A 300B model, responding in 100ms
The AI model for agents
Make your software decide, classify, rank, route, and estimate, with frontier-model intelligence, at light speed, and a calibrated confidence score you can branch on.
Real responses recorded from the Sage API. Every scenario is a preset you can run live in the console.
The problem
LLMs are built for humans. Your agent needs structured outputs, low latency, and confidence.
Machines need decisions, not paragraphs
Chat models return prose to parse and second-guess. Automation needs a structured answer it can branch on.
The hot path has a latency budget
A judgment call inside an agent loop or a checkout can't wait two seconds for a frontier model to stream an essay.
Overconfidence is the failure mode
LLMs sound just as sure when they're wrong. Without a calibrated signal, you trust everything or nothing.
The missing primitive
LLM intelligence at classifier speed, with a confidence score.
Sage lets machines choose, act, or escalate to a human in 60–200ms, always with calibrated confidence. The missing primitive for agentic workflows: fast enough for the hot path, smart enough to read a system prompt, honest enough to say “I don’t know.”
How it works
Content → Decision + Confidence
Sage is an agentic model we built and train ourselves: it answers closed-ended questions instead of generating text. Send content and a question, get a typed answer with calibrated confidence in one round-trip.
- 1
Send content + a question
Any text or list, plus a closed-ended question. Your policy goes in the instructions; Sage reads all of it.
- 2
Get a typed decision
Not prose: an answer field your code can switch on, plus a calibrated 0–1 confidence score.
- 3
Route on confidence
High confidence: act. Low confidence: escalate to a human. That one branch makes automation safe to ship.
# pip install levanto
from levanto import LevantoClient, YesNo
client = LevantoClient(api_key="lv_live_...")
env = client.decide(document, YesNo("Needs compliance review?"))
if env["result"]["confidence"] < 0.8:
escalate_to_human(document) # not sure? a human decides
elif env["result"]["answer"] == "yes":
send_to_compliance(document)Agent-native install
Don’t integrate it. Let your agent do it.
Paste one prompt into Cursor or Claude Code. Your agent installs Sage, then reads your repo and proposes where an agentic model would replace brittle rules or a slow LLM call.
- 1
Paste the prompt
- 2
The agent installs the skill
- 3
It finds where Sage fits
levanto-sage-decide.SKILL.md. Drop it in .cursor/skills/ or .claude/skills/
You now have access to Levanto Sage, an agentic-model HTTP API that turns content + a question into a structured, machine-actionable decision with a calibrated confidence score (Yes/No, Choice, Scale, Sort, Tags).
1. Install the skill so you know the full API contract:
mkdir -p .cursor/skills && curl -fsSL "https://platform.levanto.ai/api/intelligence/skill" -o .cursor/skills/levanto-sage-decide.SKILL.md
(If you use Claude, put it in .claude/skills/ instead.)
2. Set my Levanto API key as the SAGE_API_KEY environment variable. If I don't have one yet, pause and tell me to mint one at https://platform.levanto.ai (plans start at $14/mo for 5,000 decisions).
3. The endpoint is https://sage.levanto.ai. POST /decide with the header `Authorization: Bearer $SAGE_API_KEY`.
4. As a first call, make this decision:
{
"content": "Marketing email draft promotes \"guaranteed 40% returns\" and describes the product as \"risk-free\" for accredited investors.",
"question": { "id": "needs_review", "kind": "yesno", "instructions": "Does this copy require compliance review before send?" }
}
Read the skill for the response shape. Then explore this repository: find places where we already make judgment calls with brittle rules, heuristics, or a slow LLM call (moderation, triage, routing, risk, tool gating, ranking, etc.). Propose 2–3 concrete insertion points, pick the strongest one with me, wire it through /decide, and branch on result.confidence: act when it clears a threshold, escalate to a human when it doesn't.Every closed-ended question your product asks
One API, five answer shapes. Each returns calibrated confidence.
The hot path
We asked three models the same question.
“May support auto-fulfill an export request today by email, without Owner approval or a DSAR?”, replayed at the speed it actually happened. Logs
Recorded run, 2026-07-23.
Run it yourself · start freeUse cases
Where teams point Sage
Judgment calls that usually run on brittle rules, slow LLM round-trips, or human review.
Agent workflows
Gate tool calls, route between models, and escalate when confidence is low: one fast call inside the agent loop.
Ops & support triage
Route tickets, score urgency, and auto-approve the clear cases; everything else escalates to a human.
Moderation & content screening
Tag spam, scams, and policy violations at feed speed, with your community's actual rules as the policy.
Risk & fraud
Judge transactions and account activity inline, so clear cases flow and only the suspicious ones stop.
Data pipelines
Tag, score, sort, and screen records in batch: LLM judgment with a confidence column, no classifier training.
Levanto Labs
We make AI safe for humans.
“Levanto” is Spanish for “I rise”: the warm light of a new dawn where humans, agents and nature prosper together.
We are hiring.