Solving the $100B
Black Box Problem
The first explainable AI trading engine built from scratch in Rust. Training on millions of market scenarios in seconds, not weeks.
Hedge funds pay millions for black-box AI that they can't audit. We're building transparent, institutional-grade intelligence for the next generation of quantitative trading.
The Black Box Crisis
Hedge funds and prop desks deploy AI models worth millions, but can't explain why they make decisions.
Current State
- ✗Neural Networks = Black BoxesRegulators and LPs demand explainability. "Why did you lose $10M?" has no answer.
- ✗Weeks to RetrainMarket crashes in hours. Your model needs days on GPUs to adapt. Too late.
- ✗Catastrophic ForgettingTraining on new regimes erases knowledge of old ones. 2020 crash patterns? Gone.
Our Solution: VBRL
- ✓Glass-Box IntelligenceEvery decision backed by 50+ historical analogues. Full audit trail for compliance.
- ✓Instant Training3M scenarios loaded in seconds. Not days. Not GPUs. Just pure Rust speed.
- ✓Perfect MemoryNever forgets. 2020 crash, 2017 bull run, 2022 bear—all instantly accessible.
$100B+ Market Opportunity
Algorithmic trading is exploding, but AI trust is collapsing. We're at the intersection.
Target Market Verticals
Our Unfair Advantage
Built from scratch in Rust. Not a Python wrapper. Not TensorFlow. A completely new approach.
Traditional AI Trading
AIPP (Our System)
VBRL: The Core Innovation
Vector-Based Reinforcement Learning. Instead of training neural networks, we instantly index 3M+ historical market scenarios and find exact matches in <1ms.
(vs weeks for ML)
(never forgets)
(full transparency)
Proven Performance
Backtested on 5+ years of crypto and forex data
Live Infrastructure Proof
Direct telemetry from our production Rust clusters. Not mockups—real signals and autonomous decisions.
* All data retrieved direct from production search clusters.
Access Full Interface →Loading live performance data...
Revenue Model
SaaS + Enterprise licensing for institutional clients
- ✓ API access
- ✓ 100 patterns/day
- ✓ Email support
- ✓ Unlimited API
- ✓ VBRL Agent access
- ✓ Priority support
- ✓ On-premise deployment
- ✓ Private data indexing
- ✓ Dedicated support
Additional Revenue Streams:
From Problem to Innovation
A 5-year journey solving the fundamental issues of AI in trading
Real-World Failure: Hedge Fund Collaboration
Collaborated with hedge fund to implement swarm of RL agents for trading. Tried distributed learning, browser-based training. Agents constantly got confused—couldn't handle real market complexity.
- • Swarm agents interfered with each other
- • Black box decisions—no audit trail
- • Constant overfitting to recent data
2021
The Breakthrough: Memory Problem
Realized the core issue: Agent couldn't remember which patterns to trade. Attempted to add memory, but agent couldn't distinguish between market scenarios.
2023
Building the Foundation
Built custom vector database engine for pattern similarity search. Created browser-based UI + backtesting framework to validate pattern matching accuracy.
- HNSW vector search from scratch in Rust
- Interactive chart UI for pattern validation
- Comprehensive backtesting infrastructure
Now
VBRL: The Hybrid Innovation
With proven pattern matching, created VBRL (Vector-Based Reinforcement Learning) agent. A completely new hybrid approach: RL decision-making powered by vector memory.
5+ years solving the fundamental problems of AI in trading.
From distributed RL experiments to production-grade VBRL infrastructure.
Join Us in Building the Future
We're revolutionizing how hedge funds and prop desks use AI. Transparent, fast, and auditable.
Currently in conversations with Angels Partners and select institutional investors.
Explore the Platform
Technology Deep Dive
Explore the VBRL architecture, Rust engine, and how our AI achieves explainable predictions.
Institutional Solutions
Learn how hedge funds and prop desks integrate our pattern recognition into their workflows.
Technical Whitepaper
Full technical documentation of the VBRL framework and backtested results.