Enterprise knowledge scattered across legacy systems required a specialist team just to begin. IBM Bob made it a one-engineer job.
An AI data enablement company helping enterprises unlock knowledge from legacy applications, siloed databases, and systems built before the AI era.
Each client engagement required a 4-6 person specialist team and 6-8 weeks of discovery work before any AI initiative could begin. As demand for AI...
AI-powered data lakehouse and automation platform for IT departments and MSPs.
Enterprise technology and consulting for cloud computing, AI, and business solutions.
CrushBank's Data onboarding is part of this use case:
Related implementations across industries and use cases
Dense API docs bottlenecked non-technical users. Now, Claude builds fully configured voice agents from simple plain-text requests.
Isolating data for 50,000 clients was operationally impossible. Multi-tenancy now serves 6.1M queries from a single instance.
Fragile data pipelines bottlenecked engineers. Now, built-in workflows let teams ship internal AI tools without managing infrastructure.
Rapid code changes left documentation outdated for weeks. An AI agent now monitors every commit and auto-generates PRs to fix discrepancies.
Manual refactoring of deprecated APIs slowed platform upgrades. Using an AI partner, developers automated the uplift, saving 160+ hours.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Reaching a new market meant standing up another dubbing team. One API now dubs into 30+ languages; creators edit tracks before it goes live.
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.
Enterprise knowledge scattered across legacy systems required a specialist team just to begin. IBM Bob made it a one-engineer job.
An AI data enablement company helping enterprises unlock knowledge from legacy applications, siloed databases, and systems built before the AI era.
Each client engagement required a 4-6 person specialist team and 6-8 weeks of discovery work before any AI initiative could begin. As demand for AI...
AI-powered data lakehouse and automation platform for IT departments and MSPs.
Enterprise technology and consulting for cloud computing, AI, and business solutions.
CrushBank's Data onboarding is part of this use case:
Related implementations across industries and use cases
Dense API docs bottlenecked non-technical users. Now, Claude builds fully configured voice agents from simple plain-text requests.
Isolating data for 50,000 clients was operationally impossible. Multi-tenancy now serves 6.1M queries from a single instance.
Fragile data pipelines bottlenecked engineers. Now, built-in workflows let teams ship internal AI tools without managing infrastructure.
Rapid code changes left documentation outdated for weeks. An AI agent now monitors every commit and auto-generates PRs to fix discrepancies.
Manual refactoring of deprecated APIs slowed platform upgrades. Using an AI partner, developers automated the uplift, saving 160+ hours.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Reaching a new market meant standing up another dubbing team. One API now dubs into 30+ languages; creators edit tracks before it goes live.
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.