Mentlio provides an enterprise platform that monitors and optimizes AI usage to cut token waste and lower costs. Its suite includes tools like Mentlio Route, which automatically switches to the cheapest capable model and can reduce spend by up to 78% while retaining 98.3% of accuracy, as well as features for semantic code search, output compression, log collapsing, and on‑device recall of tool output.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises using large language models (LLMs) often incur high token costs due to inefficient model selection, excessive context windows, and verbose AI outputs, leading to wasted spend and slower development cycles.
Solution
Mentlio offers an AI adoption intelligence platform that optimizes token usage throughout the development workflow. Its routing engine automatically switches to the lowest‑cost model when cache‑aware savings are viable, preserving model accuracy while cutting spend. Semantic code search reduces the amount of code loaded into context, dramatically lowering token consumption. Output compression tools shorten AI responses and collapse repetitive tool logs without losing critical information. The system also stores full tool outputs on‑device for on‑demand recall, enabling developers to work with concise previews while retaining access to the complete data when needed.
Target Audience
Primary customers are software development teams and engineering organizations that integrate LLMs into their tooling pipelines and seek to control AI spend while maintaining productivity.
Features
- Mentlio Route: dynamically routes requests to the cheapest capable LLM based on real‑time cache savings, achieving up to 78% cost reduction with minimal accuracy loss
- Mentlio Lens: semantic code search that limits context to relevant snippets, reducing context tokens by up to 98%
- Mentlio Quiet: compresses AI response text while preserving exact paths, commands, and warnings, cutting output tokens by ~43%
- Mentlio Logs: collapses repetitive tool output, keeping only errors, warnings, and stack traces, achieving up to 88% token reduction
- Mentlio Recall: presents a short preview of large tool outputs and stores the full copy on‑device for instant retrieval, minimizing context size
- Integrated usage analytics that generate role‑specific training recommendations based on AI interaction patterns