Medical Writing
QC Automation

for Pharma Biotech

With the increase in work load, regulatory expectations and submission timelines getting shorter, quality control for documents has become more complex and resource-intensive. Traditional manual QC processes are no longer sufficient to handle growing document volumes, evolving global regulations, and increasing scrutiny from health authorities.

Schedule Demo

0%

Reduction in QC Time

0%

Consistency Check

0

Formatting Errors

21

CFR Part 11 Compliant

The Challenge: Limitations of Manual QC

At DDi, we understand that QC issues will lead to manual reworks, quality findings, may be submission delays, and additional costs. Even minor misses by QC can trigger issues.

Time-Consuming

Reviewing lengthy documents line by line ( or sometimes side-by-side) slows down submission timelines and increases pressure on medical writers.

Human Error

Fatigue, repetitive checks, and tight deadlines can lead to missed inconsistencies or overlooked formatting issues.

Lack of Standardization

Different reviewers may apply QC rules inconsistently, leading to variable quality across documents and projects.

Scalability Issues

As organizations expand globally, maintaining consistent QC manually becomes increasingly complex.

Our Solution:
Intelligent QC Automation (both AI and non-AI options)

Automation introduces rule-based checks and AI capabilities that systematically validate documents against predefined standards or your rule books By automating repetitive checks, our platform allows authors and QC reviewers to focus on higher-value work rather than mechanical tasks.

Unlike static systems, our AI models continuously improve by learning
from historical documents, agency feedback, and organizational preferences. This enables the detection of logical gaps, terminology inconsistencies, and conflicting data, ensuring strict alignment with regulatory best practices.

Content QC

Automation & AI helps identify inconsistencies in terminology usage, in-text table differences compared to source, detects logical gaps, flags conflicting data across sections (and across source documents/data), and highlights deviations from regulatory writing best practices.
AI models also supports cross-document QC by comparing multiple documents within a submission to ensure alignment across protocols, clinical study reports, investigator brochures, and summaries.
AI also supports cross-document QC by comparing multiple documents within a submission to ensure alignment across protocols, clinical study reports, investigator brochures, and summaries.

Terminology Consistency

Logical Gap Detection

Cross-Doc Alignment

Adaptive Learning