LLM Convergence vs Jarvis

One AI model can be confidently wrong. LLM Convergence asks several at once and makes them reconcile. Pose a question and LLM Convergence sends it, in parallel, to a panel of leading AI models. A model you pick acts as the leader: it breaks every answer down into individual claims and reconciles them over a few rounds. What comes back is a single answer the panel converged on - plus a convergence score, and the specific points they disagreed on surfaced instead of buried. HOW IT WORKS - Ask in parallel - your prompt goes to several models at once, across OpenAI, Anthropic, Google, and fast open-model hosts. - Reconcile - a leader model decomposes each answer into claims and reconciles them round by round, so agreement is earned, not assumed. - See convergence - read one reconciled answer with a convergence score, and inspect the claims that didn't line up. TWO CONSENSUS MODES - Single judge - a leader model judges and reconciles each round. - Claim-commit voting - claims commit by panel quorum and stay committed (inspired by RAFT). GROUND ANSWERS IN THE WEB (OPTIONAL) Turn on grounding and LLM Convergence runs a web search before the panel answers, then puts the cited results in front of every model - so they reason over real sources instead of memory alone. Grounded answers come back with tappable [S1]-style citations and a Sources list, a grounding score next to the convergence score, and an honest "couldn't ground this" when a search turns up nothing. It uses a frontier key you already have (Claude, Gemini, or an OpenRouter model) - no separate search key, and it works on every platform. FACT-CHECK MODE Ask LLM Convergence to fact check something and it switches from answering to verifying. Paste a link and it opens the page - or check pasted text, a bare claim, or the answer it just gave you. It pulls out the checkable claims, runs a separate web search for each one, has the whole panel weigh the evidence, and returns a verdict per claim - supported, refuted, partly supported, or unverifiable - with citations you can tap. It reads how you phrased the question, so there is no mode to switch on. Fact-check mode needs a model that can search the web. BRING YOUR OWN KEYS Use your own provider API keys. They're stored securely in your device's keychain and used only to call the providers you choose - never sent to us. One key per provider: add more models from a vendor you've already set up without re-pasting its key, or override the key for a specific model. Mix and match models, run more than one model from the same vendor, and pin whichever one leads. PRIVATE BY DESIGN LLM Convergence is a pure client - there's no account and no server of ours. - No analytics, no telemetry, no ad SDKs. - Prompts go straight from your device to the providers you configured. - Chat history is saved locally and stays on your device. Delete any conversation anytime. YOU'RE IN CONTROL Choose which models sit on the panel, which one leads, how many rounds run, and the convergence threshold. Multi-turn chat with local history and Markdown-rendered answers. Run more than one conversation at a time - a chat keeps working in the background while you read another. Supported providers: OpenAI, Anthropic (Claude), Google (Gemini), and OpenAI-compatible hosts including Groq, Fireworks, Cerebras, and OpenRouter - the last reaching many models behind a single key - with free open models available. Note: LLM Convergence requires your own API keys from the providers you want to use. Provider usage is billed by those providers per their own pricing.

Achieve more with 5 AI models that provide you with 1 defensible answer. Jarvis was built for medical examiners who write opinions that get scrutinized - starting with VA Compensation & Pension examiners drafting nexus opinions on tight deadlines. A single chatbot gives you one confident answer and sometimes a fabricated citation. Jarvis runs your question through five leading AI models in parallel - GPT, Claude, Gemini, Perplexity, and Grok - then synthesizes a consensus answer with a confidence score based on how strongly the models agree. Agreement is signal. Disagreement tells you exactly what to verify before it goes in your report. FOR THE HARDEST PART OF THE JOB • Research medical-literature support for service connection across decades • Check whether current findings are consistent with in-service events, exposures, and presumptive conditions • Pressure-test your rationale: if five independent models split on a question, so might a reviewer • Sources and citations surfaced for verification against the actual literature HOW IT WORKS • Ask once - five models answer independently, in parallel • Consensus synthesis with a confidence score on every answer • Swipe through each model's full response side by side • Every question saved as an experiment - revisit, share, export BUILT FOR ANY HIGH-STAKES OPINION IMEs, peer reviews, disability and workers' comp evaluations, utilization review - anywhere "the AI said so" isn't a defense. Jarvis is a research aid. It does not provide medical advice, and all clinical judgments and final opinions remain those of the examiner. Not affiliated with the VA, VES, or any government agency. FREE TRIAL - 7 days free, then subscription. Cancel anytime in Settings. --- Jarvis Pro is an auto-renewable monthly subscription. Payment is charged to your Apple Account at confirmation of purchase. The subscription automatically renews unless canceled at least 24 hours before the end of the current period. Manage or cancel your subscription in your Apple Account settings after purchase. Terms of Use: https://nodes.bio/terms Privacy Policy: https://nodes.bio/privacy