juno.

juno.

Marketing Mix Modeling · Agentic AI · LLM Evaluation

Turn Marketing Mix Model outputs into decisions you can trust — grounded, cited, and measurably evaluated.

0 benchmark cases0.0% ranking accuracy0.00 groundedness
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the copilot

Grounded reads, not guesses

Juno is an agentic copilot that interprets MMM outputs, recommends what to do next, and — uniquely — ships with an evaluation framework that quantifies whether its advice can actually be trusted.

analysis · six_channel_with_saturation
AffiliateROI 4.5x
high
SearchROI 2.9x
high
TikTokROI 1.7x
low
grounding · uncertainty

“TikTok’s ROI credible interval is wide (0.6–2.8); treat the ranking as provisional and validate with a lift test.”

01Two pieces of equal weight
The Tool01

A copilot that actually reasons about your model

A multi-agent chat system that interprets MMM outputs, generates prioritized recommendations, and answers follow-up questions — every claim grounded in the model output or a cited methodology source.

The Proof02

An evaluation framework that quantifies trust

A benchmark suite stress-tests the agent against ground-truth MMM scenarios using an LLM-as-judge, scoring accuracy, calibration, groundedness, and hallucination rate — so the advice is measurably trustworthy, not just plausible.

02Capabilities

Built like a production AI system

Not a cool demo — a system with the guardrails that separate a copilot from a confident guesser.

01

Grounded reasoning

Values come from the parsed model; methodology comes from a cited knowledge base. Nothing is invented.

02

Explicit confidence

Every interpretation and recommendation carries a high / medium / low confidence with a one-line rationale.

03

Multi-agent router

Questions are classified and dispatched to specialized handlers — interpretation, recommendation, uncertainty, and more.

04

Retrieval-augmented

Hybrid retrieval over a curated corpus of MMM methodology grounds the agent's reasoning in real sources.

05

LLM-as-judge

A stronger model grades the agent on six dimensions, validated against hand-scored references.

06

Failure-mode catalog

Low-scoring responses are logged and categorized into a growing taxonomy of where the agent breaks.

03How it works
01

Load an MMM output

Pick a pre-loaded sample or upload your own model JSON to start in seconds.

02

Read the analysis

Juno streams a structured report: overview, per-channel reads, risks, and ranked recommendations.

03

Chat about it

Ask what to do, what if, or how confident you should be — grounded, cited answers every time.

04Evaluation

Measured on six defensible dimensions

A benchmark suite generated from ground-truth MMM scenarios grades every response — so quality is a number you can point at, not a vibe.

Accuracy
> 0.00
Calibration
ECE < 0.00
Groundedness
> 0.00
Actionability
> 0.0 / 5
Failure recall
> 0.00
Hallucination
< 0.00
See the latest benchmark results →

See what your MMM is really saying

Load a sample model and start a grounded conversation in under 30 seconds.