We built this because we know what manual batch records actually cost
Katalyze AI came out of years working alongside pharma quality teams, watching skilled scientists spend their afternoons on documentation that machines could prepare for them.
Born out of a problem we saw repeatedly
Anand Kulkarni and Elise Korner spent years building AI data systems for regulated industries. The same pattern appeared everywhere: quality operations that depended on highly skilled people doing low-judgment paperwork. Batch record review that consumed entire afternoons. Deviation routing that happened by email, three days after the event.
They joined forces with Marcus Chen, who had spent his career in GMP manufacturing quality, to build the AI review layer that quality teams actually wanted: one that prepares, not decides. The reviewer still makes every quality judgment. The system handles the preparation.
Katalyze AI was founded in Boston in 2023. We are independently funded and building for the long term, which means we take every customer relationship seriously and talk to every early-access customer ourselves.
Three people, two domains
AI systems and GMP quality operations. Building the intersection that quality teams have needed for years.
Built AI data systems for regulated-industry document workflows across multiple industry contexts before focusing on pharma quality operations. Spent years developing document understanding systems where audit trail completeness and attribution are legal requirements, not preferences. Brings the technical and operational experience of deploying AI in compliance-sensitive environments to Katalyze AI's product architecture.
Built data pipeline and compliance-system backends for regulated software contexts with deep experience in audit-trail architectures and data integrity requirements. Approaches pharma quality data as a systems design problem: what gets captured, how it gets attributed, and how it needs to look when an inspector asks. Leads all technical architecture and infrastructure decisions at Katalyze AI.
Spent years in GMP documentation, deviation management, and validation across solid-dose and biologics manufacturing environments. Understands how quality work is actually done on the floor: what the forms look like, where the friction is, and which exception patterns repeat across sites and product types. The team's connection between AI system design and the operational reality of pharmaceutical quality work.
Three principles we do not bend on
The reviewer decides, we prepare
AI surfaces exceptions. Humans make quality decisions. Every batch release, every deviation closure, every quality sign-off stays with the qualified person. We prepare the materials; we do not make the call.
Evidence before claims
Every metric we publish comes from actual pilot data, not projected estimates. When we say customers cut batch review time by a specific number, that number came from a real manufacturing site, not a model.
One fewer hour of paperwork per day adds up
No grand vision statements. The value is compounding: one less hour of review preparation per day, per team member, over a year is a material difference in how a quality team can spend its time.
We are a small team and we talk to every customer ourselves
If your quality system is struggling with batch records or deviations, reach out directly. We will tell you honestly whether Katalyze AI is the right fit for your operation.