The operating system for safety intelligence
Every vGenix Ai module runs on one shared core: one brain, one data model and one set of rules. Not just AI-native, but neurosymbolic.
Neural language understanding + symbolic reasoning over knowledge graphs, ontologies and your SOP rules
The whole safety lifecycle on one core
Intake to submission runs on the same data model, the same rules and the same audit trail. Nothing is re-keyed between steps, and every step can see what the others know.
Bolt-on AI multiplies your problems
Adding a separate AI tool to each process gives you separate models, rules, validations and audit trails to manage. vGenixOS puts one governed core underneath everything, the way an operating system sits under every app.
Every tool is its own island
- A separate validation for every tool
- Different answers to the same question
- Evidence scattered across vendors
- Nothing one tool learns reaches the others
One core, shared by every module
- One validation approach and change control
- The same rules applied everywhere
- One audit trail for people and AI
- What one module learns, every module uses
Six layers, one operating system
If you know how a computer works, you already know vGenixOS: apps, permissions, a processor, drivers, a kernel and a file system. Select a layer to see what it does.
The modules your teams use every day
Five modules share one case model, one set of rules and one audit trail. Nothing is re-keyed between them, and every module sees what the others know.
- Case processing
- Literature monitoring
- Signal management
- Aggregate reports
- Regulatory submission
- One record from intake to submission
- Your SOP rules applied in every module
- One audit trail across all of them
One governed path for every action
Every action, by a person, an AI agent or a connected system, passes through the same controls for access, AI, data, process gates, deadlines, evidence, change and partner exchange.
- Identity and access
- AI governance
- Data protection
- Process and SOP gates
- Time, evidence, change, partners
- Nothing acts unscoped
- Nothing skips a required gate
- Every action leaves evidence
The part that thinks, and checks itself
Neural AI reads unstructured sources the way an expert would and proposes an answer. Symbolic AI checks it against rules and the knowledge graph, then accepts it with its reasoning, routes it to a person, or rejects it.
- Language and document understanding
- Rule checks against your SOPs
- Reasoning over the knowledge graph
- Confidence scoring and routing
- Every output has a source
- Every assessment shows its rule
- Uncertain work goes to people
Domain knowledge a generic AI lacks
Four intelligences supply safety terminology and clinical context, the ability to read any document, agency rules and timelines, and a running check against GVP and your SOPs.
- Pharmacovigilance Intelligence
- Document Intelligence
- Regulatory Intelligence
- Compliance Intelligence
- Configured to your products and markets
- Versioned under change control
- Also available as APIs (IntaaS)
Five small cores everything runs on
The parts that must never fail are kept small, stable and validated, and every module relies on the same ones.
- Case kernel
- Clock kernel
- Rules kernel
- Knowledge kernel
- Evidence kernel
- The same answer in every module
- Deadlines that never depend on memory
- A complete history for every case
Connected data, not isolated records
Every case, product, event, source document, signal and rule is connected in one graph, with standard terminologies and interfaces to the systems you already run.
- One safety data model
- MedDRA · WHO Drug · your product dictionary
- E2B (R3)
- Argus · ARISg · Veeva Vault Safety · APIs
- Facts linked to their source
- Duplicates and follow-ups found
- Every module sees the same picture
Five small cores that everything runs on
In an operating system, the kernel is the part that must never fail, so it is kept small and changed carefully. vGenixOS has five safety kernels. Modules come and go; the kernels stay stable, validated and shared.
1Case kernel
One case model for every module and domain. Every change creates a new version; nothing is overwritten.
2Clock kernel
Holds day 0, the due date for every market and report type, and the escalation rules around them.
3Rules kernel
Your SOPs, GVP and local requirements as explicit, versioned rules and decision tables.
4Knowledge kernel
The safety knowledge graph: MedDRA, WHO Drug, your product dictionary and the links between cases, sources and signals.
5Evidence kernel
The append-only audit trail, e-signatures and source links for every action, human or AI.
Why it matters for validation: because every module relies on the same kernels, a new module or report builds on cores that have already been validated, rather than starting from scratch. Validation scope for each change is agreed with your QA team.
Neural AI understands. Symbolic AI checks.
Generative AI is very good at reading messy, unstructured safety data, and it can also produce a confident answer that is wrong. Rule-based systems are exact but cannot read. vGenixOS puts them in series, so one proposes and the other decides.
| Capability | Neural AI alone | Rules alone | Neurosymbolic |
|---|---|---|---|
| Reads free text, scans and any language | Yes | No | Yes |
| Follows explicit rules exactly | Not reliably | Yes | Yes |
| Shows why it reached an answer | Hard to show | Yes | Yes |
| Handles cases nobody wrote a rule for | Yes | No | Yes, and routes them to people |
| Can state facts that are not in the source | Possible | No | Checked before anyone relies on them |
Four worked examples
Illustrative examples of how the two halves work together. Exact behaviour is configured from your SOPs.
Ask any answer “why?”
