Pharma quality and manufacturing intelligence on AWS, Databricks and Claude.

Pharmaceutical manufacturers already hold the data needed to understand most quality issues. The evidence is simply fragmented across MES, LIMS, QMS, ERP, batch records, SOPs, equipment data and past investigations. We build a governed intelligence layer across those systems, on AWS and Databricks, and use Anthropic Claude to help quality and manufacturing teams investigate, understand and act on the evidence faster. Nothing validated is replaced. An intelligence layer is added across it.

Book a readiness assessment See the architecture

Built for manufacturers in India and Canada, and for the US and EU markets they export to.

Built onAWSDatabricks LakehouseUnity CatalogDatabricks Vector SearchMLflowAnthropic Claude via Amazon Bedrock

Target architecture

An intelligence layer across the systems you already run.

Data leaves the source systems only to land in a governed lakehouse under your own AWS account. Databricks correlates, computes and retrieves. Claude reasons over governed evidence and writes for the person reading. Applications sit on top, one per persona.

01
Enterprise systems
ERP, MES, LIMS, QMS, documents
02
AWS
S3, IAM, KMS, private networking
03
Databricks
Delta, Unity Catalog, ML, search
04
Governed evidence
Data, documents, events, rules
05
Claude
Reasoning, retrieval, drafting
06
Applications
Copilots and dashboards
Every fact shown to a person carries its source record. Every model, prompt and evidence set used to produce a draft is versioned and logged.

Sources

SAP, Oracle or Dynamics for ERP. MES, historians, SCADA and equipment sensors for manufacturing. Veeva Vault QMS, TrackWise, MasterControl and similar for quality. LabWare, LabVantage and other LIMS for the laboratory. SharePoint, S3, network drives and electronic batch records for documents, SOPs, audit reports and maintenance records.

AWS

An S3 data lake with IAM, KMS encryption, Secrets Manager, VPC and PrivateLink, CloudTrail and CloudWatch, deployed in the region that matches your residency requirement: Asia Pacific Mumbai for India, Canada Central for Canada.

Databricks

Lakeflow ingestion into a Delta Lake medallion architecture, Unity Catalog for governance and lineage, data quality checks, Databricks SQL, Vector Search over documents and events, MLflow, feature engineering and the statistical and machine learning models that watch the process.

Governed evidence

Structured data, documents, historical events, business rules, SOPs and analytics assembled into an evidence set with provenance, so Claude only ever sees what governance allows and every item can be traced back.

Claude

Evidence reasoning, document understanding, investigation assistance, summarisation, tool use and agent workflows, through Amazon Bedrock with the region and inference profile chosen to match your residency requirement.

Applications

A deviation investigation copilot, CAPA intelligence, a manufacturing quality dashboard, a QA executive copilot and an audit preparation assistant, each shaped for its persona.

The principle

AI never replaces the pharmaceutical quality decision.

The platform separates three kinds of processing and gives each one only the job it is reliable at. That separation is what makes the output trustworthy enough to sit inside a quality system, and it is why hallucination risk falls rather than rises when Claude is added.

Deterministic computation

Numbers, thresholds and statistics.
Quality rules, dates and durations.
Batch comparisons and calculations.
Whether a validated limit was breached.

Computed in Databricks. Claude never calculates what a query can.

Machine learning

Anomaly detection across process parameters.
Prediction and trend detection.
Clustering of similar events.
Pattern recognition across sites and lines.

Trained and tracked in MLflow, with every model version recorded.

Claude

Synthesis and explanation.
Document reasoning over SOPs and records.
Retrieval of related evidence.
The interaction with the investigator.

Receives governed evidence only. Distinguishes facts, correlations, hypotheses and recommended steps.

The split, in one example

Databricks computes

Temperature excursion: 81.7 °C
Validated upper limit: 78 °C
Duration above limit: 17 minutes

Claude explains

What occurred, in plain language
Which related records exist
Which investigation paths to consider

First release

Three connected modules.

The first release focuses on three tightly connected capabilities. Each is built on the same lakehouse and the same evidence layer, so a pattern found in one is available to the others.

Module 01

Deviation investigation copilot

For the QA investigator

When a deviation is raised, the platform gathers the relevant context from batch records, MES, LIMS, QMS, equipment history, SOPs, environmental monitoring, previous deviations and CAPAs. Databricks handles correlation, historical comparison, anomaly detection and governed retrieval. Claude turns the evidence into an investigator-friendly workspace.

