Text analysis pipelines once required chaining specialized NLP libraries, custom training loops, and brittle regex. Large language models collapsed that stack into a single API call, but production workloads introduce a new constraint: inference cost scales with input size. For long documents, transcripts, or agentic loops that feed text back into the model, token-based billing accumulates fast. Oxlo.ai removes that variable with request-based pricing, so the cost of analyzing a 500-word product review is identical to processing a 50,000-word legal contract.
From Heuristics to Prompts
Text classification, named entity recognition, sentiment scoring, and topic extraction are now prompt engineering problems rather than model training exercises. A zero-shot prompt with clear instructions and a few examples often matches fine-tuned performance on structured domains. The shift means teams can iterate on analysis logic in hours instead of weeks.
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