Clinical documents are not AI-ready by default | Adlib @ BIO 2026

The problem with AI in pharma & biotech

AI hallucinations on clinical data are not a model problem. They are a data quality problem.

Before your LLM can be trusted, your documents have to be. Most aren't.

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MIT research found that when AI models hallucinate, they use more confident language than when providing accurate information: 34% more likely to say "definitely," "certainly," or "without doubt." In regulated clinical environments, confident-sounding fabrications are the most dangerous kind.

The root cause is upstream, not in the model: garbage-in, garbage-out. When clinical documents are unstructured, non-searchable, or inconsistently formatted, LLMs fill gaps with plausible-sounding fabrications. Structured, validated document inputs reduce hallucination rates by up to 33% with prompt-based mitigation alone.

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How Adlib solves this

The document accuracy layer for life sciences AI

Adlib sits upstream of your core systems, LLMs, and RAG pipelines, transforming messy, multi-format clinical content into structured, validated, audit-ready data before AI ever touches it.

1. Multi-format normalization

Ingests 300+ document types (PDFs, scanned CRFs, CAD, emails, lab reports) and normalizes them into consistent, machine-readable structure with fidelity-preserving rendering and advanced OCR.

2. Multi-LLM comparison & voting (Adlib Accuracy Score)

Runs extractions across multiple LLMs simultaneously, cross-checks results via confidence scoring and voting algorithms, and routes low-confidence documents to human review before they enter downstream systems.

3. Validation against compliance rules

Checks documents against your organization's regulatory rules, flagging exceptions before they reach submissions. Every step is logged for SOC 2, HIPAA, FDA 21 CFR Part 11, and GxP audit trails.

4. PrecisionPath for life sciences

Pre-built accuracy pipelines for clinical operations, regulatory submissions, batch release, and RWE — packaged with extraction models, prompts, and integration starters for eTMF, Veeva, RIM, QMS, and MES systems.

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Value by business area

Where Adlib delivers for CROs and sponsors

Select a business area to see specific challenges, outcomes, and proof points.

‍Ensure clinical data integrity from trial initiation to eTMF closeout

CROs and sponsors handle thousands of CRFs, ICFs, monitoring reports, IRB approvals, and GCP documents per trial. When these enter AI workflows in unstructured form, hallucination risk and compliance exposure multiply. Adlib auto-ingests, standardizes, and validates trial documents at scale, ensuring audit-readiness without expanding headcount.

✓Auto-ingestion and validation of CRFs, ICFs, site reports, and TMF documents

✓Structured extraction of subject IDs, adverse events, and critical endpoints for downstream AI

✓Real-time eTMF integration, documents processed in 15 seconds vs. hours manually

✓8,500+ work hours saved annually at a leading CRO supporting 85% of FDA-approved drugs

Eliminate FDA/EMA rejections with compliant, submission-ready document assembly

Regulatory submissions fail when source documents (NDAs, BLAs, INDs, CSRs) have format inconsistencies, broken structure, or missing validation. Adlib auto-assembles submission packages with PDF/A compliance, bookmarks, ToC, and field validation baked in, so your content arrives defensible on first submission.

✓Automated smart assembly and validation of NDA, BLA, and IND packages

✓PDF/A rendering, bookmarks, citation anchors, and format validation before submission

✓$1.7M annual savings at one CRO from reduced rejections and rework elimination

✓Integration with Veeva, RIM, and eCTD publishing tools, no rip-and-replace required

Accelerate batch record processing with AI-ready, compliant EBRs

Batch Manufacturing Records and Electronic Batch Records are among the most document-intensive and audit-sensitive workflows in pharma. Manual processing creates audit exposure, delayed batch release, and rising overhead. Adlib automates formatting, metadata validation, and distribution of batch documentation while feeding structured data into QMS, MES, and ERP systems.

✓Automated EBR assembly, validation, and compliant PDF generation for batch delivery

✓Structured extraction of batch data into QMS, MES, and ERP for real-time updates

✓ICH and GMP formatting compliance enforced automatically, no manual rework

✓Instantly searchable batch records for faster inspection response and audit readiness

Make real-world evidence and adverse event data defensible for regulators

Real-world evidence programs and safety reporting depend on extracting accurate, traceable data from complex, multi-format sources, EHRs, claims, lab reports, adverse event narratives. When LLMs process unstructured documents without an accuracy layer, fabricated safety signals or missed adverse event details create serious regulatory and patient safety risk. Adlib structures and validates these inputs before AI ever acts on them.

✓Structured extraction of adverse event narratives, safety reports, and follow-up documents

✓LLM-based PII/PHI redaction before data leaves the document processing layer

✓Citation-anchored chunking ensures every AI-generated insight is traceable to source documents

✓Audit trails and chain-of-custody logging for every extracted value, defensible by design