Enterprise AI for Pharmaceutical Companies | Dyyota

Enterprise AI for Pharmaceutical Companies

Pharma companies generate massive volumes of clinical, regulatory, and safety documentation at every stage of the drug lifecycle. Most of this work is still done manually by highly trained people doing repetitive tasks. We build AI systems that handle the document-intensive work so your scientists and regulatory teams can focus on decisions that require expertise.

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55%

faster clinical document preparation

70%

reduction in literature screening time

4–6 wks

from kickoff to validated pilot

What We See in Enterprise AI for Pharmaceutical Companies

Clinical study reports take 8 to 14 weeks to compile because medical writers manually extract data from multiple trial databases and source documents

Regulatory submission teams spend thousands of hours formatting, cross-referencing, and quality-checking documents for FDA, EMA, and other agency filings

Pharmacovigilance teams manually review adverse event reports, medical literature, and social media signals, often falling behind on the volume of incoming safety data

Literature reviews for regulatory filings and drug development decisions require analysts to screen hundreds of papers per week, with most time spent on initial relevance filtering

How We Help

Clinical Trial Document Automation

Our AI extracts data from trial databases, electronic data capture systems, and statistical outputs, then generates first drafts of clinical study reports, protocol summaries, and investigator brochures. Medical writers review and refine instead of starting from scratch.

✓ Clinical study report preparation time reduced by 50 to 60%, freeing medical writing teams to focus on interpretation and quality

Regulatory Submission Assembly

We build systems that pull documents from your content management system, check them against agency formatting requirements (eCTD, NeeS), flag cross-reference inconsistencies, and assemble submission-ready packages. Regulatory teams review a pre-assembled, validated package.

✓ Submission assembly time cut by 40%, with a measurable reduction in agency queries caused by formatting and cross-reference errors

Pharmacovigilance Signal Detection

AI monitors incoming adverse event reports, medical literature, and other safety data sources to identify potential signals faster than periodic manual reviews. Each flagged signal includes case summaries, trend data, and source links for your safety team to evaluate.

✓ Safety signals detected 3 to 5x faster than manual periodic review cycles, with 30% fewer missed signals

Automated Literature Review

We deploy AI that screens published papers, conference abstracts, and preprints against your search criteria, classifies them by relevance and study type, and generates structured summaries. Analysts review pre-filtered, summarized results instead of reading every abstract.

✓ Literature screening time reduced by 70%, with analysts spending their time on relevant papers instead of triage

Drug Safety Case Processing

AI reads incoming adverse event reports from multiple channels (MedWatch, call centers, partner feeds), extracts case details, codes events using MedDRA, and populates your safety database. Safety officers review pre-processed cases and handle escalations.

✓ Individual case processing time drops from 45 minutes to under 10 minutes, with coding accuracy above 90%

Our Services for This Industry

Multimodal RAG SystemsView →

AI Agent DevelopmentView →

Agentic AutomationView →

Frequently Asked Questions

How do you handle GxP validation requirements for AI systems?+

We build with validation in mind from day one. Our systems include audit trails, version control, and documented testing protocols that align with GAMP 5 guidelines. We work with your quality team to produce the validation documentation your QA process requires, including IQ/OQ/PQ protocols.

Can your AI work with our existing clinical data systems like Veeva or IQVIA?+

Yes. We integrate with Veeva Vault, IQVIA platforms, Medidata, Oracle Argus, and other standard pharma systems via APIs and validated data connectors. We also work with custom in-house databases. Integration architecture is defined during the scoping phase.

How do you ensure the AI does not hallucinate in regulatory or safety contexts?+

We use retrieval-augmented generation that grounds every output in your source documents and data. The system cites its sources for every claim. For safety-critical outputs, we add confidence scoring and mandatory human review checkpoints. Nothing goes to a regulator or a safety database without human sign-off.

What is the typical timeline for a pharma AI deployment?+

A single-use-case pilot, such as literature screening or case processing, takes 4 to 6 weeks including validation activities. Broader deployments across multiple use cases typically run 4 to 8 months, depending on validation requirements and system integration complexity.

Let's build your AI system.

Production-grade AI for Enterprise AI for Pharmaceutical Companies. We deploy in weeks, not quarters.

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