Controlant: Powering the AI workforce in pharmaceutical supply chains

As pharmaceutical companies accelerate their journey toward AI- and data‑driven operations, one requirement consistently stands out: a trusted, AI‑ready foundation of high‑quality, compliant data. As the company’s CEO and CCO explain, this is where Controlant plays a critical role.

“Controlant does more than monitor shipments. We enable our customers’ AI workforce—their data scientists, operations teams, and emerging AI agents—to work faster, smarter, and with confidence in a regulated environment,” says Gísli Herjólfsson, Controlant CEO.

AI embedded into Controlant’s own platform

Controlant is inherently AI‑driven. “Our platform already combines real‑time IoT data with AI‑driven analytics, carbon footprint tracking, and GenAI‑powered recommendations to help customers reduce cost, improve quality, and accelerate decision‑making,” says Herjólfsson.

"We extend this even further with the ability to build Agentic AI solutions for our customers, as well as enabling them."

— Carsten Lützhøft, CCO at Controlant

Today, advanced analytics and geo‑spatial modeling help customers understand lane risks, packaging performance, and systemic supply‑chain weaknesses. As we move towards predictive analytics and prescriptive and agentic AI, the extent to which AI can not only predict outcomes but also recommend or trigger actions, is being determined largely by the extent to which our clients can share data. To that end, we have started combining other data sources with a new API to enable combining internal and external data sets.

Generative AI serves as a natural extension of our analytics suite, which provides customers with tailored insights and recommendations, while allowing them to set up smarter workflows.

A platform built to enable customers’ AI and machine learning

“Controlant’s solution is designed to enable our customers’ own AI initiatives,” explains Lützhøft. “We provide enriched, curated analytics data models, so customers do not need to start by cleaning, structuring, and harmonizing raw IoT and shipment data.”

Controlant has already done that heavy lifting. These curated models can be combined seamlessly with ERP, TMS, QMS, and other enterprise data—accelerating internal machine‑learning projects such as route risk prediction, demand forecasting, inventory optimization, and quality trend analysis.

Our data access patterns are intentionally AI‑ready:

• Daily refreshed analytics data delivered via Delta Sharing, designed for data lakes and ML pipelines
• Real‑time access through APIs and webhooks, feeding command centers, models, and automation services
• BI dashboards for human insight and validation alongside machine‑driven analytics

“This blend of batch, streaming, and real‑time access mirrors how modern AI teams actually work,” says Lützhøft.

An MCP server alongside our APIs creates a standardized interface for AI agents to securely connect to our data and services. This positions Controlant as infrastructure not just for analytics, but for agentic AI workloads—such as supply‑chain copilots and autonomous decision systems.

Pharma‑grade guardrails for AI innovation

“AI innovation in life sciences cannot happen without trust."

— Gísli Herjólfsson, Controlant CEO

“Controlant provides the pharma‑grade guardrails that make AI adoption viable in regulated environments,” says Herjólfsson.

Our platform is built on validated quality systems and strong governance, including ISO 9001, ISO 27001, SOC 2 Type 2, and compliance with FDA 21 CFR Part 11 and EU Annex 11. Data is securely hosted within the EU on AWS, with comprehensive audit trails, access controls, and data integrity safeguards.

Enabling the next generation of supply‑chain intelligence

By enabling its customers’ AI strategies, Controlant frees teams from data engineering, reduces operational noise, and provides a validated foundation on which human experts, machine‑learning models, and AI agents can all collaborate.

“That is Controlant’s value as an AI partner: not just using AI internally, but empowering a scalable, compliant AI workforce across the pharmaceutical supply chain,” Lützhøft concludes.