Validation happens inside the network — not after it. Every model generates and grades simultaneously. The highest confidence wins.
P models receive the same prompt simultaneously — each reasoning independently, in parallel, with no shared state and no anchoring bias between them.
Validation happens inside the network itself. Every model scores every response — including its own — concurrently. P² confidence scores, all at once.
Confidence scores accumulate per response across all concurrent graders. A hallucinated answer cannot survive peer review from 4 independent judges.
arg max Σ — the response with the highest total confidence is returned. Not voted on. Not post-processed. Proven inside the network.
Every stage of building with AI — in a single unified platform.
20+ frontier models, multi-model presets, and composable pipelines — configured in minutes, not months.
Deep Fusion routes your prompt to multiple models simultaneously. Every model grades every response — concurrently, inside the network — and the highest-confidence answer is returned.
A single unified endpoint. Swap models, add RAG, tools, and memory without touching your integration.
Platform
OpenAI-compatible API. Change one URL. Keep every SDK, tool, and integration you already have.
Process entire codebases and documents without accuracy degradation.
Streaming responses begin in under a second across all model tiers.
Enterprise-grade reliability with redundant infrastructure globally.
The Balon Difference
Multiple models think in parallel, grade each other, converge on truth. Not one opinion — a consensus.
Semantic slicing reduces what you pay without reducing what you get. Million-token contexts at a fraction of the cost.
Your data doesn't leave. No third-party agreements, no data sales. Encrypted end-to-end, always.
more accurate than single-model responses
In production
Identified contextual errors in 35-page USPTO submissions and rewrote sections in accordance with case law — without external data access.
Generated comprehensive renal surgery guidelines at senior clinical staff quality — including prep protocols, techniques, and probabilistic efficacy analyses.
From law firms to venture capital, Atlanta to Silicon Valley.
"We've actually made something real, something that works. We don't have to trade on theory or possibility, because we've proven it."
"You guys are solving real issues, man. This is really cool. You're making major progress, congratulations."
"You have made something that everyone needs. If they're using AI, they need this, period."
"You guys have to patent this. You've made something significant, something of substance. Now you have to protect it. This is gonna be huge."
"You guys are crazy, you know that right? It's not enough to already fight hallucination, but you're making models too?"
"You guys are absolutely doing an amazing job at making LLM hallucinations free. Which is just crazy to think about."
"Stay ambitious AND insane! As a tech innovator they go hand in hand!"
"Looking forward to seeing you guys scale. To Balon AI Growth!!"
"This is impressive. It's fast, way faster than I thought it was going to be; I could benchmark this against individual models."
"Most AI startups are building on top of models. These guys are trying to replace them. Instead of trusting one model and its flaws, they're creating a system that cross-verifies outputs and makes AI actually reliable at scale."
"Atlanta has two founders I'd bet on right now. They're working on deep technical problems that most NYC investors haven't even heard of yet."