In drug safety, “the model said so” is not evidence. Every vGenixOS answer carries its source, the rule it applied, how sure it is, and who confirmed it. Try it.
Why “serious”?
Why this term?
Why day 15?
Why “possible”?
Governs every action, not only AI
Most AI control planes govern model calls. In safety, the AI is only part of the risk. The vGenixOS control plane governs every action, by a person, an AI agent or a connected system, and every action leaves evidence.
Identity and access
Named users, roles, markets and products; segregation of duties; single sign-on.
AI governance
Validated models only, prompts under change control, confidence thresholds, model versions recorded.
Data protection
Data minimisation for each task, regional residency, masking of personal data where it is not needed.
Process and SOP gates
Review and approval steps set from your SOPs; nothing skips a required gate.
Time and deadlines
Every case on its clock; warnings and escalation before a deadline is at risk.
Evidence and audit trail
Every create, change and delete recorded with old and new values and a reason; e-signatures with their meaning.
Change control
Rules, configuration, models and prompts change only through approved, versioned releases.
Partners and exchange
Safety data exchange with licensees and vendors tracked against agreements; reconciliation built in.
What the control plane checks, and what it records
| Action | What the control plane checks | Evidence it records |
|---|---|---|
| A case processor changes seriousness | Is the role allowed? Is a reason given? Does the clock need recalculating? | Old and new value, reason, user, time; new due date |
| An AI agent suggests a MedDRA term | Is the model version validated? Is confidence above the threshold for this field? | Suggestion, model version, confidence, routing |
| A safety physician signs causality | Is this the right person for this gate? Is the signature’s meaning captured? | E-signature with meaning, time and the record signed |
| A licensee sends a case | Is the agreement in force? What is the exchange deadline? | Receipt time, day 0, reconciliation entry |
| An administrator edits a rule | Is there an approved change request? Is validation evidence attached? | New rule version, approver, effective date |
| A user exports case data | Is the purpose allowed? Does the export respect data residency? | Export log with user, scope and purpose |
This is what electronic-records rules ask for: under 21 CFR 11.10(e), secure, computer-generated, time-stamped audit trails of operator entries and actions that do not obscure earlier values; under EU GMP Annex 11, a documented reason for each change or deletion of relevant data. Read our guide to audit trails in pharmacovigilance.
Agents that do the work, inside the guardrails
vGenixOS runs specialised agents for each step of the lifecycle. They work through the control plane, so each one has a scope, follows your SOPs and knows when to hand over.
Reads every source in any format or language, checks the four minimum criteria and finds duplicates and follow-ups.
Codes events to MedDRA and products to WHO Drug and your product dictionary, with a confidence score for each term.
Drafts case narratives and proposes seriousness, expectedness and causality, with the evidence for each.
Runs your searches, screens abstracts and full texts, and extracts reportable cases into intake.
Runs disproportionality and trend detection, clusters related cases and prepares the evidence for review.
Builds E2B (R3) files, checks business rules, tracks every regulatory clock and handles acknowledgements.
Gets better with use. Never changes behind your back.
Each module learns from what your experts confirm and correct, shares it in one knowledge graph, and every module benefits. Improvements are validated and released like any other change.
Learns across modules
A product term confirmed in a case is recognised in literature screening. A cluster spotted in literature becomes an input to signal detection. Coding decisions improve suggestions in every module.
Never changes without you
- Models and prompts in live use
- Rules, thresholds and review gates
- Anything that affects validated behaviour
Your data, your rules
Learning happens within the boundaries set in your agreement and configuration. [Confirm and state the data use policy here.]
Designed as an
AI-native safety OS
vGenixOS was not built by adding AI to an old workflow. These four principles shape how it works.
Always on
Batch runs and backlogsWork flows continuously as sources arrive, so nothing waits for the next run.
Validated at every step
Checks only at the endEach step is checked as it happens, so errors are caught where they start.
Decisions, not tasks
Queues of routine workExperts see what needs judgement, with the evidence ready, instead of re-keying data.
Humans in control
Black-box automationYour SOPs decide where people review, approve or override, and every choice is recorded.
Start where it fits your landscape
vGenixOS is not a separate product you have to adopt on its own. Use it through the modules, through APIs, or alongside the systems you already run.
Run every process on it
Use the vGenix Ai modules end to end, from intake to submission, on one core.
Explore the platform →Call it from your systems
Use Pharmacovigilance, Document, Regulatory and Compliance Intelligence through secure APIs in your current workflows.
Explore IntaaS →Keep your system of record
Add intake, preparation and checks around Argus, ARISg or Veeva Vault Safety, one process at a time.
Why this works →vGenix Ai is built to support EU GVP, ICH E2B (R3), 21 CFR Part 11 and EU Annex 11. Validation and regulatory responsibility are agreed for each implementation.
See vGenixOS on your own cases
Bring a sample of your reports. We’ll show every answer, its source and its reasoning, in a working session with your safety, QA and IT leads.
6-week pilot on literature monitoring
- Baselines agreed up front
- Configured to your SOPs
- Measured on your own data
- Validation evidence from day one