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 are not marketing badges — they are 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
  • 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.