Reduce wasted LLM tokens by deterministically canonicalizing and compressing prompts

Reduce wasted LLM tokens by deterministically canonicalizing and compressing prompts targets a $6.0B = 2M businesses × $3K ACV total addressable market with medium saturation and a year-over-year growth rate of 70% YoY growth in generative AI API spending and LLM adoption (industry estimates from market research reports and API provider growth signals).

Key trends driving demand: Rapid LLM adoption — more products are integrating LLMs which increases total token spend and creates direct downstream demand for cost optimization.; Larger context windows — longer contexts mean more potential token waste and bigger absolute savings from canonicalization.; Developer-first tools proliferation — standard agent frameworks create clean integration points for middleware to insert optimizations.; Cost sensitivity in startups and mid-market firms — as budgets tighten, predictable token savings shift from a "nice-to-have" to a measurable ROI requirement..