Rivus — Domain AI That Compounds

Multi-model reasoning that learns from every task. Deployed in your domain, improving autonomously.

The Problem

Generic LLMs forget everything between sessions — no institutional memory, no improvement over time
Single-model blind spots — every model has systematic weaknesses that go undetected without cross-validation
No systematic quality loop — outputs degrade silently; mistakes repeat because nothing enforces learning

The Solution

Multi-model reasoning — 4–8 frontier models (Claude, GPT, Gemini, Grok) collaborate and cross-validate on every task
Self-improvement from mistakes — automated review extracts behavioral principles; each run encodes corrections for the next
Autonomous 24/7 operation — supervisor agents monitor, prioritize, and self-correct; human-in-the-loop by policy, not necessity

How It Works

Task → Multi-model reasoning → Structured output → Auto-review → Principles extracted

Next task starts with accumulated knowledge — quality compounds, not resets

Key Metrics

25K+

Learned Instances

664+

Sessions

Strategies

20+

Pipelines

Domain Examples

Finance

Earnings call analysis cross-referenced with price data at 250ms latency. Automated backtesting of analyst signals against market outcomes.

Supply Chain

500+ companies mapped with relationship graphs, bottleneck detection, and risk scoring across multi-tier supplier networks.

Intelligence

Automated dossiers on entities and individuals with TFTF scoring (Threat, Fit, Timing, Flag). Continuous monitoring and update cycles.

Generic LLM vs Rivus

	Generic LLM	Rivus
Memory	Resets each session	25K+ persistent instances
Models	Single provider	4–8 cross-validated
Quality	Static, degrades silently	Auto-review, compounds
Operation	Human-driven, session-bound	Autonomous 24/7
Improvement	Manual prompt tuning	Systematic principle extraction