Claude never declares a root cause. It separates facts, correlations, hypotheses and recommended steps.

The question

“Investigate the dissolution failure for this batch and show similar historical events.”

What comes back

Abnormal process parameters with their values
Previous batches with a similar profile
Related equipment and maintenance events
Relevant SOP sections
Historical CAPAs
Suggested investigation paths, each linked to its source

Module 02

CAPA intelligence

For the QA manager, CAPA owner, audit team

Intelligence across the historical CAPA record. Databricks computes recurrence, effectiveness and trend metrics. Claude explains the findings and retrieves the supporting documentation, so a recurring pattern is a question rather than a quarterly review.

Every metric is computed deterministically. Claude narrates and cites; it does not score.

The question

“Which CAPAs closed in the last twelve months show evidence of recurrence, and on which lines?”

What comes back

Recurring failure chains, such as repeated excursions on one line after similar maintenance events
Repeat deviation rate after CAPA completion, by line and department
CAPAs that failed their effectiveness checks
Root cause categories and their trends
The records behind each finding

Module 03

Manufacturing quality intelligence

For the Manufacturing manager, site head

Continuous monitoring of process parameters, batch-to-batch variation, laboratory results, equipment data, environmental monitoring, yield, rejects, deviations, OOS and OOT results and maintenance history. Statistical and machine learning models flag unusual patterns before they become quality events.

The model flags. A person decides what to do about it.

The question

“What quality risks have increased on this line in the last thirty days?”

What comes back

Deviation frequency and its change
Parameter variation that has widened
Maintenance incidents on the same subsystem
Batches with borderline results
A concise operational explanation with the evidence one click away

A worked investigation

What an investigator sees when a result is out of specification.

Illustrative. The batch, records and values are invented to show the shape of the output.

Event

Batch: B24087
Product: Product-X 20 mg
Test: Dissolution
Result: 71%
Specification: 80% or above

Databricks gathers and finds

Databricks retrieves the batch manufacturing data, process parameters, raw material lots, operator records, environmental conditions, equipment history, previous batches, laboratory data, historical OOS events, deviations, CAPAs and applicable SOPs.

Granulation stage 3 temperature 6.8 °C above the historical distribution.
Temperature sensor T-17 involved, calibration approaching its scheduled interval.
Two previous batches, B20114 and B19821, with a similar excursion and similar dissolution degradation.

Claude drafts the investigation summary

Observed issue

Dissolution testing for batch B24087 returned 71% against an acceptance criterion of 80% or above.

Relevant manufacturing anomaly

Granulation stage 3 recorded higher-than-normal temperature conditions.

Historical correlation

Two previous batches with similar temperature excursions also showed reduced dissolution performance.

Equipment relationship

The historical events involved temperature sensor T-17.

Potential investigation area

Sensor calibration and stage 3 temperature control should be reviewed.

Supporting records

Batch B24087, deviation DEV-20114, deviation DEV-19821, maintenance record MNT-4581, SOP-QA-142.

Potential relationship identified. Root cause has not been established.

Approval workflow

AI generates the investigation draft
QA investigator reviews: accept, modify or reject
QA manager reviews
Approved investigation

Recorded with every draft

AI-generated contentUser editsEvidence usedModel versionPrompt versionTimestampApproverFinal decision

Running a plant in India or Canada?

A readiness assessment tells you what is buildable on the data you already have.

Bring your current systems list and the quality metrics your leadership asks about. In two to four weeks you get a data-source inventory, a GxP and AI risk assessment, a target architecture and a costed roadmap, whether or not you continue with us.

Book a readiness assessment See the compliance design

Who uses it

One layer, a different experience per role.

A site head and an investigator should not see the same screen. The evidence layer is shared; the applications on top are shaped for each person and each question.

Persona	Primary value	A question they ask
QA investigator	Faster deviation investigations	“Show every manufacturing event related to this deviation.”
QA manager	Recurring issue visibility	“Which CAPAs have failed their effectiveness checks?”
Manufacturing manager	Process risk monitoring	“Where has process drift widened this month?”
CAPA owner	CAPA effectiveness monitoring	“Has this issue recurred since closure?”
Lab manager	OOS and OOT intelligence	“Find similar OOS dissolution events for this product.”
Site head	Quality trends and operational risk	“What are the five biggest quality risks at this facility?”
Auditor	Evidence retrieval and traceability	“Show the evidence and approvals behind this investigation.”
Executive	Cross-site quality intelligence	“Summarise quality performance across our three sites this quarter.”

