Rivus

Problem

Enterprises spend millions on LLM deployments. They get inconsistent outputs, models that forget everything between sessions, and zero systematic improvement. Every day starts from zero.

Solution

A domain reasoning engine. Multiple frontier models reason in parallel. The system reviews its own sessions, extracts principles from mistakes, and feeds them back into future work. 20+ autonomous pipelines run 24/7 with self-healing. Quality compounds — every task makes the system better at your domain.

How It Works

• 1 Multi-model reasoning — 4-8 frontier models (Claude, GPT, Gemini, Grok) run in parallel across 19 strategies built from 10 composable stages and 9 analytical lenses. The system picks the right approach for each problem.
• 2 Self-improvement — Every session is reviewed. Mistakes become encoded principles. 25K+ learned instances feed back into reasoning. The same mistake does not happen twice.
• 3 Autonomous operation — 20+ self-healing pipelines and 40+ expert workflows run continuously. When something breaks, the system diagnoses and fixes itself.

Traction

Verticals: Financial analysis (earnings x price at 250ms), supply chain (500+ semiconductor companies), entity intel (automated dossiers). System is self-improving in production.

Why Now

Frontier models are commoditizing — GPT, Claude, Gemini, Grok converging in capability and racing to zero on price. The value layer is shifting from raw models to reasoning orchestration and domain knowledge accumulation. That layer does not exist yet.

Big Vision