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Life Sciences & Healthcare
AI for life sciences, built to pass validation
Your data is regulated, your instruments are qualified, and your quality organization has veto power over anything that touches production. We consider that a reasonable arrangement.
Since 2016, Sigmoidal has built AI systems for pharmaceutical manufacturers, diagnostics networks, and healthcare payers. For Novartis, that meant predictive analytics for drug sales; for the diagnostics division of a global pharmaceutical leader, predictive maintenance that now sets the service calendar. Anything we build inside the GxP boundary is engineered from the first design review to satisfy the people who sign the validation protocols.
Regulated environments reward engineering discipline
Most AI vendors discover GxP the week before go-live. Their pilots work; their production deployments die in change control. We staff teams that have written validation documentation, sat through supplier audits, and defended model behavior to quality assurance. The result is a shorter path to production, because nothing has to be rebuilt to pass review.
Capabilities
What we build
Six system families, engineered for an industry where every number is examined before anyone acts on it.
Predictive maintenance for laboratory instruments
An unplanned analyzer outage backs up the worklist, breaks turnaround-time commitments, and forces QC reruns. We model instrument telemetry and service histories to catch degradation before the error code, then schedule the fix inside a planned maintenance window. At a global pharmaceutical leader: 72% less unplanned analyzer downtime across the monitored fleet.
Drug sales and pharmaceutical demand forecasting
Launch curves, formulary shifts, and seasonality make drug demand a hard read; forecast error lands in production volumes, launch inventory, and territory targets. For Novartis, we built predictive analytics for drug sales: forecasting models the commercial organization could interrogate, not a black box. The results are the client's to publish; a forecast the planners do not trust changes nothing.
Clinical intelligence and knowledge systems
Every senior scientist who retires takes thirty years of study reports, regulatory correspondence, and unwritten context with them. We build retrieval and reasoning systems over your controlled documents, every answer cited to source page and document version: the traceability your quality team asks about first.
Healthcare claims fraud detection
Fraud, waste, and abuse hide in the long tail of billing codes, where static rules engines fell behind a decade ago. We score claims against provider peer groups and longitudinal patient patterns, tuned for investigator trust: a queue analysts routinely override gets ignored. We defend precision, not headline recall.
Real-world data and evidence workflows
Claims extracts, EHR exports, and registry files were never designed to agree. We build the linkage, cleaning, and documentation pipelines that turn them into submission-grade real-world evidence, every derivation recorded, with lineage a biostatistician can defend line by line.
ML operations inside the GxP boundary
A model under GxP is a controlled system: versioned, monitored, change-managed. We build MLOps where the audit trail is a byproduct of normal operation, with risk assessments and validation deliverables your computer system validation team files as-is. Retraining follows change control on a schedule the quality unit approved.
Proof
Measured in production
All three figures come from one fleet-scale program in daily use. The downtime figure is the client's own, validated in their annual service review; the others are measured by the system in production. None are pilot projections; where nothing was measured, we publish nothing. The rest of the client-measured record is on our case studies page.
Client story
A global pharmaceutical leader retires break-fix maintenance
The diagnostics division of a global pharmaceutical leader ran a large fleet of clinical chemistry and immunochemistry analyzers on a reactive service model: the instrument failed, the lab called, a field engineer was dispatched. Every failure meant halted assays, samples couriered to other sites, and QC reruns after the repair.
We embedded a team alongside their service organization, built anomaly detection on instrument telemetry, and connected predictions to the dispatch and parts systems the engineers already used. Failures that used to surface as error codes now surface as scheduled interventions.
The program delivered 72% less unplanned analyzer downtime across the monitored fleet and changed how the service organization plans its week. The case study covers how the telemetry pipeline was qualified under the client's quality system and how alert thresholds were tuned with the dispatchers.
Engagement model
Embedded teams that respect the quality system
We deploy small senior teams into your environment: an engagement lead, three to five senior engineers, and a domain specialist who has worked inside pharmaceutical quality, clinical operations, or commercial analytics. They read your SOPs before they write code, work inside your change control, and produce the validation documentation alongside the software.
Engagements start with a two-week diagnostic that scopes the system and the validation path together. There is a working system in front of users by week six; at a global investment manager, the first production release shipped in week seven. Inside a GxP boundary, when the system enters production is a change control decision, and the documentation is ready when the question is asked.
When we leave, your team can operate, retrain, and defend the system without us, and the documentation proves it.
- A delivery process that has passed client supplier audits.
- Validation and change-control documentation delivered alongside the code.
- Structured knowledge transfer, so your organization can operate and retrain the system independently.
Bring us a system that has to pass review
The first conversation is a working session with the engineers who would do the work. Bring the instrument fleet, the document corpus, the sales forecast, or the claims feed you have been circling for a year. We will tell you what is buildable, what it will cost, and what the validation path looks like.