Pharma & Life Sciences · Pharmacovigilance automation
The 15-day serious AE clock does not wait for a case backlog to clear
Banao builds NLP-driven pharmacovigilance automation that reads adverse-event narratives, extracts the structured fields, classifies seriousness and expectedness, and routes the cases a human pharmacovigilance professional must review to the front of the queue — before the reporting deadline.
The system handles spontaneous reports, literature signals, and digital-source intake in one pipeline. Human sign-off stays in place where the regulation and your SOPs require it; the automation removes the triage and data-entry work that currently consumes your PV team's day.
Hummcare— clinical and case documents structured for downstream review, with each model decision logged for audit.
The first call is free · 45 minutes · no obligation
What we build
What a Banao pharmacovigilance automation covers
Each component addresses a concrete time and compliance cost your PV operation carries today — manual classification, data entry, literature screening, or deduplication across sources.
AE narrative intake and entity extraction
NLP reads incoming case narratives — from CSRs, patient calls, digital channels, and literature — and pulls the structured ICH E2B fields: reaction terms (MedDRA coded), suspect drug, patient demographics, and reporter details.
Seriousness and expectedness classification
A classification model pre-assesses each case against the serious criteria (fatal, life-threatening, hospitalisation, congenital anomaly) and the reference safety information, surfacing cases that must reach a qualified person within the expedited timeline.
Case routing and workload management
Classified cases are routed by urgency, product, region, and reporter type to the right PV associate — so your team opens a prioritised queue, not an undifferentiated inbox, and 15-day SUSARs reach a qualified reviewer the same day they arrive.
Literature monitoring automation
Systematic searching of PubMed, Embase, and grey-literature sources for safety-relevant publications — NLP identifies the articles a pharmacovigilance reviewer must assess, so weekly screening takes hours instead of days.
Duplicate detection across sources
A deduplication model identifies cases that have arrived from more than one channel — spontaneous report, clinical site, and literature — before they are processed separately and reported twice to a competent authority.
E2B pre-population and submission preparation
Extracted fields pre-populate the ICH E2B(R3) message so the PV associate reviews and confirms rather than types. A validation layer flags missing mandatory fields before the report is submitted to the authority.
Receipts
Deployed on clinical and case documentation
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Case documents structured and routed for clinical review
For a digital-health operation handling sensitive clinical and case paperwork, Banao built document intelligence that extracts structured fields, flags which documents need a clinician's attention, and logs every model decision. The human reviewer stays in the loop on every sensitive case; the NLP removes the reading and classification work that preceded that review.
Dogfooding
We run AI on our own operation before we sell it
Banao operates a ~300-person engineering company on its own AI products daily. InterviewGod screens our own engineering hires before any client sees it. Vikaas runs our own demand-generation pipeline end to end.
A model that must survive our internal operation has already been tested on accuracy, audit trails, and the edge cases that break things in production. That standard is what we hold pharmacovigilance automation to — the version that reaches your PV system is already hardened.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When pharmacovigilance automation is not the right step
We would rather give you an honest read in the first call than sell you automation that creates new compliance problems:
- Very low case volume: below a few hundred cases a year, the classification model returns less value than the validation overhead it adds in a GxP environment. We'll say so and suggest a different priority.
- Immature PV process: if your SOPs, reference safety information, and case-handling procedures are not yet stable, automating an undefined process locks in the inconsistency. The right first step is process, not automation.
- Causality and medical assessment: NLP can surface structured data and flag priority, but causality determination and medical assessment of a SUSAR requires a qualified person. We do not automate away the medical judgment — we make the supporting work faster.
How we start
How we engage — start with the problem, not the build
A pharmacovigilance automation project that fails at validation costs more than the manual process it replaced. We prove viability before you approve a budget.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit a sample of your real AE narratives and case-intake data, test NLP extraction accuracy on your actual report formats, and deliver a baseline accuracy estimate, a regulatory risk read, and ROI maths for case-processing time saved — yours to keep. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Data engineering on your case management system feeds, then extraction and classification model training on your report corpus. E2B pre-population and routing logic are built as validated, auditable components — with the documentation your QA and regulatory teams will need.
- 03
Production & supervised rollout
Deployment alongside your existing ICSR workflow, with a side-by-side accuracy period before automation takes primary routing. PV team change management is a deliverable — adoption determines whether the system holds at inspection.
FAQ
Frequently asked questions
Which regulatory submissions does this support?
The automation covers individual case safety reports (ICSRs) submitted in ICH E2B(R3) format to competent authorities including FDA, EMA, and MHRA. Literature monitoring and signal detection outputs feed periodic safety reports (PSURs/PBRERs) but do not replace the medical authorship those require.
How does the system handle expectedness and causality?
NLP classifies expectedness against the reference safety information (RSI) for the product. Causality assessment is surfaced as a suggested classification with the supporting narrative — a qualified person reviews and confirms. We do not remove the medical judgment from seriousness or causality; we remove the clerical work that precedes it.
Can it process reports from multiple sources simultaneously?
Yes. The intake pipeline handles spontaneous reports from your safety database, literature references from monitoring searches, digital and social-media signals, and clinical trial ICSRs in a single classification and routing flow — with deduplication before a case is counted or routed.
How is the model validated for a GxP environment?
We build the classification model as a validated computer system component: documented user requirements, configuration specifications, testing evidence, and audit trail. A qualified-person review step is built into every routing path. The validation documentation is part of the deliverable, not a handover task left to your IT department.
What does the 2-week Discovery Sprint produce?
A baseline NLP accuracy test on a sample of your real AE reports, an assessment of your case-intake data quality and what pre-processing it needs, a regulatory risk read on where human oversight is non-negotiable, and ROI maths on case-processing time and expedited-reporting compliance cost — all yours to keep whether or not you build.
Get started
Find out where your PV backlog can be cleared by NLP
Bring a sample of your real AE narratives and your current expedited-reporting miss rate. In 45 minutes we'll map the automation opportunity, what validation will take, and whether the ROI justifies the build.
Book a Discovery Sprint