Your TMF AI
is extracting
the wrong data.
Your eTMF system works. Your AI layer runs. Your team still corrects every record before filing. That is not an automation problem. It is an accuracy problem.
What your team
corrects every day
In top CROs and pharma organisations worldwide, the same four corrections happen before every eTMF upload. Every cycle. Without exception.
Wrong expiry date
Issue date extracted instead of expiry. Creates compliance risk on every GCP certificate filed.
Incorrect personnel name
Organisation name or role title extracted instead of the individual. Breaks traceability at inspection.
Misclassified document type
Wrong TMF category assigned. Makes retrieval unreliable when an inspector asks for it.
Manual validation before every upload
Your team reviews AI output before filing because trusting it directly is not an option.
It is assisted.
What gets wrong.
What we get right.
Same document. Same certificate. Two very different outputs.
| Field | Existing AI Output | Structured Extraction Output |
|---|---|---|
| Document Type | Unknown / Unclassified | GCP Certificate99% |
| Personnel Name | Site Coordinator | Dr. Sarah Mitchell98% |
| Issue Date | 20 Mar 2026 | 20 Mar 202497% |
| Expiry Date | Not found | 20 Mar 202697% |
| Role | Not found | Principal Investigator96% |
| Issuing Body | Not found | TransCelerate95% |
Four reasons we
built differently.
Generic AI was not designed for clinical document context. These four structural gaps are what DocLoom was built to close.
Multiple entities, one document
GCP certificates contain the individual, their organisation, the issuing body, and the training provider. Generic extraction pulls the wrong entity every time because it has no clinical document context.
Issue date and expiry date confusion
Two dates in close proximity with no structural distinction. Pattern-based extraction defaults to the first date found. Issue date and expiry date are filed as the same value.
No standard certificate format
Certificates from TransCelerate, CITI, local institutions, and in-house training programmes each have a different layout. A model trained on one format fails on all the others.
Single-engine OCR has no fallback
Handwritten annotations, low-resolution scans, and varied fonts compound extraction errors. A single OCR engine with no confidence fallback passes bad data through silently.
Generic LLMs were not built
for clinical document extraction.
The question comes up every time. Here is the honest answer on why a general-purpose LLM does not solve the TMF accuracy problem, and what it would take to actually fix it.
LLMs hallucinate on structured clinical data
A general LLM will produce a plausible-looking output even when it cannot find the field. It invents a value rather than flagging uncertainty. In a regulated TMF, a fabricated expiry date is worse than a missing one. You need confidence scoring, not confident-sounding output.
No clinical document ontology
GCP certificates have a specific structure, vocabulary, and field hierarchy that varies by issuing organisation. TransCelerate, CITI, local institutional certificates, and in-house programmes each present data differently. A general LLM has no model of this. It reads the page. It does not understand the document.
No audit trail by design
LLM outputs are not traceable to a specific extraction decision. You cannot tell an inspector which logic produced a particular field value or why a date was chosen over another. A structured extraction layer produces a per-field decision record with confidence score and source reference. That is what inspection-readiness requires.
Single-pass extraction misses multi-entity documents
GCP certificates contain the individual, their employer, the training provider, and the issuing body , sometimes in the same sentence. A single-pass LLM extracts the first entity it recognises. Structured extraction uses a multi-step validation pipeline that resolves entity type before field assignment.
Cannot handle poor OCR source reliably
Most TMF documents arrive as scanned PDFs with varying quality. General LLMs process the OCR output they are given. If the OCR engine misreads a character, the LLM inherits that error. Multi-model OCR with fallback routing catches what single-engine OCR misses before the LLM even sees the text.
No exception routing built in
A general LLM has no mechanism to route low-confidence fields to a human reviewer and pass high-confidence fields straight through. That exception-only review model is the operational outcome that reduces your team's workload. It requires a confidence layer that LLMs do not natively provide.
We know your documents.
Sits before your
eTMF system.
No system replacement. No workflow disruption. The accuracy layer processes documents before they reach your TMF platform and pushes structured, confidence-scored metadata directly into it.
Received
Layer
System
Test it on your
own documents.
Share 5 to 10 GCP certificates or study team documents. We run structured extraction and show you exactly where your current model breaks down field by field.
Sign up free. 200 credits included. No system changes required.