AlphaStream Labs

Generating high-fidelity 3D simulated environments using Unreal Engine and Python to train physical robot navigation systems safely before real-world deployment.

We have generated over 10,000 unique warehouse, factory, and outdoor environments. Our RL models trained entirely in simulation achieve 99.8% collision avoidance when transferred to physical robots — reducing training costs by 90% compared to real-world data collection.

An automated testing harness that evaluates frontier models against proprietary enterprise datasets to measure latency, accuracy, hallucination rates, and safety alignment.

Our benchmarking suite tests models across 12 dimensions including factual accuracy, instruction following, code generation, reasoning, and safety. We evaluate models from OpenAI, Anthropic, Google, Meta, and Mistral against real enterprise use cases in finance, healthcare, and legal domains.

Researching the use of autonomous AI agents to continuously probe web ecosystems for vulnerabilities, creating a 24/7 AI red team for defensive security.

Our framework combines traditional scanning tools with LLM-powered reasoning to identify complex vulnerability chains that automated scanners miss. In testing against known vulnerable applications, our agents identified 40% more critical vulnerabilities than commercial tools alone.

Bridging the gap between high-level reasoning LLMs and low-level C++ firmware APIs for instantaneous robotic motor control.

We are developing a middleware layer that translates natural language commands into precise motor control signals. Early experiments show promising results with robotic arms performing complex assembly tasks from text instructions with sub-millisecond latency after initial planning.

Developing techniques for training and deploying machine learning models on sensitive data without exposing private information.

Our research focuses on federated learning, differential privacy, and homomorphic encryption applied to healthcare and financial data. We are building tools that allow organizations to benefit from ML on sensitive datasets while maintaining mathematical privacy guarantees.

Researching the effectiveness and safety of AI-generated code in production enterprise environments with human-in-the-loop verification.

We are studying how AI code assistants perform in real enterprise codebases — measuring code quality, security vulnerability introduction rates, and developer productivity gains. Our findings inform how we integrate AI tooling into our own development workflows.