Levanto Labs
Levanto Labs builds models and infrastructure for the agent economy, with the stated goal of making AI safe for humans. Its first product, Sage, is a decision model that lets software ask questions at runtime and receive structured, machine-actionable decisions with calibrated confidence and a null when it is unsure.
At a Glance
- Developers building AI agents and agentic workflows
- Operations, DevOps, SecOps and customer-support teams
- Companies needing moderation, compliance or tool-call guardrails
- Risk, fraud, finance and trading applications
- +1 more
AI Tools by Levanto Labs
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Levanto
Runtime Semantic Decision Model
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Latest News
SageCraft published as an open-source harness that lets Sage play World of Warcraft and reach level five.
Levanto published the GrowBlocks case study showing Sage selecting monster actions and dungeon encounters at runtime.
Levanto published Sage v1.1 benchmark results, including JevBench and multistep_decisions accuracy.
Sage v0.8 introduced 128K context windows, batching and subscriptions.
Products & Services
A closed-ended decision/inference API for agent and application workflows. It accepts content plus a typed question and returns Yes/No, Choice, Scale, Sort or Tags results, probabilities/confidence, and null for uncertainty on supported decision types. It supports batching, reasoning, image inputs and optional web-search grounding.
An open-source Apache-2.0 harness that lets Sage play World of Warcraft from screenshots, choosing actions through visual, structured decisions.
A beta integration that exposes Sage decision types to GrowBlocks games, allowing developers to provide game state, instructions and legal choices while the platform manages Sage access, rate limits and per-game allowances.
Market Position
Levanto positions Sage as a decision layer between brittle rules/small classifiers and slow, prose-oriented frontier LLMs: structured outputs, confidence and human handoff at classifier-like latency. Its published comparisons cover Jev/TypeSafe, OpenAI GPT-5, Google Model Armor, OpenRouter Auto and other guardrail/model-routing systems. Levanto reports 93.9% on JevBench v1.2 and 98.2% AIME quality in its model-router test, while noting that benchmark methods and scopes differ.
Leadership
Founders
Marco De Rossi
CEO of Levanto Labs; an Italian serial founder who pioneered EdTech and school innovation, founded Oilproject and later WeSchool, and subsequently worked as an AI Lead at MetaMask. His public profile also identifies him as an ERC-8004 author and says he has had two exits.
Christopher Kocurek
Co-founder and CGO of Levanto Labs. Public profiles describe him as a product-led growth and marketing executive behind Web3 enterprise developer tooling with millions of users and billions in traded volume; he previously founded and served as CEO of Chainscape Games.
Executive Team
Marco De Rossi
Chief Executive Officer
Serial founder and former EdTech entrepreneur behind Oilproject and WeSchool; AI Lead at MetaMask and ERC-8004 author.
Christopher Kocurek
Chief Growth Officer
Product-led marketing and growth executive with Web3 enterprise developer-tooling experience; founder and former CEO of Chainscape Games.
Founding Story
Levanto Labs was founded in July 2026 by Marco De Rossi and Christopher Kocurek. They came out of stealth with the view that mass adoption of AI agents requires reliability and that software needs models built for machines, not human-oriented chatbots. Their initial product direction was agent cybersecurity; they launched Sage as a decision layer that can make fast, typed decisions and explicitly hand uncertain cases to a human.
Business Model
Revenue Model
Monthly subscriptions with included usage, metered by input tokens, output tokens and optional web-search grounding. There are no overage invoices; calls return HTTP 402 when included usage is exhausted until the next billing period or an upgrade. Custom higher-volume plans are available by contacting the company.
Pricing Tiers
$0.20 usage included; input $0.05 per 1M tokens.
$14 usage included; input $0.05 per 1M tokens.
$49 usage included; input $0.046 per 1M tokens.
$99 usage included; input $0.042 per 1M tokens; priority support.
$249 usage included; input $0.038 per 1M tokens; 128K context and priority support.
Target Markets
- Developers building AI agents and agentic workflows
- Operations, DevOps, SecOps and customer-support teams
- Companies needing moderation, compliance or tool-call guardrails
- Risk, fraud, finance and trading applications
- LLM-native game developers and game platforms
- Agent workflow routing, branching and escalation
- Tool-call guardrails and agent security
- DevOps, SecOps and support triage
- Content moderation, tagging, scoring and ranking
- Risk, fraud, refund and trading decisions
- Model routing
- GrowBlocks / Rogblox
- Tripcite