Pharma
Applied AI for pharmaceutical and biotech operations.
For production, engineering, maintenance, and quality teams in pharma and biotech, and for the equipment suppliers and service firms that support them: AI tools that answer from your controlled documents.
Controlled knowledge, usable where the work happens.
Pharmaceutical and biotech operations run on controlled documents, qualified equipment, defined procedures, and teams that span shifts, sites, and languages. We design AI tools that connect that knowledge to the questions people face in production, engineering, maintenance, and quality, and keep each source visible for review.
Keep the controlled source behind every answer.
SOPs, work instructions, equipment manuals, specifications, change control records, and deviation reports often live in different systems and versions. A specialized knowledge assistant can help people find, compare, and summarize the relevant passages, showing which document, version, and section each answer comes from.
Connect engineering, maintenance, and production.
A question about a line stop, an alarm, a changeover, or a planned maintenance task rarely belongs to one team. A tool can bring together procedures, equipment knowledge, commissioning and qualification documents, and service history, so the next step is easier to check and discuss. Equipment suppliers and engineering service firms that support pharmaceutical plants face the same need.
Designed for a GxP environment.
GxP requirements, GMP-relevant documentation, data integrity expectations, and guidance from regulators such as the FDA or EMA shape how pharmaceutical work is done and recorded. We design tools that keep sources, versions, review points, roles, and decisions visible. They support the people involved; they do not replace the organization's own quality system or regulatory responsibilities.
The same approach for other regulated industries.
This pattern is not unique to pharmaceuticals. Medical devices, food and beverage, and chemicals also rely on controlled documents, equipment knowledge, and human review, as does any operation built around a production line. The same approach applies there.
Start where knowledge slows the work down.
A first project might focus on multilingual SOPs and manuals, support for troubleshooting and deviation investigations, comparing requirements with the technical documentation, or knowledge transfer between sites, shifts, and teams. We map the real workflow first, then choose the right level of automation.