Industrial AI solution

GxP SOP AI Assistant For Pharma Manufacturing

GxP-validated AI assistant that answers operator and QA questions from SOPs, batch records and product-quality reviews - with 21 CFR Part 11 audit trails and full validation documentation.

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Pharma operator using a GxP-validated SOP AI assistant

Acceptance before scale

Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

Direct answer

NeoBram builds GxP-validated SOP and batch-record AI assistants for pharma manufacturing - answering operator, QA and production questions from SOPs, MBRs, BMRs and PQRs with full 21 CFR Part 11 audit trails, GAMP 5 validation, and integration with Werum PAS-X, Siemens Opcenter, LIMS and QMS.

Private deployment available

Why evaluate it

SOPs Are The Backbone. And Nobody Reads Them.

The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

A typical pharma site has 2,000-10,000 controlled SOPs, work instructions, MBRs and policies. Operators and QA staff routinely re-train, re-read, and re-search for the same answer - or skip the step because finding it is too slow.

A GxP-validated AI assistant gives instant cited answers from the controlled document set, in the operator's language, with every interaction logged for audit. Deviations, batch-record questions and CAPA research that took 30-60 minutes complete in seconds.

We deploy with full GAMP 5 / CSV validation - URS, functional spec, IQ/OQ/PQ, change-control plan, 21 CFR Part 11 audit trails, on-premise or validated private cloud. The assistant is positioned as a decision-support tool with citations, not as a controlling system.

Candidate capabilities

What a production solution may need to do.

Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

Cited SOP & MBR Q&A

Operators, QA and supervisors get instant answers from the controlled document set - every response cites the exact SOP, section and revision. Stale or obsolete documents are filtered out.

Citations to SOP number, section and approved revision
Always-current approved revisions only
Refusal to answer when context is insufficient
Multilingual - operators ask in their language

Deviation, CAPA & PQR research

QA and production investigators query the full history of deviations, CAPAs, change controls and PQRs to find precedent, root cause patterns and effective corrective actions.

Search across TrackWise / Veeva QMS deviations
Pattern detection across similar past events
Effective-CAPA recommendation from precedent
Annual PQR data assembly and summarisation

21 CFR Part 11 audit trail

Every question, retrieved source, generated answer and operator action is logged immutably with user, timestamp, model version and dataset hash - audit-ready for FDA, EMA, MHRA, CDSCO.

Immutable, append-only audit trail
Cryptographic linkage of model + dataset + answer
Periodic audit-trail review reports
Inspector-ready export of all interactions

Multilingual operator interface

Operators across global sites use the assistant in their native language while source SOPs remain in English (or local approved language). Translation is shown with source alongside.

30+ languages including Hindi, Mandarin, Spanish, Japanese
Source-language answer always available beside translation
Voice input via on-prem Whisper
Tablet, HMI and desktop access

Architecture context

Select components after the boundary and test are clear.

The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

Foundation models

Llama 3.3 70B (on-prem)
Open-weight for sovereignty
Azure OpenAI GPT-4o
Validated cloud option
Mistral / Mixtral
Efficient on-prem alternative

Validated RAG stack

LlamaIndex / LangChain
Orchestration with citation enforcement
Qdrant / pgvector
Vector store with audit log
BGE-M3 embeddings
Multilingual retrieval
Unstructured.io / LayoutLM
MBR & SOP parsing

GxP validation

GAMP 5 CSV package
URS, FS, IQ, OQ, PQ
21 CFR Part 11 audit trail
Immutable interaction log
Model & dataset versioning
Cryptographic hashes
Periodic review framework
Annual revalidation

Source systems

Veeva Vault QualityDocs
Controlled SOPs
MasterControl / TrackWise
QMS & deviations
Werum PAS-X / Siemens Opcenter
MES & batch records
LabWare / STARLIMS
LIMS context

Planning ranges

A standard path from decision to operation.

Discovery and qualification

1-2 weeks

Named workflow, owner, baseline, risks and go/no-go questions.

Readiness assessment

2-4 weeks

Data, integration, security, value and operating-readiness findings.

Technical proof of value

4-6 weeks

A bounded test on representative data with documented limitations.

Production pilot

8-12 weeks

One controlled workflow, integrated and evaluated with real users.

Enterprise or multi-site rollout

3-6+ months

Phased scale-out, monitoring, support and change management.

AI capability or CoE programme

3-6+ months

Governance, delivery methods, reusable assets and team enablement.

These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

Production safeguards

Private deployment is one control, not the whole control system.

Data boundary

Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
Document data flows, storage, deletion, backups and support access before production.
The customer approves every interface and any permitted external connection.

Model and application controls

Evaluate representative cases, uncertainty and harmful failure modes before use.
Use suitable access control, input handling and output guardrails for the selected risk.
Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

Traceability and operation

Define identity, roles, logs, monitoring, updates, backup and incident handling.
Keep the evidence needed to investigate outputs and reproduce important decisions.
Assign a named business and technical owner for production operation.

Human authority

Domain, quality, safety and regulatory owners retain decision authority.
Compliance depends on the implemented system and the customer's validated controls.
Escalation and safe fallback are part of the acceptance criteria.

Limitations to plan for

Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

Buyer questions

Direct answers before you plan a pilot.

How is an LLM-based system validated under GAMP 5?+

This legacy draft included a quantitative benchmark that has not been published with a reviewable source or customer evidence. NeoBram now treats it as an open validation question and defines the baseline, test method, acceptance threshold and limitations during discovery.

How do you prevent hallucinations in a GxP context?+

Three layers: (1) strict RAG with the model instructed to answer only from retrieved passages; (2) citation enforcement - the system refuses to display answers without source citations; (3) a guardrail layer that detects ungrounded claims and forces a refusal. The system is positioned as decision-support with mandatory operator review for any action - it does not autonomously execute production steps.

Can the assistant write or modify batch records?+

No. By design the assistant is read-only against the controlled document set and write-only against its own audit log. It does not modify SOPs, MBRs, BMRs or batch records. Authoring assistance (e.g. drafting a deviation investigation) produces a draft that flows through your existing QMS approval workflow.

How do you handle model updates without re-validation?+

A change-control matrix categorises changes: (a) document re-indexing (no revalidation), (b) embedding model swap (regression test against gold set), (c) LLM version upgrade (full OQ re-execution), (d) architecture change (full re-qualification). All categories are pre-agreed with site QA in the validation master plan.

What does the 21 CFR Part 11 audit trail capture?+

Every interaction logs: user ID and role, timestamp, question text, retrieved document IDs and revisions, full model response, model version hash, dataset version hash, operator follow-up action, and any feedback flag. Logs are write-once, cryptographically linked, retained per your record-retention policy, and exportable for inspection.

What's the deployment & validation timeline?+

A focused production pilot is commonly planned over 8 12 weeks after scope, representative data, owners and acceptance tests are ready. Discovery or a technical proof of value may be shorter; integration, validation, hardware, site access and change management can extend the plan. This is a planning range, not a delivery guarantee.

Bring one workflow

Define the evidence, boundary and acceptance test together.

Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

Plan the first project