Use Case: Cost Control

Reduce agent costs by up to 90%.

Reality Signal™ calibrates any model's confidence into a true probability with uncertainty — so you can cut LLM costs by routing simple steps to cheaper models. Two API calls. No retraining.

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Smart Routing for Agents

Add calibrated confidence to any agentic workflow in under 10 minutes.

Smart Model Rerouting

Stop paying for flagship models when a smaller model is confident enough. Reality Signal's calibration tells you which model to use per-request.

if decision.uncertainty < 0.1:
use_model("gpt-4o-mini")
else:
use_model("gpt-4o")

RealityRouterrealityrouter.dev →

Open-source proxy that plugs in between your agents and every LLM. Picks the cheapest model that will still nail each query.

# change one line — keep everything else
openai.api_base = "http://localhost:8000/v1"
# routes each call to the cheapest model that works

$curl -fsSL https://raw.githubusercontent.com/Lars-confi/RealityRouter/main/install.sh | bash

Your model says 85% confident. Is it really?

Raw confidence scores aren't probabilities. When you threshold on uncalibrated scores, you over-automate risky cases and escalate easy ones — silently burning money and your reputation on every decision.

73%

of AI teams report production incidents from overconfident models

$2–5

per multi-step agent run on flagship models — multiplied by thousands daily

Silent

model drift is invisible until customers complain or costs spike

Implementation in 5 Minutes

Reality Signal integrates with your existing agentic framework.

python

import requests

res = requests.post(
    "https://api.realitysignal.ai/decide",
    headers={"x-api-key": "YOUR_KEY"},
    json={"features": {"score": 0.82}}
)
decision = res.json()
# decision["prob_est"] -> 0.94
# decision["uncertainty"] -> 0.02

Stop guessing. Start deciding.

Get started for free. See calibrated decisions in under 5 minutes.

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