Scale ingestions for your AI pipeline
The infra to ingest and run multi-functions on every request for your unstructured and structured data loads.
The highly scalable infra of GlassFlow makes it suitable for companies who need to process hundreds of millions of events per month.
Store and reprocess all historical events when models or business logic changes.
Build rule-based functions to reach the best possible results for your RAG pipelines.
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The bottleneck
LLM request management
Coordinating large volumes of API requests while managing rate limits and costs.
Processing reliability
Ensuring consistent processing despite network issues or service disruptions.
Unscalable logics for model selection
Manually created logics that depend on results of functions before are causing wrong decisions and poor quality of responses.
The solution
In-built queue system
Ensure reliability, scalability, and fault tolerance in AI data pipelines by managing spikes, retrying failed tasks, and preventing data loss with ur in-built queue system.
Automated error handling
Built-in retry mechanisms and dead-letter queues ensure no data is lost during processing failures.
Multi-functions
Setup multiple functions based on rules to ensure that your RAG pipelines is selecting the best model for each of the events ingested.
