Regulated-first digital delivery
Fraser Analytics builds clean, validated-by-design systems for regulated environments, and elegant web experiences for teams that need reliability, auditability, and speed.
Build or improve systems used in production environments with a quality mindset: roles & permissions, audit trails, data integrity, SOP-aligned workflows, and documentation that stands up to scrutiny.
Fast, accessible, and brand-consistent websites and web apps with performance, security, and maintainability built in.
AI that supports operators, trends quality data, and reduces administrative load — designed with controls, traceability, and “human-in-the-loop” governance.
Software for production is different: controls, evidence, and repeatability matter.
I design features so they’re easy to specify, test, and defend: clear roles, controlled configuration, robust logging, and predictable behaviour.
Interface and workflow choices that respect the reality of controlled areas: quick interactions, clear status, minimal typing, and mobile-friendly layouts.
Useful intelligence without turning your quality system into a black box.
Operator / process trending, defect categorisation, and leading indicators to support coaching, CAPA prioritisation, and continuous improvement.
Best for: fresh-to-maturing datasets, Excel-first environments.
Draft summaries, create structured records, and answer “where is it / what changed” questions against approved procedures and controlled documents.
Best for: SOP navigation, governance packs, training support.
Sensible model boundaries, change control, prompt/data logging, and validation strategy planning so AI becomes auditable and manageable.
Best for: regulated teams exploring first AI deployments.
If AI influences decisions in GMP, we design for transparency: versioning, monitoring, approval thresholds, and “human-in-the-loop” controls.
Agile delivery with regulated discipline: ship value while maintaining evidence.
Goals, constraints, data reality, stakeholders, and what “good” looks like.
Lightweight URS/FDS, threat model, validation impact, and acceptance criteria.
Small releases, test coverage, auditability, and feedback from real users.
Docs, training, support model, and change control to keep it compliant.
Tell me what you’re building, where it will run, and what success looks like.