About

About 8legs

The people and the company behind the multi-LLM audit platform.

What we build

8legs is a multi-LLM audit platform. You ask one question and it is sent to 8 leading AI models at the same time: OpenAI ChatGPT (GPT-5.6 Luna), Anthropic Claude (Sonnet 5), Google Gemini (3.5 Flash), xAI Grok (4.5), DeepSeek (V3.1), Meta Llama (4 Maverick), Moonshot Kimi (K2.6) and Alibaba Qwen (Qwen3 235B).

You see every answer side by side, plus a consensus summary that flags where the models agree and where they contradict each other, so you can catch a hallucination before you rely on it, instead of trusting a single AI answer.

Who we are

8legs is built and operated by UAB Gildium, a company registered in Lithuania (company code 307192563), based in Vilnius. Full legal details are in our imprint.

We're a small product team working from Vilnius. We're not an AI lab: we don't train models, we don't sell any of them, and we have no stake in which one wins. That independence is the point: 8legs only works if we have no reason to make any model look better than it is.

Why we built this

We built 8legs after being burned the same way most people have: an AI gave us a confident, fluent, completely wrong answer, and it looked exactly like all the correct ones. That's the real problem with hallucinations. They don't come with a warning label.

There's a simple, old idea for handling an unreliable source: don't ask one, ask several independent ones and compare. Different companies train these models on different data with different methods, so when eight of them independently agree, the answer deserves more confidence. And when they contradict each other, that disagreement is exactly the warning label a single answer never gives you.

Doing that by hand meant eight tabs, eight subscriptions and a lot of copy-paste. So we built the tool we wanted for ourselves: ask once, see all eight answers side by side, and get the contradictions flagged before you act on them. Agreement still isn't proof (nothing here replaces checking what matters), but it beats trusting one model on faith.