Dashboards

Four views, one evidence layer.

Quality command centre

Open deviations, deviation ageing, OOS and OOT trends, CAPA backlog, repeat deviations, critical quality events, site comparison and a risk score.

Manufacturing intelligence

Batch variability, critical process parameters, yield trends, equipment anomalies, process drift, environmental monitoring and quality correlation.

CAPA effectiveness

CAPAs created and overdue, recurrence, effectiveness checks, root cause categories, department trends and repeat issues.

AI investigation workspace

Event summary, evidence, similar events, timeline, data analysis, relevant SOPs, hypotheses, investigation questions, draft narrative and approval. This is where Claude works.

In plain language

The questions the platform answers.

Each question is answered from governed evidence, with every figure computed in Databricks and every record linked back to its source.

“Show deviations involving Line 3 in the past eighteen months where temperature was potentially contributing.”“Which CAPAs have failed their effectiveness checks?”“Find similar OOS dissolution events for this product.”“Were there any maintenance events within thirty days before these failures?”“Which production lines have the highest repeat deviation rate after CAPA completion?”“Summarise quality performance across our three manufacturing sites this quarter.”

Integrations

Above your existing systems, not in place of them.

The platform reads from the systems a manufacturer already runs and validated. It does not ask you to migrate, re-validate or replace any of them.

Enterprise

SAP
Oracle
Microsoft Dynamics

Manufacturing

MES
Historians
SCADA
Equipment and IoT sources

Quality

Veeva Vault QMS
TrackWise
MasterControl
Similar QMS platforms

Laboratory

LabWare
LabVantage
Other LIMS

Documents

SharePoint
Amazon S3
Network drives
PDF repositories
Electronic batch records

Compliance architecture

Designed to support your validated environment.

For a pharmaceutical manufacturer this cannot be an afterthought. The platform is designed around the controls that environments operating under GxP expect, and positioned carefully: it supports your validated environment and your data-integrity obligations. It does not claim to make you compliant on its own.

India

CDSCO expectations and the revised Schedule M good manufacturing practice requirements, with the AWS Asia Pacific Mumbai region available for data residency.

Canada

Health Canada good manufacturing practice guidance for drugs, with the AWS Canada Central region available for data residency.

Export markets

The frameworks Indian and Canadian manufacturers already work to for the US and EU: FDA 21 CFR Part 11, EU Annex 11, GAMP 5 and ALCOA+ data-integrity principles, with ICH Q9 and Q10 informing the risk and quality-system design.

Controls in the architecture

Role-based access and least privilege
Encryption at rest and in transit
Private networking
Data lineage in Unity Catalog
Immutable logging and audit trails
Model and version tracking
Prompt versioning
Evidence provenance on every output
Human approval before anything is recorded
Retention policies
PII and PHI controls where applicable
Validation support documentation

How the engagement runs

Four phases, starting with an assessment rather than a platform.

The first step is a short assessment that establishes your baseline, your data sources and your priority use cases. Targets for the build are agreed against that baseline, which is how the return is measured rather than promised.

01
AI readiness and quality assessment
Two to four weeks
What happens
Current architecture, data-source inventory, priority use cases, a GxP and AI risk assessment, target architecture, integration map and a return analysis against your measured baseline.
What you get
Assessment report, target architecture, integration map, implementation roadmap.
02
Quality intelligence foundation
Six to ten weeks
What happens
The AWS environment, the Databricks lakehouse with Unity Catalog, ingestion from QMS, LIMS, manufacturing and document sources, Vector Search, the Claude integration, and security and observability.
What you get
Governed lakehouse, connected sources, evidence layer, security and monitoring.
03
Investigation and CAPA intelligence
Scoped per site
What happens
The deviation copilot, similarity retrieval, CAPA intelligence, the investigation timeline, evidence retrieval, AI-generated drafts, approval workflows and the analytics dashboards.
What you get
The three modules, the four dashboards, approval workflow, evaluation set.
04
Managed pharma intelligence
Ongoing
What happens
Pipelines and Databricks operations, Claude evaluation, prompt and model management, monitoring, cost management, security reviews, and new agents and use cases as the platform grows.
What you get
A running platform with a named team behind it.

The return

Measured against your baseline, not promised in the abstract.

The gains are operational and specific. During the assessment we measure how long each of these takes today, and the build commits to targets against those numbers.

