40%
Reduction in batch deviations reported in the engagement brief
Anonymized engagement brief
Pharma manufacturingIndia
A private AI assistant for deviation investigation.
An anonymized engagement brief describing source-grounded search across controlled quality records, with human approval retained by the quality team.
Engagement answer
NeoBram designed a private retrieval assistant for a defined pharma deviation-investigation workflow. It connected approved quality and batch records, returned source-linked passages and preserved human approval before any controlled record changed. The outcome figures below came from the non-public engagement brief; they are not independently verified and should not be used as a forecast for another site.
Reported outcomes
What changed in the stated scope.
55%
Faster CAPA closure reported in the engagement brief
Anonymized engagement brief
12h
Investigation time reduction reported per reviewed case
Anonymized engagement brief
0
Post-go-live audit finding count stated in the brief
Anonymized engagement brief
These figures are not a guarantee or a benchmark for another organization. A new project requires its own baseline, scope, measurement method and acceptance test.
Client context
Who the brief describes
An anonymized pharmaceutical formulations manufacturer in India. The customer name, facility, audit history and supporting records are not public, so certifications and market claims are intentionally omitted from this version.
Business problem
What needed to change
Investigators needed to search fragmented batch, laboratory, quality and procedure records while retaining traceability and qualified human judgement. The project focused on retrieval and investigation support, not autonomous quality decisions.
Baseline
Where the work started
- Deviation and CAPA evidence was distributed across controlled systems and documents.
- Investigators depended heavily on experienced quality staff to locate comparable events.
- The project required source traceability, role-based access and a defined review step.
Data
Evidence used by the system
- Approved extracts from historical batch and quality records
- Laboratory and out-of-specification records within the agreed scope
- Controlled SOPs, master records and selected validation documents
- Permissions and document-status metadata needed to filter retrieval
Solution
What NeoBram built
The engagement brief describes a retrieval-augmented assistant deployed inside a customer-controlled environment. It searched the approved corpus, returned source-linked passages, surfaced comparable records and showed uncertainty for reviewer judgement. The quality organization retained authority over investigation conclusions and controlled-system updates.
Integration
Systems and interfaces
- Customer-approved read-only access or controlled extracts from quality systems
- Document ingestion with status, version, owner and permission metadata
- Customer identity and role mapping
- Reviewable interaction logs and controlled escalation
Timeline
Reported delivery sequence
The work followed discovery and corpus qualification, a bounded technical evaluation, a production pilot and a wider operating rollout. Exact dates are held in non-public engagement records. New projects use NeoBram's standard planning ranges and are adjusted for validation, access and change-control requirements.
Governance
Controls described in the brief
- Customer quality owners approved the intended use and review workflow
- Source references were preserved for investigator review
- Access and logs were designed around the selected quality-system boundary
- AI output did not approve or close a deviation or CAPA
- Regulatory suitability depended on the customer's validated implementation and procedures
Measurement method
How to read the outcome
- Reported comparisons were made against the engagement's stated pre-project operating baseline.
- The public page does not include the source dataset, denominator, exclusions, calculation workbook or signed customer attestation.
- A buyer should request those records under an appropriate confidentiality process before relying on the figures.
Limitations
What this brief does not prove
- This page does not establish compliance with FDA, EU GMP or another regulatory regime.
- Retrieval quality depends on controlled-document status, permissions, chunking, evaluation cases and reviewer behaviour.
- The result cannot be separated publicly from other process, training or quality changes made during the period.
- Another facility requires its own intended use, risk assessment, validation plan and acceptance criteria.
Build your own evidence record
Define the baseline and acceptance test before the pilot.
NeoBram will help turn one operating problem into a scoped, reviewable AI project and hand over the production capability.