Our Protocol Design Intelligence Suite transforms traditionally manual, fragmented processes into a dynamic, insight-rich design environment. Built on the principles of augmentation—not replacement—it empowers trial architects with verifiable evidence, interactive modeling, and end-to-end traceability.
Proactively identifies design elements and operational risks that could lead to future protocol amendments.
Optimizes patient visits, assessments, and procedures to balance scientific validity with operational efficiency.
Evaluates protocol readiness for global deployment and identifies required region-specific adaptations.
Ceresity's AI-driven Digital Data Flow transforms clinical trials from document-heavy processes into structured, interoperable, and reusable data assets — traceable across the entire study lifecycle.
// DIGITAL SYNAPSE • LIVE TRACING
study.crf • structured metadata
auto-mapped • 47 variables
ADSL • ADAE • ADEFF
auto-generated • 23 outputs
END-TO-END ARCHITECTURE
Digital Synapse establishes structured, persistent linkages across every layer of the clinical trial lifecycle — from study design inputs through to final reporting outputs.
PROTOCOL
Procedures, SoA, study design parameters
ACRF
Structured data collection fields & metadata
SDTM
Standardized clinical datasets & variables
ADAM
Analysis-ready data with derivation logic
TLF
Auto-generated tables, listings & figures
REPORT
Validated, submission-ready reporting
DIGITAL SYNAPSE — Persistent Traceability Layer
Automated linkage maintained continuously across all layers. Any change propagates instantly. Full audit trail preserved.
PLATFORM VALUE
From automation to regulatory readiness — Digital Data Flow addresses every friction point in modern clinical trials.
Eliminates manual effort in linking data collection elements to analysis structures and output layouts. Intelligent mapping logic runs continuously, keeping everything synchronized.
A persistent, verifiable linkage is maintained from every CRF question through SDTM and ADaM transformations to final TLF outputs — ensuring complete transparency and reuse across the lifecycle.
By replacing document-based handoffs with structured, interoperable data assets, the platform dramatically reduces rework, late-stage surprises, and the errors that come with manual data handling.
Built in alignment with CDISC, ICH, FDA, and EMA requirements. The framework is platform-independent, ensuring portability across sponsor systems and submission environments.
Creates the data infrastructure needed for automated study setup, dynamic readiness monitoring, and downstream reuse — enabling a truly data-centric operating model for sponsors.
As raw or interim data becomes available, the platform enables early population and preview of outputs — supporting validation of study design assumptions long before database lock.
The flow begins with finalized annotated CRFs and structured study metadata, read alongside mappings and analysis parameters to build a complete picture of the study architecture.
Structured shells are automatically suggested for all planned analyses — aligned to sponsor standards and pre-populated with layout, variable references, and derivation details ready for programming teams.
Once raw or interim data flows in, transformations are applied and outputs are previewed in real time — surfacing gaps, flagging issues, and confirming the planned data structure can support the SAP.
CORE CAPABILITIES
Four foundational capabilities that keep every layer of your study synchronized, auditable, and ready.
Shells are generated automatically from annotated CRFs and associated specifications — removing the time-consuming manual drafting process and ensuring consistency from the start.
Links collection fields to analysis structures and output layouts through smart, rule-based mapping that accounts for sponsor standards and CDISC conventions.
Shells and previews stay synchronized with every change in CRFs, specifications, or data. No more version drift — what you see is always current.
Delivers complete, structured specifications to programming and validation teams — including layout, variable references, and derivation details — ready to use without interpretation.
Digital Data Flow delivers tangible value to every function involved in study execution and reporting.
EFFICIENCY GAINS
Less time spent drafting and maintaining shells. Earlier confirmation that the planned data structure can actually support the SAP — before issues become expensive.
EARLIER ALIGNMENT
Visibility of key displays much earlier in the process. Improved alignment between endpoints, narratives, and final reporting needs — reducing late-stage rewrites.
TIGHTER FEEDBACK LOOP
A direct feedback loop from expected outputs back into CRF and edit check design. Catch issues at the source, not at database lock — avoiding rework and late surprises.
