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An end-to-end platform that helps engineering teams productionize ML models and integrate them into high-scale backend systems via efficient APIs and serving infrastructure.
Added May 10, 2026
8 signals
Engineering teams across AI-forward companies struggle to bridge the gap between ML research artifacts and production backend systems. Integrating models into real-time applications requires custom serving infrastructure, throughput optimization, evaluation frameworks, and tight collaboration between ML, data, and product teams — work that is currently done by hiring specialized infrastructure engineers at every company.
A turnkey platform that wraps trained models (including LLMs and multimodal models) into production-grade APIs with built-in serving, autoscaling, throughput/compute optimization, and evaluation tooling. It provides connectors and SDKs so backend engineers can embed models into real-time workflows without rebuilding ML pipelines, model training infrastructure, or LLM serving stacks from scratch.
The explosion of GenAI and multimodal models has created urgent demand at every AI-driven company for production inference infrastructure, and companies from OpenAI to Abridge to Glean are all hiring the same role — a clear signal that the underlying capability should be a product, not a headcount line.
No signals available