Solutions
Built for the teams that carry the clinical data workload.
The same platform, read two ways. CROs feel the effort and the margin. Sponsors feel the inconsistency and the difficulty of reconstructing what happened. Both are costs of the same manual work — the work the platform takes on today, and the base a wider set of life-sciences services is being built from.
CROs
Turn Clinical Data Operations into a Competitive Advantage.
Study build, data capture, and statistical programming are effort-heavy, repeatable, and margin-sensitive. FlashEDC and FlashAnalysis move the mechanical part of that work into tooling that produces consistent, reviewable output — from one protocol read, with the same model architected to reach the wider systems a CRO runs.
Reduced manual study-build effort
Visit structures, forms, fields, and edit checks are generated from the protocol instead of assembled by hand — and the same design is what data is then captured against.
Faster study startup
A reviewable study design is available earlier, so the review that used to gate startup starts sooner.
Consistent study designs
The same generation path across studies produces designs that are comparable rather than individually improvised.
Reduced statistical programming effort
SDTM, ADaM, and TLF production is specification-driven, with derivations recorded rather than re-derived.
Independent QC
QC re-implements the computation instead of re-running production, so it can actually find defects.
Reconciliation
CRO and vendor deliverables are compared against sponsor outputs and differences are surfaced.
Traceability
Outputs connect back to source records, which shortens the path from a query to an answer.
Better margins
Less mechanical re-work per study is less unbillable effort per study.
Pharma & Biotech
Build and analyze clinical data with greater speed, consistency, and traceability.
Sponsors carry the cost of inconsistency and the burden of reconstructing what happened. Clindaddy keeps the study design, the datasets, and the submission artifacts connected to their sources.
Faster study startup
Protocol-to-EDC generation shortens the path from an approved protocol to a buildable study design — and the same design is what the study then captures data against.
Reproducibility
Deterministic execution means the same specification produces the same result on a re-run.
Data lineage
Every output can be traced back through its transformations to the source records behind it.
QC
Independent QC and cross-artifact QC check the work rather than confirming it.
Submission readiness
Define-XML, aCRF, cSDRG, ADRG, and an eCTD-lite package are generated alongside the outputs they describe.
Visibility into vendor deliverables
Reconciliation against CRO and vendor deliverables turns a manual review into a comparison.
Scope
Outcomes we describe, and outcomes we can demonstrate.
The outcomes on this page describe what the platform is built to change — the mechanical effort inside study build, standards transformation, programming, QC, and submission documentation.
They are not measured results from customer deployments, and we do not publish percentages we cannot substantiate. Where a number appears on this site it is an engineering metric, stated as such.
If you want to know what a specific claim is based on, ask us directly. We would rather answer that question than have you assume.
Next step
Tell us where the manual effort sits in your studies.
We will show you the parts of that work the platform takes on today, and the parts it does not.