Our Active Projects
From protocols to runtimes to trained models — these are the projects we build and use in production.
XCT — Execution Control Transfer
An architectural protocol that keeps language models from granting themselves executive authority. XCT inverts the sovereignty model: models propose, but don't execute. Control remains with deterministic tools. Born from production experience with high-risk AI deployments.
Protocol Principles
- No action without explicit tool invocation
- One step per iteration — controlled loop
- Errors are first-class control signals
- System has veto power over any proposal
- Ambiguity resolves to minimal action
Why XCT Exists
XCT is not a prompt style or agent framework — it's an architectural protocol. Traditional AI integrations let models decide what to do and when. XCT inverts this: the model proposes, the system validates, deterministic tools execute. Authority remains external by design.
Polaris Core
Ultra-optimized C++ binding for llama.cpp with first-class Python integration. Built for maximum-performance local inference with streaming, XCT protocol support, and intelligent batch management.
Features
- GGUF model support (Qwen, Mistral, LLaMA, Gemma)
- GPU acceleration via CUDA (CPU/Metal also supported)
- Token streaming through Python callbacks
- JSON early-stop for XCT protocol (~30% time savings)
- Intelligent batch backoff with automatic retry
- Thread-safe architecture with GIL management
Performance (Qwen3-4B · RTX 4090)
Polaris v2
Multi-modal AI assistant with conversational AI, voice synthesis, document processing, and multi-platform connectivity. Customizable chat deployable on local infrastructure or cloud — your models, your data, your rules.
Capabilities
Infrastructure Options
Architecture
- FastAPI backend with JWT authentication
- MongoDB persistence + ChromaDB vector store
- LangChain context management
- Dynamic model selection (local or cloud)
- Prometheus metrics and observability
- Docker Compose deployment
Polaris v3
Automation platform powered by the XCT Protocol. Models propose actions, the system validates and controls execution through deterministic tools. Built for infrastructure orchestration, deployment pipelines, and compliance-sensitive environments where reliability matters more than speed.
XCT Execution Flow
Tool Ecosystem
Architecture
- Clean Architecture with layered separation
- FastAPI REST + WebSocket executor
- RAG with ChromaDB for contextual retrieval
- MongoDB dual-layer memory (short + long term)
- Local inference (Polaris Core) or Groq Cloud
- Hot-reload configuration without restarts
- Prometheus metrics and observability
XCT-Qwen3-4B
An execution-oriented language model fine-tuned with LoRA to operate under the XCT Protocol. Not a conversational assistant — a constrained execution engine for controlled, deterministic systems.
Model Specs
Quantized Variants (GGUF)
Designed For
- Deterministic execution agents
- Infrastructure orchestration (K8s, CI/CD)
- Deployment automation pipelines
- Tool-driven workflows
- Compliance-sensitive environments