“LLMs are changing the workplace” is more than just a tag line. Consider this: Categorizing 10,000 support tickets would take even your fastest employee about 55 hours (at 20 seconds per ticket). With an optimized LLM pipeline, the same task takes minutes. This isn’t incremental improvement — it’s a transformational efficiency gain that saves thousands of labor hours and dramatically accelerates response times.
As data volumes grow and AI automation expands, cost efficiency in processing with LLMs depends on both system architecture and model flexibility. An efficient batch processing system scales in a cost-effective manner to handle growing volumes of unstructured data. Being able to flexibly switch LLMs helps businesses optimize costs by right-sizing models for each use case and easily upgrading as models improve.
And to create significant technology and team efficiencies, organizations need to consider opportunities to integrate LLM pipelines with existing structured data workflows. Expanding existing investments around pipeline management, processing and orchestrations simplifies architecture and reduces operational complexity from integration and infrastructure maintenance work. This unification can also empower data engineers, who already manage structured pipelines, to easily onboard and maintain unstructured data workflows.
