How Big Pharmas cut GLP-1 admin costs with AI-trusted document & data automation

Quick answer

Pharmas lower GLP-1 admin costs by pre-processing unstructured content (scans, emails, CAD, lab reports) into searchable, structured, submission-ready assets, then applying LLM-orchestrated extraction and validation to reduce human review. This improves AI accuracy and trust, compresses cycle times for eCTD/CMC/BMR, and cuts rework and rejection risk, crucial as GLP-1 demand grew ~38% annually (2022–2024) and compounding “workarounds” wound down in 2025.

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Why “AI trust” is the real unlock for GLP-1 scale

Most data is unstructured.
Roughly 80% of enterprise data sits in unstructured formats that AI and downstream systems can’t reliably use without prep. 54% is stale, injecting risk and noise into models and submissions. ‍

Regulatory rigor is unforgiving.
eCTD is the standard format for FDA submissions; Modules 2–5 are harmonized across regions via ICH CTD. If formatting, completeness, or fidelity slips, approvals slow. ‍

The GLP-1 moment magnifies errors.
As shortages abate, FDA’s time-boxed enforcement discretion for compounded semaglutide/tirzepatide ended in spring 2025, pushing patients and payers back to branded pathways and putting a premium on compliant, high-throughput operations. ‍

Bottom line: AI is only as trustworthy as its inputs. Pharmas that standardize and validate content before it hits an LLM see higher accuracy, fewer rejections, and lower admin drag.

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Where trust breaks in the GLP-1 lifecycle

Clinical trials (TMF/CTMS): consent forms, investigator brochures, lab outputs, site reports, often scanned or emailed, arrive in inconsistent formats that resist automation.

eCTD compilation: region-specific requirements, changing notices, and version sprawl increase rework risk and submission latency.

CMC documentation: proofs of process/quality/stability are dense and technical; if text, tables, or images aren’t captured pixel-perfect, QA must repeat checks.

BMR management: batch data from multiple systems must reconcile exactly; any OCR errors or metadata drift undermine traceability.

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The automation blueprint that improves accuracy and cuts cost

1) Intake & normalization

• Convert 300+ file types (including legacy/CAD) into searchable, pixel-perfect universal formats; apply multi-engine OCR with de-skew/despeckle and watermark/highlight cleanup so the text the LLM sees is pristine.  

“We provide the LLM the best, cleanest text possible, not a messy PDF or image.” — Anthony Vigliotti, CPO.

2) LLM-orchestrated extraction (with validation)

• Orchestrate your approved LLMs (OpenAI, Gemini, Claude, on-prem/private) via AiLink to extract fields (e.g., endpoints, subject IDs, CMC attributes) into JSON/XML. Build confidence-based rules that trigger selective human-in-the-loop (HiTL) only when needed.  
• Maintain a single pane of glass for auditability and traceability across steps.

3) Compliance formatting & assembly

• Auto-apply eCTD/EMA technical criteria (TOC, bookmarks, headers/footers, split/merge, link checks), ensure PDF/A conformance, and validate against business and regulatory rules before submission.  

4) System hand-offs & RAG-readiness

• Snap outputs into TMF, CTMS, RIM, QMS, and data stores; generate vector embeddings from cleaned, chunked documents to power RAG with less hallucination.  

What “good” looks like (GLP-1 use cases):

• Clinical trials: faster, structured intake; automated ID extraction across file types/media; audit-ready TMF.
• eCTD: compilation time down 50%+ with automated validations.
• CMC: automated extraction of critical parameters; earlier detection of quality issues.
• BMR: OCR + technical validation + smart assembly; real-time updates to eTMF/RIM/QMS.

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Why leaders standardize on Adlib for accuracy and trust

Proven in life sciences
‍7 out of 10 of top pharmas rely on Adlib for regulatory documentation; many report millions saved by eliminating manual processing.  

Fidelity at scale
‍Pixel-perfect rendering and multi-engine OCR preserve every detail regulators expect, critical for GLP-1 submissions.

Any file, any LLM, any platform
‍300+ file types, BYO-LLM interoperability, containerized scale, and deep connectors across ECM/PLM/CTMS/RIM/QMS.  

Security & sovereignty
‍Typically deployed on-prem or private cloud in air-gapped environments; role-based access and detailed audit trails.  

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Measurable business impact for GLP-1 programs

Faster submissions
‍50%+ reduction in regulatory compilation time via automated validation and assembly.

Lower admin costs
‍Intelligent routing and exception handling reduce HiTL to only edge cases; one CRO saved 8,500+ hours/year standardizing trial docs.  

Higher AI accuracy & trust
‍Clean, structured inputs and validation pipelines reduce hallucinations and rework, powering reliable analytics, RAG, and downstream automations.

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FAQ

What’s the fastest way to lower GLP-1 admin costs?
Start by standardizing every document (300+ formats) into searchable, compliant outputs, then apply LLM-orchestrated extraction with validation so humans review only flagged exceptions.  

How does this improve AI accuracy and trust?
Pre-processing (multi-layer OCR, object separation, chunking) creates clean, context-rich inputs that reduce hallucinations and token waste, while rules-based validation and audit trails enforce reliability.  

Will it fit our existing GLP-1 stack (Veeva, MasterControl, etc.)?
Yes, Adlib snaps into TMF/CTMS/RIM/QMS and can be embedded by partners like MasterControl or orchestrated via APIs.  

Do we have to change our LLM?
No. Use your approved model(s), cloud or private. AiLink routes data to OpenAI, Gemini, Claude, or on-prem models, returning results in JSON/XML.

Is this compliant with eCTD/ICH expectations?
Automation enforces formatting, completeness, and technical criteria, integrates with eCTD workflows, and logs every action for audits, aligned to FDA/ICH guidance.

What to do next

1. Pick one GLP-1 dossier (e.g., a line extension) and baseline your submission prep time and first-pass accept rate.
2. Instrument automated intake → extraction → validation → assembly → hand-off.
3. Measure: submission cycle time, HiTL minutes per document, and resubmission/rework rate.

Want to see a GLP-1 dossier flow go from 3 weeks to days with audit-ready, AI-trusted outputs?

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