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Enterprise AI for Pharmaceutical Companies

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 still gets done manually by highly trained people doing repetitive tasks. We build AI systems that handle the document-heavy work so your scientists and regulatory teams can focus on decisions that require expertise.

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Up to 55%
faster clinical document preparation
Up to 72%
reduction in literature screening time
7-12 wks
from kickoff to validated pilot

What We See in Enterprise AI for Pharmaceutical Companies

1

Clinical study reports take 10 to 16 weeks to compile because medical writers manually pull data from Veeva Vault, EDC systems like Medidata Rave, and SAS statistical outputs, then cross-reference every number against the SAP before a single draft section ships.

2

Regulatory submission teams using Veeva Vault RIM spend thousands of hours per filing formatting eCTD modules, cross-referencing hyperlinks, and quality-checking documents for FDA, EMA, and PMDA submissions, with the last 2 weeks before a deadline turning into a red-eye all-nighter cycle every time.

3

Medical-Legal-Regulatory (MLR) review of promotional materials in Veeva PromoMats is the biggest bottleneck in commercial launch readiness. Reviewers spend 3 to 5 hours per piece cross-referencing claims against the PI, study reports, and fair-balance rules, with each round adding days to the cycle.

4

Pharmacovigilance teams in Oracle Argus or ArisGlobal LifeSphere manually review adverse-event reports, medical literature, and social signals, and routinely fall behind on incoming volume during signal-rich periods, which is exactly when speed matters most to patients and to the regulator.

How We Help

Clinical Trial Document Automation

Our AI pulls data from Veeva Vault Clinical, Medidata Rave, and SAS statistical outputs to generate first drafts of clinical study reports, protocol summaries, investigator brochures, and DSMB reports. The agent follows your document templates, applies your phrasing conventions, and cites every number back to the source table. Medical writers refine interpretation sections and scientific narrative rather than re-typing tables and cross-references.

✓ 55% faster CSR preparation and medical writers focused on interpretation

Regulatory Submission Assembly

We build systems that pull documents from Veeva Vault RIM, check them against FDA, EMA, and PMDA eCTD requirements, flag cross-reference inconsistencies and hyperlink errors, and assemble validated submission-ready packages. Regulatory writers review a pre-assembled package with a QC report rather than compiling modules by hand in the last two weeks before a deadline.

✓ 42% faster submission assembly and 61% fewer agency queries on formatting

MLR Review Acceleration for Promotional Content

The agent reads incoming promotional pieces in Veeva PromoMats, maps every claim against the PI, cited study reports, and fair-balance requirements, and generates a pre-flagged review that MLR reviewers validate rather than dictate from scratch. Cycle time through MLR drops materially and launch readiness timelines compress. All decisions are logged against the source evidence for commercial compliance audit.

✓ 48% reduction in MLR review cycle time and 31% fewer review rounds per piece

Pharmacovigilance Signal Detection and Case Processing

AI monitors incoming AE reports from MedWatch, call centers, partner feeds, medical literature, and social channels. It extracts case details, codes events against MedDRA, populates Oracle Argus or ArisGlobal LifeSphere, and flags potential signals with trend data and linked cases. Safety officers review pre-processed cases and handle escalations rather than triaging raw inbound volume.

✓ Case processing from 45 min to under 10 min and signals detected 3 to 5x faster

Automated Literature Review

We deploy agents that screen published papers, conference abstracts, and preprints from PubMed, Embase, and other sources against your search criteria, classify them by relevance and study type, and generate structured summaries with key results, patient populations, and safety data extracted. Analysts review pre-filtered, summarized results with full citations rather than reading every abstract cold.

✓ 72% reduction in literature screening time with analysts working relevant papers only

Our Services for This Industry

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Engagement shape

Timeline

A typical pharma engagement runs seven to twelve weeks to validated production. Weeks one and two are discovery: SME interviews, QA and regulatory alignment, user requirements, and a written integration pattern against Veeva Vault, Medidata, Argus, or your specific systems. We build a validation eval set in week two from 500 to 2,000 of your own documents, cases, or pieces with SMEs setting the ground truth.

Weeks three through six are build with validation artifacts produced in parallel (functional specs, traceability matrices, risk assessment). Weeks seven and eight cover shadow execution against the eval set and IQ, OQ, PQ protocol execution. Weeks nine through eleven are formal validation sign-off from QA, user acceptance testing with business SMEs, and SOP updates. Week twelve is validated production cutover with a controlled ramp and hypercare for 30 days. All changes post-go-live move through your formal change-control process.

Cost model

Most pharma engagements fall between $130k and $320k for the first validated production use case. The main drivers are validation scope, Veeva or Argus integration depth, therapeutic area coverage, and whether global health authorities (FDA, EMA, PMDA) are each in scope with their own documentation. A single-therapeutic-area literature screening pilot sits near the bottom of the range. A multi-region validated MLR or regulatory submission agent with full GAMP 5 documentation lands at the top. Ongoing validated platform and inference costs typically run $10k to $35k per month.

