Adopting new languages inflated project costs. Now, developers use AI trained on internal code to write and debug unfamiliar languages.
A specialized Korean IT outsourcing company builds large-scale direct-to-consumer commerce platforms and media streaming services with a small team of under 50 employees.
Expanding into new programming languages increased project costs, as training staff took considerable time and deploying highly experienced...
“GitHub Enterprise is great because it enables generative AI support that utilizes our own code base. Rather than being based on public data, it learns from our existing code repository that we've developed, which allows for more accurate and contextual code recommendations and questions.”
IT infrastructure and cloud managed services provider for enterprise systems.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Syntek Information Systems's Code generation is part of this use case:
Related implementations across industries and use cases
Feature requests once sat in backlogs. Now, developers let AI generate working preview branches in minutes for real-time customer review.
More output meant more headcount. AI coding agents shifted 700 engineers toward harder problems and cut new-hire ramp-up in half.
Legacy migrations took 12.5 man-months. Engineers now set the architecture and let AI handle bulk coding, freeing them to focus on QA.
Feature requests once sat in backlogs. Now, developers let AI generate working preview branches in minutes for real-time customer review.
More output meant more headcount. AI coding agents shifted 700 engineers toward harder problems and cut new-hire ramp-up in half.
Every new meeting question triggered days of data prep. Finance teams now ask the data lake directly and test hypotheses in minutes.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
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.
Adopting new languages inflated project costs. Now, developers use AI trained on internal code to write and debug unfamiliar languages.
A specialized Korean IT outsourcing company builds large-scale direct-to-consumer commerce platforms and media streaming services with a small team of under 50 employees.
Expanding into new programming languages increased project costs, as training staff took considerable time and deploying highly experienced...
“GitHub Enterprise is great because it enables generative AI support that utilizes our own code base. Rather than being based on public data, it learns from our existing code repository that we've developed, which allows for more accurate and contextual code recommendations and questions.”
IT infrastructure and cloud managed services provider for enterprise systems.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Syntek Information Systems's Code generation is part of this use case:
Related implementations across industries and use cases
Feature requests once sat in backlogs. Now, developers let AI generate working preview branches in minutes for real-time customer review.
More output meant more headcount. AI coding agents shifted 700 engineers toward harder problems and cut new-hire ramp-up in half.
Legacy migrations took 12.5 man-months. Engineers now set the architecture and let AI handle bulk coding, freeing them to focus on QA.
Feature requests once sat in backlogs. Now, developers let AI generate working preview branches in minutes for real-time customer review.
More output meant more headcount. AI coding agents shifted 700 engineers toward harder problems and cut new-hire ramp-up in half.
Every new meeting question triggered days of data prep. Finance teams now ask the data lake directly and test hypotheses in minutes.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
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.