Engineers rebuilt routing and access controls for every AI feature. Now, a central gateway standardizes governance portfolio-wide.
One of the world's largest business management software providers operates more than 20 software companies serving over 160,000 customers, ranging from healthcare organizations to financial institutions.
The organization wanted to embed generative models across its massive product portfolio without risking fragmented governance, uneven quality, or...
“We wanted to deliver the benefits of AI at scale. But software innovation only delivers real value when it’s built on customer trust. Governance had to enable innovation, not constrain it.”
Business management and ERP software suite for mid-market organizations.
Enterprise software, cloud infrastructure, and consumer electronics platform.
The Access Group's Model governance is part of this use case:
Related implementations across industries and use cases
Brokers were bottlenecked manually re-entering data across systems. Now, AI reads incoming paperwork and runs real-time compliance checks.
Paper recipes took months to digitize; each new regulation meant combing the library for impact. AI agents do both now—engineers review.
Users relied on disjointed AI chats, causing copy-paste errors. Now, an embedded Claude agent assembles workflows natively node-by-node.
Flying blind with agents in critical infrastructure — raw logs the only window. Now production traces feed evaluation before every release.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Engineers rebuilt routing and access controls for every AI feature. Now, a central gateway standardizes governance portfolio-wide.
One of the world's largest business management software providers operates more than 20 software companies serving over 160,000 customers, ranging from healthcare organizations to financial institutions.
The organization wanted to embed generative models across its massive product portfolio without risking fragmented governance, uneven quality, or...
“We wanted to deliver the benefits of AI at scale. But software innovation only delivers real value when it’s built on customer trust. Governance had to enable innovation, not constrain it.”
Business management and ERP software suite for mid-market organizations.
Enterprise software, cloud infrastructure, and consumer electronics platform.
The Access Group's Model governance is part of this use case:
Related implementations across industries and use cases
Brokers were bottlenecked manually re-entering data across systems. Now, AI reads incoming paperwork and runs real-time compliance checks.
Paper recipes took months to digitize; each new regulation meant combing the library for impact. AI agents do both now—engineers review.
Users relied on disjointed AI chats, causing copy-paste errors. Now, an embedded Claude agent assembles workflows natively node-by-node.
Flying blind with agents in critical infrastructure — raw logs the only window. Now production traces feed evaluation before every release.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.