Frequently Asked Questions

How do you handle GxP validation and 21 CFR Part 11 requirements?+
Validation is in the build plan from kickoff. Our systems include complete audit trails, version control, access logging, and electronic-signature support aligned with 21 CFR Part 11. The build follows GAMP 5 guidance with documented user requirements, functional specs, and traceability. We deliver IQ, OQ, and PQ protocols your QA group can execute, plus risk assessments tied to specific intended uses. For GCP and GVP systems we handle the computerized systems validation package end-to-end. Your QA owner reviews and approves each validation artifact before production use, and we maintain the validated state through change control thereafter.
Can your AI integrate with Veeva Vault, Medidata, IQVIA, and Oracle Argus?+
Yes. We've built against Veeva Vault Clinical, Vault RIM, Vault Quality, and Vault PromoMats through the Vault API. For clinical data we integrate with Medidata Rave, Oracle InForm, Veeva EDC, and IQVIA platforms through their respective APIs and SDTM data feeds. For pharmacovigilance we integrate with Oracle Argus and ArisGlobal LifeSphere through their E2B interfaces and APIs. For custom in-house databases we use REST or file-based transfer. Integration architecture is defined during discovery and documented in validation artifacts, and write-back is gated by your change-control process.
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 specific source pages or database records for every claim it makes. For safety and regulatory outputs, we add confidence scoring, mandatory human review checkpoints, and a second-pass validation model that checks for claims not supported by the cited source. Nothing goes to a regulator, a safety database, or a promotional audience without human sign-off. For particularly sensitive outputs (AE causality assessments, MLR approvals) we build explicit dual-review workflows rather than single-signature sign-offs.
What does a pilot cost and how long does it take?+
A focused validated pilot on one use case (literature screening, MLR acceleration for one therapeutic area, or PV case processing) runs 7 to 10 weeks from kickoff to validated production. Pricing typically lands between $130k and $290k depending on validation scope, Veeva or Argus integration depth, and therapeutic area coverage. Broader rollouts across multiple use cases run 4 to 8 months. Validation work is a meaningful part of the cost, and we quote it explicitly in the SOW so QA and regulatory affairs see the breakdown rather than an opaque bundle.
What data stays on our infrastructure vs. with the AI vendor?+
Clinical data, patient-level data, case safety reports, and proprietary regulatory and commercial content stay inside your tenant. We deploy the application layer in your AWS, Azure, or GCP tenant under your existing qualified environment and run inference against models hosted in your own account with zero retention and zero training on your prompts. PHI and CSR content never transit a public AI API. For published literature searches against PubMed and Embase, the agent uses the ordinary publisher and NLM APIs under your existing subscriptions. Full egress map is handed to your IT and QA teams before validation execution.
Who's accountable when the AI gets a regulatory or safety output wrong?+
The accountable human is the same named role accountable today: the medical writer, the regulatory affairs lead, the MLR reviewer, the qualified person for pharmacovigilance. Our agents surface recommendations, pre-draft content, and flag signals, but the final sign-off remains human. For PV case narratives, a trained safety physician or scientist reviews and signs. For MLR, the medical, legal, and regulatory reviewers each sign off. For regulatory submissions, the named regulatory affairs lead submits. We deliberately avoid autonomous decisioning in any workflow where a patient, a regulator, or a prescriber depends on the output. Liability allocation lives in the MSA and we carry life-sciences appropriate E&O coverage.
How is this different from Veeva AI, a big consulting firm like Deloitte or Accenture, or an in-house data science build, and how do we measure ROI?+
Veeva ships platform-integrated features. We tune agents to your specific therapeutic areas, document templates, SOPs, MLR rules, and prior regulatory correspondence, and we deliver validated code that sits inside your Vault and Argus workflows rather than alongside them. Big consulting firms deliver a multi-year digital roadmap and staff augmentation. We deliver running validated systems in weeks. Internal data science teams often have the skills but not the validation-delivery muscle for a GxP environment. ROI is measured against a baseline captured in discovery: CSR cycle time, submission assembly hours, MLR cycle time, PV case processing time, literature screening throughput. Most pharma deployments pay back within 12 to 15 months on loaded staff cost.
What's the hand-off between AI and our people, and how do we validate accuracy pre-production?+
Every workflow has an explicit hand-off. Drafts go to a qualified reviewer. Pre-production, we build a validation eval set from your own documents, cases, or literature with SMEs setting the ground truth, and we agree accuracy targets in writing before build: typically 95%+ on structured extraction, SME-rated quality scores on narrative, and specific precision-recall for signal detection. The system does not enter validated production until it hits those thresholds on your data. Post-go-live we run continuous accuracy monitoring with monthly reports QA reviews, and we handle model updates through formal change control rather than silent upgrades.

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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