AI That Actually Works in the Real World
From embedded systems to multi-agent architectures, we design and deploy AI systems that operate across physical, biological, and digital domains.
Not demos. Not slides. Systems.
Multi-agent AI systems Retrieval-augmented knowledge architectures Edge + embedded AI (ESP32 → Jetson → Cloud) Simulation, digital twins, and real-world integration
👉 Work with us to build AI that does something.
⚙️ WHAT WE DO
🧩 Multi-Agent AI Systems
We design AI systems composed of specialized agents that collaborate, reason, and act.
LangChain / tool-using agents Domain-specific expert agents Orchestrated workflows across knowledge + action Autonomous research, analysis, and decision systems
Use cases:
Scientific research copilots Compliance + regulatory agents Operational AI teams
📚 Knowledge Systems (RAG + Graphs)
We build AI that knows your world.
Retrieval-Augmented Generation (RAG) Vector databases + embeddings Knowledge graphs (SPARQL, OWL, hybrid DBs) Offline-capable knowledge systems
Outcome:
AI that answers with your data, not generic internet noise.
🤖 Embedded & Edge AI
AI doesn’t belong only in the cloud.
We deploy intelligence directly into devices:
ESP32 / microcontrollers Jetson / Coral TPU Sensor-driven systems (vision, audio, environment) Real-time inference pipelines
Examples:
Robotics + drones Smart devices + IoT Autonomous sensing systems
🌌 Simulation & Digital Twins
We bring AI into simulated and physical worlds.
Physics-based simulation (MuJoCo, Simulink, NASA Trick) Digital twins of systems, environments, or organisms AI trained in simulation, deployed in reality
Applications:
Robotics training Aerospace & defense Industrial systems
🧪 AI for Science, Biology & Chemistry
We operate at molecular and metabolic scales.
Cheminformatics (RDKit, metabolic modeling) CRISPR / bioinformatics pipelines Scientific knowledge systems AI-assisted discovery
🚀 HOW WE WORK
1. Architecture First
We don’t start with models—we start with systems design.
2. Build End-to-End
From hardware to cloud, from data to UI.
3. Deploy in Reality
If it doesn’t run in the real world, it doesn’t count.
🧠 WHY SYZYGYX
We span scales most teams can’t:
Microbes → Molecules → Machines → Space systems Embedded firmware → AI agents → institutional systems Simulation → deployment → operations
Led by Dr. Daniel McShan:
NASA Orion Simulation Architect PhD Computational Bioscience Builder of multi-agent AI systems and knowledge architectures
🛠 SELECTED SYSTEMS
Multi-agent AI platform (Formul8) AI-native knowledge system (Terpedia) Autonomous faculty (Inquiry Institute) Robotics + simulation pipelines (ROS2, MuJoCo)
💼 ENGAGEMENT MODELS
Advisory — architecture, strategy, system design Build — full-stack AI systems Prototype — rapid R&D + proof-of-concept Embedded — hardware + edge AI deployments
📡 CALL TO ACTION
Have a system that needs intelligence?
Let’s build it.
👉 Contact Syzygyx
👉 Schedule a working session
👉 Send your hardest problem