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NewDocument Intelligence builds AI pipelines that read, extract and check your regulated documents — adverse event reports, safety literature, trial master files, protocols and SOPs — with every output routed to a human for review before it counts.
Reading, extracting and re-typing is where document-heavy teams lose their week. The pipelines do that part; your qualified staff verify and approve — which is the part that actually needs them.
Every pipeline is piloted against your own ground truth — accuracy, throughput and turnaround measured before you commit.
When AI does the reading and structuring, each qualified person processes multiples of their manual volume, at higher consistency. Example: a literature reviewer covers several times the citations by assessing an AI-flagged shortlist instead of screening everything.
Case intake, screening triage and TMF checks compress from hours to minutes — and regulatory reporting clocks gain their margin back. Backlogs stop being a standing agenda item.
Case processing consumes 60–70% of a typical PV budget, with volumes growing ~10–15% a year. Pipelines absorb that growth without matching headcount or outsourcing spikes — and one avoided late-report finding can exceed the cost of the pipeline.
Figures are indicative of typical industry workloads. Your pilot establishes your actual baseline and measured return before any rollout decision.
Each pipeline is scoped to one document type and one decision — so it can be measured, validated and trusted.
Extracts patient, reporter, drug and event details from emails, call transcripts and forms; suggests MedDRA coding and seriousness; detects duplicates — then queues the structured case for your processor’s review.
Why it matters: case processing consumes 60–70% of most PV budgets.
Screens journal articles and abstracts for adverse event mentions of your products, extracts the relevant details and prioritises ICSR candidates — replacing manual screening of thousands of citations a week.
Why it matters: literature monitoring is a regulatory obligation under EU GVP and FDA rules.
Continuously audits your trial master file for missing, misfiled, unsigned or expired documents against your TMF reference model — so findings surface now, not during inspection prep.
Why it matters: TMF completeness is one of the most common inspection findings.
Checks protocols and SOPs for internal inconsistencies, conflicting requirements between documents, and the impact of regulatory changes on your procedure set.
Why it matters: a single protocol amendment can cost a sponsor hundreds of thousands.
Converts paper and scanned PDF archives — batch records, correspondence, historical study files — into structured, searchable data with OCR, classification and metadata extraction.
Why it matters: retention obligations run for decades; retrieval shouldn’t take days.
Medical information letters, contracts, batch records, submission dossiers — if your team spends hours reading a document type, we can likely build a pipeline for it. Bring us the bottleneck.
Start here: a scoping call to identify your highest-volume document pain.
The features quality and PV teams tell us they can't work without.
Reads native PDFs, scans, faxes and photographed forms — including handwritten entries, flagged with lower confidence.
Every extracted field carries a confidence score. Anything below your threshold is automatically queued for human review.
Each data point links back to the highlighted passage it came from, so reviewers verify in one click — and auditors can too.
Identifies when the same case, article or record arrives twice through different channels, before it creates rework downstream.
Processes ten documents or ten thousand with the same turnaround — backlogs and seasonal spikes stop being a staffing problem.
Approved data flows to your safety database (E2B), eTMF, QMS or data warehouse via API — no re-typing between systems.
Extracts from source documents in major European and Asian languages, with the original text preserved alongside.
Live visibility of throughput, accuracy, review queues and turnaround times — the numbers you’ll show your management and your inspectors.
Documents arrive from email, DMS, scans or uploads.
AI reads and structures the content, field by field.
Rules and confidence scoring flag anything uncertain.
Your qualified staff verify, correct and approve.
Human-in-the-loopApproved data flows to your safety database, TMF or QMS — fully logged.
Whitehall has taught GxP compliance for over two decades. That thinking is in every design decision.
Requirements, risk assessment, test evidence and traceability delivered with every pipeline.
No extracted data enters a GxP system without qualified review and approval. Low-confidence fields are always flagged.
Every extraction, correction and approval is attributable and time-stamped, with a link back to the source document.
Patient and commercial data processed in your approved region. No training on your data.
It depends on the document type, and we never ask you to take it on faith. Every pilot starts with a baseline measurement on your real documents against your team’s ground truth. Fields below your agreed confidence threshold are always flagged for human review — the system is designed so that accuracy limitations reduce speed, never data integrity.
No — it removes the reading and re-typing so they can spend their time on assessment and decisions. The review step is mandatory by design: your qualified staff remain the approvers of record for everything that enters a GxP system.
Emails, PDFs (native and scanned, via OCR), Word documents, images of forms, spreadsheets and structured exports. Handwritten content is supported with lower confidence and is routinely routed to human review.
Yes — pipelines deliver approved data to your existing systems (for example via E2B for safety databases, or API/import formats for eTMF and QMS platforms). Integration scope is agreed during the pilot.
Each pipeline ships with a GAMP 5 (Second Edition) validation package: user requirements, functional risk assessment, test scripts with your documents, a traceability matrix and a periodic review plan — plus support writing the SOP that governs its use.
You choose one document type and provide a representative sample. We build the pipeline, measure accuracy against your ground truth, and demonstrate the human review workflow — so you have real performance numbers before committing to a deployment.
In a 30-minute call we'll identify your highest-volume document bottleneck and scope a 4-week pilot on your real documents — with accuracy measured against your own team's ground truth.
Scope a pilotNo obligation · NDA available · measured results before you commit