsSeparate who may read, suggest and approve each type of information.
Review recordsDesign records to distinguish AI output from human corrections and confirmations.
Define the scope of adoption
in three clear stages.
Separate what you can see in the product now, what needs integration for your environment, and what must be validated before operation.
1. Features available to demonstrate now
See CRF design and versioning, participant and visit management, queries, SDV, data locking and change history.
Review how study files, emails, attachments and conversations can be stored and searched together.
2. Capabilities to connect for your environment
Connect your existing EDC, EMR and laboratory systems with source record locations.
Configure human review of evidence-linked CRF suggestions, then connect follow-up tasks and schedules.
3. Items to agree before live operation
Agree on role-based access, privacy, retention, backup, recovery and the scope of AI processing.
Review test results, change management and the responsibilities of the customer and SetFN against the study purpose and applicable requirements.
Before buying a product,
validate it against your study’s workflow.
Use representative, de-identified source records and CRFs to assess entry suggestions, queries, SDV and audit workflows.
Review study structure, roles and current EDC, document and email workflows.
Use de-identified source records and key CRFs to test entry, review and SDV.
Define deployment, security, retention, validation and shared responsibilities.
Apply enterprise plugins and expand integration, training and operations.
Extend only what you need with enterprise plugins.
Use SetFN Workspace as the foundation for study information, then connect EDC, Mail, Workflow, Schedule, study RAG and local AI agents as needed. Enterprise licensing is proposed around data transfer policies, roles, permissions, validation and operational support.
You don’t have to replace it today.
Connect first. Migrate when it makes sense.
Replacing a live EDC all at once creates migration and revalidation work. Keep the current system and first connect the evidence that supports your study: sites, participants, visits, CRFs, source records, queries and change history.
Use SetFN Workspace to connect source and collaboration material without interrupting current entry and review work.
Map site, participant, visit and CRF structures to source records, queries, SDV and change history to retain context and traceability.
When replacement is needed, assess export formats and APIs, then migrate study by study after trial transfers, reconciliation and transition planning.
Migration scope and method are determined after assessing the existing EDC’s exports and APIs, study status and applicable requirements.
Whether you’re starting fresh or moving from another EDC,
start with your actual study data.
Use real CRFs and source records to assess adoption scope, integration and migration options together.