The cloud AI engine
ShimmerCat’s cloud AI engine is the part where we analyze the data to create optimization rules, and it is mainly done via Google Cloud and AWS.
Data about website visitor behavior is collected and sent to the AI engine where it is analyzed and optimised for performance. This provides an automatic way of going beyond handmade conventional optimizations, and also makes it possible to learn, predict, and act pro-actively on web-traffic.
What kind of data ShimmerCat sends to the service for analysis?
HTTP headers and other metadata (timestamp, TCP details, TLS and protocol fingerprints, etc).
What is meant with the “data pipelines”?
The pipelines are the flow from where the SaaS solution collects data, analyzes it, and uses the results to update the optimization rules in ShimmerCat. Specifically the bots pipeline deals with the bot data, for example whether to block or allow a bot, and specifically the image pipeline deals with identifying images, optimizing them, and sending them to the edges.
What kind of optimization ShimmerCat delivers?
At the moment we automatically build rules for things like HTTP/2 Push and resource preloading, rules for redirecting automatic traffic, above-the-fold image prioritization data, and next-gen optimized image formats.