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TrialTrace
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Every mapping reviewed.
Every value traceable.

TrialTrace brings clinical data mapping, validation, review and audit into one workspace. Proposals prepare the work, your data managers and programmers make the decisions, and every decision stays on record.

Explore the live demoWalk through the workflow
Interactive demo · fictional sample study · no sign-up
Synthetic study
OverviewWorkspaceMapping reviewValidationLineage & audit
DemoDR
Synthetic-data workbench

Study overview

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Five connected work areas · synthetic data onlyOpen Study overview in the demo
How it works

From raw exports to a reviewed output, in one place.

01

Bring in your study

Upload synthetic CSV datasets and the protocol. Files are parsed, checksummed and versioned the moment they arrive.

02

Review each proposal

Each mapping comes with a confidence score and rationale. The demo uses a transparent mock proposal engine, and every proposal waits for a reviewer.

03

Resolve what validation finds

Checks run on every run and explain each issue plainly, including whether it started in the source data or in a mapping.

04

Approve and deliver

Approvals record the reviewer and mapping version. Export a reviewed JSON artifact and follow its connected evidence trail.

Who it's for

Built for the people accountable for the data.

Clinical data managers

See readiness across every domain.

Catch data issues early and locate the source row with the evidence already attached.

  • Study readiness at a glance
  • Source issues separated from mapping issues
  • Source rows identified in each finding
See the study overview
Statistical programmers

Spend your time on the judgment calls.

Inspect proposed targets, confidence and reasoning, then approve, reject or change the mapping with a recorded decision.

  • Keyboard-accessible review controls
  • Editable targets with version checks
  • Lineage that connects to output artifacts
See mapping review
Quality & compliance

Answer “who approved this, and why?”

Every decision is recorded as it happens, so nobody has to reconstruct it from email threads before an inspection.

  • Decisions tied to mapping versions
  • Earlier source versions retained
  • Output artifacts with verifiable hashes
See lineage & audit
Governance

Proposals prepare the work. People make the decisions.

Inspect the rules, review each mapping, and follow the evidence. This synthetic-data MVP makes the workflow visible before introducing a live AI provider.

01

Nothing publishes on its own

Proposals stay drafts until a human reviews them. Export also requires current validation.

02

Same inputs, same results

Validation runs as versioned rules, never as a model's opinion.

03

Decisions with context

Each decision records a self-reported reviewer, timestamp, note and mapping version.

04

History you can hand over

Sources, decisions and run snapshots remain connected. This is an application audit trail, not tamper-proof storage.

DeploymentDocker / Vercel + APITrust & scopeSynthetic data only · no certification claimsSystem validationDeterministic checks · not GxP validated

See it with a synthetic study.

Start with the interactive demo, or create a study and upload your own synthetic source files.

Open the workbenchExplore the live demo