AI · Life Sciences

Quality Management System for Life Sciences

Pharma manufacturers operate under strict FDA and EU regulations that require timely reporting and closure of all quality events. Any lapse — whether a delay in reporting, incomplete documentation, or failure to drive a deviation or CAPA to closure — can trigger serious regulatory action. These actions range from warning letters and halted market releases to full license revocation or shutdown of manufacturing units, given the potential impact on patient safety.

AI-enabled Quality Management — AI · Life Sciences case study cover

Role: UX Lead
Duration: 10 months
Company: Compliance Group
Domain: Life Sciences · Pharma · Regulated SaaS

Problem Statement

Quality teams were struggling with fragmented, paperwork‑heavy workflows. Audit preparation, deviation tracking, and CAPA processes lived in disconnected systems, slowing coordination and limiting visibility across the quality lifecycle. This fragmentation created blind spots, delayed risk detection, and made it difficult to ensure timely reporting and closure of quality events — ultimately increasing compliance risk and extending time‑to‑market, sometimes pushing product bring‑up to nearly five years.

Business Objective

Create the most efficient path to quality excellence by transforming deviation, audit, and risk workflows into a proactive risk‑signal layer for FDA / EU MDR‑regulated manufacturers.

Embed AI into the QMS to support quality‑event trend forecasting and reduce manual reporting overhead.

Human‑Centric Design Process

Five‑stage design process — from discovery to iterative improvement.

Discovery & Alignment

Research, market analysis, and stakeholder alignment to define the problem space, constraints, and success metrics.

User interviews

Stakeholder categories

Field visits to manufacturing floors

Deep dive into QMS workflows

User Research

Mixed‑method research across manufacturing floors and back‑office environments. Tasks were prioritised using a weighted scoring model so engineering effort aligned with regulatory risk, not the loudest opinion.

01b

Consolidation of User Interview Findings

Mapped pain points across five persona groups to reveal where severity clustered and to anchor design priorities against the customer emotion curve from pharma manufacturing to market release.

User Personas

Demographics
Working environment
Context
Pain points

Customer Emotion Curve

Surfaced where time‑to‑market actually slips and where user frustration peaks.

Ideation Phase

Conducted B2C and B2B ideation workshops with sample users, SMEs, and development teams to explore process deep‑dives.

Task prioritisation

Weighted scoring and grouping across five dimensions:

Task frequency
Severity
Regulatory risk
Cross‑team dependency
Effort vs. impact

Workshop outputs

Information architecture
Task flows

01Divergence — free thinking
02Emergence — pattern recognition
03Convergence — decision‑making

Experience Design

Insights from Discovery and Ideation informed the next layer of design artefacts.

User flows
Wireframes
Interaction patterns

Deliverables included low‑fidelity and high‑fidelity wireframes, shared through structured feedback loops with product, engineering, and domain experts to validate direction.

Prototyping & In‑Product Validation

Built core features, templates, and functional prototypes. Validated them inside the platform to assess feasibility, clarity, and workflow efficiency.

Every screen traced back to a regulated CAPA step

01Identify
02Evaluate
03Plan
04Investigate
05Develop
06Implement
07Measure
08Document

Usability Testing & Iterative Improvement

Heuristic Analysis

Designs were evaluated against focused heuristics to maintain consistency and clarity:

Visual hierarchy — guiding attention to critical elements
Consistency — adherence to the design system
Navigational clues — helping users orient within workflows
Heuristic checks — user control, error prevention, help/documentation

User Testing

Moderated, task‑based sessions with manual and screen recordings. Insights informed iterative refinement and feature expansion. Tracked parameters:

Discoverability of CTAs
Task completion time
Interpretation of nomenclature
Adaptability to new task flows

Project Summary

Client Feedback — From our FDA‑facing client: "You've hit the ball out of the park." They praised the risk‑prediction advantage achieved before their FDA compliance audit and the significant reduction in timelines, marking a standout success in proactive quality readiness.

Success Metrics — 40% overall efficiency increase. 45% reduction in test planning time. 60% reduction in summary reporting time.

Learnings — 1) In regulated domains, design must make risk visible early, not hide complexity. 2) AI is most effective when it reduces report‑writing time, not when it attempts to replace human judgment on non‑conformities.

Core Functionalities Shipped

Deviation tracking — identifies and documents process deviations, linked to corrective actions (CAR).
Audit & compliance — internal audits, documentation control, validation protocols for FDA / EU MDR.
Risk management — embedded risk assessment and CAPA management to address non‑conformities proactively.
AI‑assisted summarisation for audit reports and deviation‑summary dashboards.