## Inspiration
We are living through the "Great Decoupling." While Big Tech has spent billions building massive Guessing Machines, the world is drowning in AI Slop—confident hallucinations dressed as facts and contextual drift disguised as coherence.
Scale was the obsession of the last decade; Integrity is the requirement of the next. PROOF AI was born from a single, uncomfortable realization: We don't need a better brain—we need a Filter for the Brain. Every civilization that scaled communication without a truth layer eventually collapsed under its own misinformation. The printing press needed editors; the internet needed search; Live AI needs PROOF.
## What it does
PROOF AI is a Real-Time Truth Engine that acts as a trust co-pilot for the Gemini Live ecosystem. As AI responses stream in, PROOF intercepts the buffer to run three parallel verification layers:
- 🔍 Hallucination Interception: Extracts factual claims mid-sentence and cross-references them against Google Search Grounding and "Golden Sources" (legal statutes, physical constants).
- 🧠 Contextual Drift Monitoring: Leverages Gemini’s 2M+ context window to flag when an agent contradicts earlier established facts.
- 📊 Neural Confidence Scoring: Generates a live PROOF Score (0–100), color-coding claims as Verified ✅, Uncertain ⚠️, or Flagged ❌ before the user even finishes hearing the response.
## How we built it
We engineered a Neuro-Symbolic Bridge using the following stack:
# Core Verification Logic
gemini_live = GeminiLiveAgent("gemini-3-flash-live")
claims = gemini_live.extract_claims(stream) # Neuro Layer
verdict = logic_gate.verify(claims, search_results) # Symbolic Layer
proof_score = scorer.calculate(verdict) # Trust Certificate
- Gemini Live Agent API: Handles real-time claim extraction via function calling.
- Google Search Grounding: Provides hard, non-parametric sources.
- The PROOF Algorithm: We quantify trust using a weighted formula:
$$S_{proof} = \frac{(C_{neural} \times G_{search})}{D_{drift}}$$
Where $C$ is confidence, $G$ is grounding density, and $D$ is contextual drift.
## Challenges we ran into
1. Latency vs. Veracity: Running deep verification inside a live stream is difficult. We solved this by implementing an Asynchronous Pipeline—the "Truth Layer" runs in a shadow thread so it doesn't break the conversational flow. 2. Recursive Hallucinations: Using an LLM to check an LLM creates "AI checking AI" loops. We broke this by anchoring PROOF exclusively to non-parametric sources (Google Search Index).
## Accomplishments that we're proud of
- ✅ Sub-200ms verification latency on live streaming claims.
- ✅ Neuro-Symbolic hybrid architecture that brings deterministic logic to generative AI.
- ✅ Real-time Interrupts: The ability for PROOF to signal the Live Agent to "self-correct" mid-sentence if a critical error is detected.
## What we learned
The "Human-in-the-Loop" model is a relic. It is too slow and too expensive for the age of Live AI. We learned that AI hallucination isn't a "model problem"—it's an architecture problem. Models are already powerful enough; they just lack a binding contract with the truth.
## What's next for PROOF AI
We are building the Universal Trust Standard.
- SDK Release: A drop-in middleware for any Gemini Live integration.
- Domain Specialization: Tailored grounding for Medical (FDA/PubMed) and Legal (Case Law) verification.
- The "Intel Inside" Vision: To become the mandatory verification layer for every high-stakes AI deployment.
Log in or sign up for Devpost to join the conversation.