Levanto offers Sage, an LLM‑based decision engine designed for agentic workflows that returns structured yes/no or categorical outcomes with calibrated confidence scores in milliseconds. The model automates routing, approval, and escalation by handling high‑confidence cases automatically while flagging uncertain decisions for human review, enabling fast, cost‑effective automation in domains such as fraud detection and financial transactions.
Funding
Funding not disclosed
Founders
Product
Problem
Automation workflows often struggle to determine when a machine decision is reliable enough to act autonomously versus when human intervention is required. Conventional large language models produce verbose, uncalibrated answers and have high latency, making them unsuitable for real-time, binary decision points such as fraud checks or transaction approvals.
Solution
Levanto offers Sage, an LLM‑based decision engine that returns a calibrated yes/no or scored outcome together with a confidence level in roughly 170 ms per request. The API automatically executes high‑confidence actions—such as routing, approval, or refund—while routing low‑confidence cases to a human reviewer. By providing a concise decision plus a confidence score, Sage eliminates the need for post‑processing of free‑form text and reduces the risk of hallucinated answers. The service is priced per decision, enabling cost‑effective scaling for high‑volume use cases like fraud detection, dynamic routing, and transaction processing.
Target Audience
Primary customers are product and engineering teams building automated transaction, fraud, and routing systems, as well as operations groups in fintech, e‑commerce, and customer‑support platforms that require rapid, reliable decision making.
Features
- Binary or scored decision output with a calibrated 0–1 confidence score
- Automatic escalation to human reviewers for low‑confidence cases
- Low latency (~170 ms) suitable for real‑time agentic workflows
- Simple REST API priced at $0.01 per decision
- Structured output tags (e.g., action, risk level, policy match) for easy integration
- Designed for high‑throughput environments such as payment processing and routing pipelines