Case Studies

See what walking in already ready could look like for your study

NeogeniQS is pre-launch, so instead of inflated claims we show illustrative target scenarios grounded in the actual work each agent automates. As design partners approve public results, named case studies will replace them here.

A note on the numbers

The scenarios below are illustrative and pre-launch. Every metric is a target based on internal benchmarks and the workflow economics of the tasks each agent automates, not a guaranteed or historical outcome. Results will vary by organization, therapeutic area, and data quality. We would rather show you an honest target than an unverifiable claim.

0
Feature agents across the trial lifecycle
0%
Target reduction in manual spec time· target
0
TMF artifacts across 11 zones
0+
Informed-consent languages· target

Illustrative scenarios

What an engagement could look like

Four representative buyers, the workflows they struggle with today, and the agent-assisted approach NeogeniQS is built to deliver.

Pharmaceutical Sponsor

Compressing protocol-to-database build for a Phase II study

The challenge. A mid-size sponsor loses weeks translating a finalized protocol into a CDASH-conformant eCRF, an annotated CRF, and an SDTM mapping, tying up scarce data managers before the study can even open.

The approach. Protocol Intelligence digitizes the protocol; CRF Designer generates the eCRF and edit checks; SDTM Annotation produces the annotated CRF and mapping in the same pass, each reviewed and approved by the sponsor's data-management lead.

Protocol IntelligenceCRF DesignerSDTM AnnotationeTMF Workflow
70%
Less manual spec-creation time
Weeks→days
Protocol to database build
CDASHIG v2.1
Conformant by design
Contract Research Organization

Standardizing data cleaning and reporting across many sponsors

The challenge. A CRO runs studies for a dozen sponsors, each with slightly different standards. Manual query management and status reporting erode margin and make quality uneven across teams.

The approach. Query Automation drafts context-aware, PHI-safe queries for review; SDTM/ADaM and TLF agents produce submission-ready outputs; the Status Dashboard keeps every sponsor's study health current in real time.

Query AutomationSDTM/ADaM DatasetsTLF GenerationStatus Dashboard & Reporting
40%
Faster query resolution vs manual
P21-clean
Datasets pass with no errors
Real-time
Sponsor-facing study status
Research Institution & Site

Sponsor-grade rigor with a lean coordinator team

The challenge. An academic medical center runs several concurrent studies with a small team. Consent readability, multi-language versions, and source-document review consume coordinator time, and inspection prep is a recurring fire drill.

The approach. Informed Consent generates plain-language, IRB/EC-ready ICFs; ICF Translation delivers verified multi-language versions; Source Document Review and AE detection surface gaps and safety findings; the eTMF stays inspection-ready continuously.

Informed ConsentICF TranslationSource Document RevieweTMF Workflow
8th-grade
Consent readability by default
30+
Consent languages
Always
Inspection-ready TMF
Emerging Biotech

Running a credible first trial without over-hiring

The challenge. A pre-revenue biotech needs to run its first study credibly but has no in-house data management, biostatistics, or medical writing, and limited runway for vendor spend.

The approach. The platform provides the full stack out of the box: Protocol Intelligence and CRF Designer for build, SDTM/ADaM for datasets, Medical Writing for an ICH E3 CSR draft, and an eTMF on the TMF Reference Model, with qualified reviewers approving every output.

Protocol IntelligenceCRF DesignerSDTM/ADaM DatasetsClinical Study Report (CSR)
Full stack
Build → data → stats → eTMF
70%
Less manual effort per deliverable
Day one
Submission-quality outputs

Figures shown are pre-launch targets based on internal benchmarks, not guaranteed outcomes. Scenarios are illustrative composites and do not describe specific named customers.

Design-partner testimonials will appear here as our first engagements complete and customers approve their results for release.
Placeholder: reserved for a verified early design partner.

How we prove it

From baseline to a case study we can both stand behind

1

Define the baseline

We agree on the workflows in scope and the metrics that matter (study-build time, query cycle time, TMF completeness, Pinnacle 21 findings) and capture where you are today.

2

Deploy agents with human review

We enable the relevant agents on one program, connect your standards and dictionaries, and run every AI output through your team's review-and-approval gates.

3

Measure and expand

We track results against the baseline with a full audit trail, then expand agent-by-agent. Approved outcomes become a named, verifiable case study.

Every result is produced inside a validated, audit-ready platform

The standards below aren't marketing badges. They're the controls that make AI-assisted outputs acceptable to inspectors and trustworthy in a real case study.

  • 21 CFR Part 11
  • ICH E6(R3)
  • CDISC (CDASH · SDTM · ADaM)
  • HIPAA
  • HITECH
  • GDPR
  • SOC 2
  • ISO 27001
  • ISO 42001

Frequently asked questions

Curious what these targets would mean for your study?

Whenever you're ready, we'll walk you through the agents and the benchmarks in detail (honest answers, including on the limits) or talk about joining the design-partner program.