Activity	Typical today	Target state
Deviation investigation	Ten to twenty hours of manual work	Evidence gathered in minutes, preparation materially faster
Finding related historical cases	Hours of searching	Seconds to minutes
SOP and document search	One to three hours	Immediate, with the section cited
Cross-system investigation	Manual, across many systems	One workspace
Recurring issue discovery	Periodic and manual	Continuous
Management visibility	Monthly reports	Near real time

Pharma quality intelligence, answered.

What is the pharma quality and manufacturing intelligence platform?

A governed intelligence layer built across a pharmaceutical manufacturer's existing MES, LIMS, QMS, ERP, batch records, SOPs and equipment data, on AWS and Databricks, with Anthropic Claude used to help quality and manufacturing teams investigate issues faster, find recurring patterns and make evidence-backed decisions. The first release covers a deviation investigation copilot, CAPA intelligence and manufacturing quality intelligence.

Does the AI make quality decisions?

No. AI never replaces the pharmaceutical quality decision. Deterministic computation in Databricks handles numbers, thresholds, statistics and whether a validated limit was breached. Machine learning handles anomaly detection and pattern recognition. Claude handles synthesis, explanation, document reasoning and retrieval, and it separates facts, correlations, hypotheses and recommended investigation steps. Every AI-generated draft is reviewed and approved by a QA investigator and a QA manager before it becomes part of a record.

Do we have to replace or re-validate our QMS, LIMS or MES?

No. The platform sits above the systems you already run and reads from them. It does not replace a validated system and it does not change how those systems operate. Integrations are built for enterprise platforms such as SAP and Oracle, MES and historians, Veeva Vault QMS, TrackWise and MasterControl, LabWare and LabVantage, and document stores such as SharePoint and S3.

How does it fit GxP, 21 CFR Part 11, Annex 11 and data-integrity requirements?

It is designed to support a validated environment rather than to claim compliance on its own. The architecture includes role-based access and least privilege, encryption at rest and in transit, private networking, data lineage, immutable logging and audit trails, model and prompt versioning, evidence provenance on every output, human approval, retention policies and PII and PHI controls. Validation support documentation is part of the build, and the specific frameworks are scoped with your quality and IT teams during the assessment.

How is it shaped for manufacturers in India and Canada?

Both markets manufacture for export as well as domestically, so the design covers the local expectations, CDSCO and the revised Schedule M in India and Health Canada good manufacturing practice in Canada, alongside the frameworks their US and EU customers audit against. Data residency is met by deploying in the AWS Asia Pacific Mumbai or Canada Central region, with Claude accessed through Amazon Bedrock using the region and inference profile that matches.

How do you reduce hallucination risk?

By never asking Claude to do what a query can do. Databricks computes every figure, threshold breach and comparison. Claude receives only governed evidence with provenance and is asked to explain, relate and recommend, with the assessment written as facts, correlations and hypotheses rather than conclusions. Every output carries its source records, and the model version, prompt version and evidence set are logged with each draft.

What does the audit trail record?

The AI-generated content, the user's edits, the evidence used, the model version, the prompt version, the timestamp, the approver and the final decision, for every investigation draft. That record is what makes the output defensible to an auditor.

How long does it take and how does it start?

It starts with a two to four week readiness assessment that produces a data-source inventory, a GxP and AI risk assessment, a target architecture and a roadmap. The quality intelligence foundation then takes six to ten weeks, and the investigation and CAPA modules are scoped per site. Targets are agreed against the baseline measured in the assessment.

What return should we expect?

Operational rather than abstract: evidence gathering that took hours takes minutes, related historical cases surface in seconds, SOP sections are cited immediately, recurring issues are found continuously rather than at review time, and management sees quality trends in near real time. We do not promise percentages universally. The assessment measures your current investigation and search times, and the build commits to targets against them.

Can it cover several sites?

Yes. The evidence layer is designed for site comparison and cross-site quality intelligence from the start, so an executive can ask about quality performance across sites while each site's data stays governed under the same Unity Catalog policies.

The platform is built from patterns we use elsewhere. See our Databricks Intelligence playbook, how we design enterprise AI for regulated organisations, and how we make agents production-ready.

Start with the assessment.

Two to four weeks to know which of your quality workflows the data already supports, what the compliance design needs to include, and what the first release would return. Yours to keep whether or not we build it.

Book a readiness assessment Start a conversation