Governed Data Foundation and Validated MLOps for Life Sciences

The company wanted to apply AI and analytics across its operations, and every initiative ran into the same wall. Its core systems were decades-old LIMS, ERP, and document platforms that did not interoperate cleanly. Data quality was inconsistent across them. And because this is a regulated environment, anything touching that data had to satisfy strict requirements: data integrity under ALCOA+ principles, electronic records and signatures under 21 CFR Part 11, GxP expectations, and validation under frameworks like GAMP 5.

The team had been treating validation as paperwork bolted on at the end of each project, which made every initiative slow, fragile, and expensive, and which meant the data foundation itself was never built to be inspection-ready. The result was that promising AI and analytics work kept stalling, not on the models, but on the data and the audit trail.

The purpose of the engagement was to build a governed data foundation with data integrity and Part 11-ready controls designed in from the start, integrating the legacy systems into something trustworthy and auditable, and then to add validated MLOps on top so models could be deployed with the provenance and documentation inspections require. The guiding principle was compliance by design: build the controls into the architecture rather than bolting validation on afterward.

Project duration

The engagement ran in three fixed-price stages over approximately six months.

Deliverables

• A governed data lakehouse integrating legacy LIMS, ERP, and eTMF data
• Full data lineage and audit trails supporting data-integrity requirements
• 21 CFR Part 11-ready controls (access, electronic records, traceability)
• A semantic layer with governed, consistent definitions
• Validated MLOps aligned to GAMP 5 and computer system validation practices
• Encryption, strong identity, and least-privilege access throughout

The pattern was consistent: the AI or analytics project did not fail on the model, it failed on the data and the audit trail. Integrating decades-old LIMS, ERP, and document systems that were never designed to interoperate was the hardest technical problem, and on top of it sat the regulatory burden. Data integrity had to be demonstrable, every record traceable to its source, with a complete history of who did what and when. AI and computer systems had to be validated. And inspectors could ask, at any time, for the provenance of any data point or decision.

When validation is treated as paperwork added at the end, none of this holds together. The data foundation itself has to be built for integrity and traceability, or every project on top of it inherits the same fragility. The company needed the foundation rebuilt with compliance in its bones.

Our solution architect designed a staged roadmap that built a governed, inspection-ready data foundation first, then added validated AI on top, with validation treated as architecture throughout.

Stage one, integrate and govern with integrity. We built a governed lakehouse and integrated the legacy LIMS, ERP, and eTMF data into it through managed connectors and APIs, modeling and reconciling it with dbt so it was consistent and trustworthy. Crucially, we built in full lineage and audit trails from the start, so every data point could be traced to its source with a complete history, supporting ALCOA+ data-integrity requirements. Access was governed with role-based and attribute-based controls, and the design was built to be 21 CFR Part 11-ready.

Stage two, the semantic and compliance layer. We added a semantic layer so governed, consistent definitions applied everywhere, and hardened the Part 11-ready controls around electronic records, access, and traceability, with encryption at rest and in transit and strong identity through IAM and MFA. By the end of this stage, the company had a data foundation that was both usable and inspection-ready.

Stage three, validated MLOps. On top of the governed foundation, we implemented MLOps aligned to GAMP 5 and computer system validation practices: model versioning, validation, documentation, and monitoring, so models could be deployed with the provenance and audit trail inspections require. Validation was built into the pipeline through CI/CD gates, not performed as an afterthought, which is what shortened the path from idea to inspection-ready model.

We provisioned a governed lakehouse on AWS or Azure through Terraform infrastructure as code, integrated the legacy LIMS, ERP, and eTMF systems through managed connectors and APIs, and used dbt to reconcile and model the data into governed, tested tables. Full data lineage and audit trails were built in through a data catalog, supporting the traceability that data integrity demands. A semantic layer ensured consistent definitions, and access was controlled through role-based and attribute-based policies, with encryption throughout and secrets in HashiCorp Vault. The CI/CD pipeline in GitHub Actions included validation gates appropriate to a regulated environment.

On top of the foundation, we built validated MLOps using MLflow with model versioning, a feature store, validation steps, documentation, and monitoring, aligned to GAMP 5 and computer system validation expectations. The result was that models could be developed, validated, and deployed with the provenance and audit trail that inspections require, rather than being held back because the data and validation could not support them.

• Compliance by design, not bolt-on validation. Building integrity, lineage, and Part 11-ready controls into the foundation is what made everything on top inspection-ready, instead of fragile.
• Lineage and audit trails from day one. Traceability was architected in, so data integrity could be demonstrated rather than reconstructed under pressure.
• Validation as architecture. MLOps was aligned to GAMP 5 and CSV practices with validation in the pipeline, which shortened the path to deployable, inspection-ready models.
• Integrate the legacy systems properly. The hardest part, making decades-old LIMS and ERP interoperate, was done at the data layer rather than worked around.

Engagements of this type target the following outcomes:

The engagement achieved its goals:

1. The legacy LIMS, ERP, and eTMF data is integrated into a governed, auditable foundation with full lineage.
2. The platform is built to be 21 CFR Part 11-ready, with data integrity demonstrable rather than reconstructed.
3. AI and analytics can move forward, because the data and audit trail finally support them.
4. Validated MLOps shortens the path from idea to inspection-ready model, with validation built in rather than bolted on.