Digital Data Flow is built in alignment with the industry's transition toward Digital Protocols and Digital Study Execution — supporting the shift from document-based processes to structured, interoperable data assets.
Automated QC for documents, data, and formats that ensures accuracy throughout the trial lifecycle.
Cut QC turnaround from days to hours for complex study reports.
Reduce rework from late‑identified inconsistencies.
Standardize QC across writers, vendors and studies.
The platform runs a configurable set of automated QC rules and lets you add your own checks so they match internal templates, style guides, and SOPs. It compares your clinical documents against key source materials, including protocol, SAP, TFLs, relevant SDTM/ADaM datasets, and safety narratives or summary sections. The engine works with standard authoring formats such as Word and PDF, avoiding disruption to existing workflows.
Configure rule sets using shared + org-specific checks
Upoad CSR + protocol, SAP, TFLs, datasets
Run QC instantly or schedule for automation
Dashboard highlights failure with guidance
OUR APPROACH
Most protocol optimization tools are powerful but complex. Teams invest in the platform yet struggle to extract actionable recommendations. Our integrated design partnership pairs your clinical team with seasoned SMEs who configure, run, and interpret simulations on your behalf — turning raw output into clear, decision-ready protocol choices.
WHAT'S INCLUDED
Three core service pillars work in concert — connecting clinical strategy, simulation depth, and decision-ready synthesis to deliver protocols that are right the first time.
Experienced SMEs remain engaged from early concept through near-final protocol, iterating on arms, endpoints, eligibility criteria, and visit schedules directly within the platform alongside your team.
The team configures realistic design scenarios using historical trial data, feasibility intelligence, and real-world enrollment patterns to estimate screen-fail rates, site workload, and likely amendment triggers.
Rather than delivering raw dashboards, our SMEs interpret results, frame the trade-offs clearly, and propose concrete design options aligned with your scientific objectives.
SME-Led Model
Our structured engagement model ensures every simulation is grounded in your clinical reality — and every output becomes a usable recommendation rather than a data artifact.
Step 01
Consultants meet your clinical team to capture the asset, indication, prior data, constraints, and success criteria.
Step 02
The current or draft protocol is encoded in the platform and alternative designs are created.
Step 03
SMEs run simulations across multiple scenarios to quantify patient burden and operational complexity.
Step 04
Results become clear design scorecards and scenario comparisons.
Step 05
Working sessions refine the preferred scenario into a scientifically sound protocol.
DESIGN QUESTIONS
Every engagement is framed around the protocol decisions that matter most — from eligibility criteria to visit burden — giving your team the clarity needed to design with confidence.
How will specific inclusion and exclusion criteria influence eligible population size, patient diversity, and overall time to enroll?
What visit schedules and assessment plans give us the scientific information we need without overburdening patients and investigator sites?
Where are we most at risk of unplanned amendments, and which specific design changes can prevent them before the first site is activated?
Which country and site mix gives us the most predictable and diverse enrollment pathway given current feasibility intelligence?
How do alternative endpoint strategies compare in terms of screen-fail rate, patient retention, and overall data quality at the site level?
What are the real operational cost implications of competing design scenarios, and where are the highest-leverage simplification opportunities?
BENEFITS
Every engagement is designed to generate measurable value — from fewer amendments to more confident enrollment projections and better outcomes for patients and sites alike.
Protocols grounded in realistic simulation data dramatically reduce unplanned amendments and the cascade of delays, rework, and costs they trigger — freeing your team to focus on execution from day one.
More predictable enrollment and trial execution emerge from designs tested against actual site behavior and historical performance — not optimistic assumptions that erode once sites are activated.
Consciously managing patient and site burden — through smarter visit schedules, streamlined assessments, and realistic eligibility sets — improves retention, diversity, and site engagement throughout the trial.
SMEs act as embedded design partners, ensuring your team extracts full value from the platform rather than leaving complex capabilities underutilized. The tool works harder because the right experts are driving it.
