The constraint is not just technical

Life-sciences teams need useful software without weakening the controls around regulated work. A system may have to serve people across regions and time zones, respect product and territorial access boundaries, preserve an inspectable history of changes, and continue working when a model or downstream service is unavailable.

I start with the operating constraint and the decisions the system must support. The answer may be an AI interface, workflow automation, a governed data product, or conventional software. The least complicated intervention that can be operated and evidenced reliably is usually the right one.

Working across the GxP landscape

The relevant operating context can include:

  • GMP — Good Manufacturing Practice: controls for consistently producing and controlling medicines and, under the applicable framework, medical devices or other regulated products.
  • GCP — Good Clinical Practice: protection of trial participants and the credibility and traceability of clinical-trial data.
  • GLP — Good Laboratory Practice: quality systems for non-clinical safety studies and their records.
  • GDP — Good Distribution Practice: product integrity, traceability, and controlled handling throughout distribution.
  • GVP — Good Pharmacovigilance Practice: collection, assessment, reporting, and monitoring of safety information after authorization, including controlled training and evidence that responsibilities are understood.
  • GACP — Good Agricultural and Collection Practice: controls for plant-derived starting materials, including cultivation, collection, handling, and documentation.

Software may support one or several of these practices. Applicability must be established explicitly before defining requirements, testing, records, and release controls.

What I can help build

DATA

Governed analytics and semantic layers

Give teams consistent definitions and natural-language access to approved data while preserving row-, region-, role-, and source-level restrictions.

AI

Controlled internal AI platforms

Connect multiple AI engines behind one enterprise interface with prompt governance, guardrails, feedback, memory controls, and end-to-end traces.

WORKFLOWS

Quality and safety operations

Reduce manual handoffs in document review, evidence collection, data-quality checks, safety monitoring, and training records without hiding accountable decisions.

PLATFORM

Secure custom software

Build internal products around corporate identity, granular authorization, private infrastructure, controlled integrations, observability, and infrastructure as code.

Controls designed into the delivery

  • Traceability: link requirements, decisions, prompts, model and data versions, deployments, and test evidence so changes can be reconstructed.
  • Accountability: make ownership, approval boundaries, human review, and escalation paths visible in the product and operating process.
  • Access control: integrate enterprise identity and role or attribute rules; enforce data permissions in the backend rather than relying on the interface alone.
  • Change control: use versioned infrastructure, configuration, prompts, and release artifacts with reviewable promotion between environments.
  • Data integrity: validate inputs and outputs, expose provenance, monitor quality, and fail safely when source data is incomplete or stale.
  • Observability: trace requests across the platform and connected engines, aggregate feedback, monitor service health, and retain the records appropriate to the use case.
  • Resilience: design for regional use, long operating windows, isolated services, explicit dependency status, and recoverable failure modes.
  • Security and privacy: minimize data exposure, separate duties, control attachments and exports, and involve security and privacy owners early.

A diagnosis-first engagement

An engagement begins by mapping the business decision, regulated boundary, users, data, and evidence obligations. From there, I can help shape the smallest credible release, identify where specialist quality or regulatory review is required, and build the technical controls needed to operate it.

Discuss a regulated software or AI project

Relevant solutions

Selected work in MedTech & Life Sciences

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