SIE
An inference engine introduced by Superlinked that supports deploying specialized small models in place of expensive frontier LLM calls, suited to high-frequency, low-variance document processing tasks, and able to reduce costs.
Timeline (last 90 days)
Superlinked's SIE engine supports two deployment modes for the same engine: public filings can be placed on SIE Cloud with pay-per-usage, while customer and internal documents can be self-hosted on your own GPUs, with both sides sharing the same SDK and model IDs.
SIE offers Apache 2.0 open-source weights, with one API covering OCR, extraction, embedding, and reranking.
The SIE self-hosted version is Apache 2.0 licensed, runs in the user's own VPC, is deployed to AWS, GCP, or Azure via Terraform and Helm, and achieves scale-to-zero between tasks on Kubernetes via KEDA.
The key practice of a hybrid architecture is: public filings go through managed inference, customer and internal documents go through self-hosted inference, a routing layer assigns paths by document category, and the two paths use the same models, model IDs, and client code.
Superlinked SIE is an inference layer for task-specific models with three operating modes: SIE Cloud managed (pay-per-use), self-hosted on VPC or Kubernetes (Apache 2.0), and local development.
In financial document processing, the highest-frequency tasks often have the smallest output space, and this is exactly the work handled by specialized models on SIE.
SIE (Superlinked Inference Engine) is an inference engine launched by Superlinked that supports simultaneous deployment of multiple models on a single cluster (multi-model co-location).
All Facts (7)
Superlinked's SIE engine supports two deployment modes for the same engine: public filings can be placed on SIE Cloud with pay-per-usage, while customer and internal documents can be self-hosted on your own GPUs, with both sides sharing the same SDK and model IDs.
50%UnverifiedSIE offers Apache 2.0 open-source weights, with one API covering OCR, extraction, embedding, and reranking.
50%UnverifiedThe SIE self-hosted version is Apache 2.0 licensed, runs in the user's own VPC, is deployed to AWS, GCP, or Azure via Terraform and Helm, and achieves scale-to-zero between tasks on Kubernetes via KEDA.
50%UnverifiedThe key practice of a hybrid architecture is: public filings go through managed inference, customer and internal documents go through self-hosted inference, a routing layer assigns paths by document category, and the two paths use the same models, model IDs, and client code.
50%UnverifiedSuperlinked SIE is an inference layer for task-specific models with three operating modes: SIE Cloud managed (pay-per-use), self-hosted on VPC or Kubernetes (Apache 2.0), and local development.
50%UnverifiedIn financial document processing, the highest-frequency tasks often have the smallest output space, and this is exactly the work handled by specialized models on SIE.
50%UnverifiedSIE (Superlinked Inference Engine) is an inference engine launched by Superlinked that supports simultaneous deployment of multiple models on a single cluster (multi-model co-location).
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