Their engineers were propping up overprovisioned LLM clusters instead of shipping. Bedrock took the upkeep; the team got back to building.
A research services and AI products company with 4,000+ experts, developing writing, discovery, and editorial tools for researchers, publishers, and life science organizations across eight global offices.
Building LLM-powered products in-house required running training and inference clusters that needed significant overprovisioning to maintain...
“With LLMs advancing worldwide, we recognized a clear opportunity to concentrate on building generative AI services that meaningfully enhanced our portfolio.”
Scientific communication and technology services for researchers and life sciences.
Cloud computing platform and on-demand infrastructure services.
Cactus Communications's Product development is part of this use case:
Related implementations across industries and use cases
Experts manually sifted 100k cases. Now, AI parses literature and highlights source text, speeding up time-sensitive diagnoses.
Manually classifying 10M media signals took 12 weeks. Now, AI agents automatically extract and match unstructured metadata to entities.
Legacy migrations took 12.5 man-months. Engineers now set the architecture and let AI handle bulk coding, freeing them to focus on QA.
Every GPU was on a live call. New model benchmarks waited months. AWS gave them a separate lane—research and production run in parallel.
Privacy rules bottlenecked AI scaling. A secure internal platform now cuts AML investigation time by days and security resolution by 50%.
The CPO sourced every candidate by hand, plus a full team. AI now builds targeted pipelines in 24 hours—she runs it solo.
Asset ledgers across three tools were unreachable by the AI helpdesk. One migration consolidated them — now employees self-serve through AI.
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.
Their engineers were propping up overprovisioned LLM clusters instead of shipping. Bedrock took the upkeep; the team got back to building.
A research services and AI products company with 4,000+ experts, developing writing, discovery, and editorial tools for researchers, publishers, and life science organizations across eight global offices.
Building LLM-powered products in-house required running training and inference clusters that needed significant overprovisioning to maintain...
“With LLMs advancing worldwide, we recognized a clear opportunity to concentrate on building generative AI services that meaningfully enhanced our portfolio.”
Scientific communication and technology services for researchers and life sciences.
Cloud computing platform and on-demand infrastructure services.
Cactus Communications's Product development is part of this use case:
Related implementations across industries and use cases
Experts manually sifted 100k cases. Now, AI parses literature and highlights source text, speeding up time-sensitive diagnoses.
Manually classifying 10M media signals took 12 weeks. Now, AI agents automatically extract and match unstructured metadata to entities.
Legacy migrations took 12.5 man-months. Engineers now set the architecture and let AI handle bulk coding, freeing them to focus on QA.
Every GPU was on a live call. New model benchmarks waited months. AWS gave them a separate lane—research and production run in parallel.
Privacy rules bottlenecked AI scaling. A secure internal platform now cuts AML investigation time by days and security resolution by 50%.
The CPO sourced every candidate by hand, plus a full team. AI now builds targeted pipelines in 24 hours—she runs it solo.
Asset ledgers across three tools were unreachable by the AI helpdesk. One migration consolidated them — now employees self-serve through AI.
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.