AI Infrastructure

Kairos

A production-grade cognitive regulation system with LLM inference, RAG retrieval, and neural safety classification. Kairos uses Cognitive Load Theory as an active control signal combined with a Mixture of Experts architecture for intelligent, closed-loop orchestration — featuring ALETHEIA epistemic protection and autism-first adaptive design.

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v0.46 Current release
7,800+ Tests passing
MoE Architecture
REST + WebSocket API

How it works

From signal to adaptation

Kairos sits between enterprise systems and end users, dynamically regulating the cognitive complexity those systems impose on knowledge workers.

01

Signal ingestion

Ingests workflow signals: task completion time, context-switching frequency, error rates, information density, and session patterns.

02

Cognitive inference

A three-dataclass orchestrator pipeline processes signals through Focus, Safety, CLT, and Progressive Disclosure layers.

03

Dynamic adjustment

Adjusts how enterprise systems deliver information — task sequencing, notification routing, and complexity tiering — without changing the underlying tools.

04

Model routing

Eleven model backends enable intelligent routing based on task type, context, and cognitive load state through CORTEX orchestration.

Core pattern

System architecture

A modular pipeline that consumes user text and produces intelligent, context-aware responses.

Kairos Frontend

Deterministic pipeline that consumes user text and produces a WorkOrder. Uses heuristics and pattern matching before any LLM is called.

Model Selection Engine

First-principles measurement-based system that assigns models to experts using deterministic probe suites (JSON adherence, coding sanity).

Local Model Runtime

ModelProvider abstraction layer supporting HuggingFace, llama.cpp (GGUF), vLLM, Ollama, and cloud APIs (Groq, OpenAI, Anthropic).

CORTEX Orchestrator

Allostatic routing that consumes WorkOrders and routes tasks through specialized Experts — with CLT headroom gating for adaptive load management.

ALETHEIA Epistemic Protection

Epistemic safeguards ensuring factual integrity, hallucination detection, and truth-preserving inference across the entire pipeline.

Autism-First Adaptive Design

Native neurodivergent support with sensory-aware content delivery, predictable interaction patterns, and configurable cognitive load thresholds.

Key features

Production-ready intelligence

Enterprise-grade features for real-world deployment.

Mixture of Experts

Unlike monolithic LLMs, tasks are split into a graph. Specialists handle planning, coding, and writing with optimized model assignments.

RAG retrieval

Retrieval-augmented generation for grounded, context-aware responses. Kairos retrieves relevant knowledge before generating — reducing hallucination.

Progressive disclosure

Adaptive streaming of text based on real-time cognitive load metrics. Information delivered at the pace you can process.

Pluggable runtimes

HF Transformers for development, llama.cpp for efficiency, vLLM for serving, cloud APIs for scale. Hybrid local and cloud routing for privacy.

Model caching

Thread-safe ModelCache with memory-aware LRU eviction. Prevents 14 GB reloads and manages VRAM and RAM lifecycle.

Neural safety classification

Real-time safety classification with recall-optimized models. Crisis detection, content filtering, and configurable policy enforcement.

Supported runtimes

Flexible model backends

Choose the right engine for your deployment scenario.

HuggingFace

Best for development and experimentation. Four-bit and eight-bit quantization support.

llama.cpp

Maximum efficiency and broad model support. Optimized for local inference via GGUF.

vLLM and Ollama

Optimized for serving and local server integration. High-throughput inference.

Cloud APIs

Groq, OpenAI, and Anthropic. Hybrid routing between local and cloud backends.

Installation

Get started

Modular installation via pyproject.toml for flexible dependency management.

pip install -e ".[api]" FastAPI + Uvicorn
pip install -e ".[llamacpp]" llama.cpp backend
pip install -e ".[full]" All backends + API
pyproject.toml 
[project.optional-dependencies]
core = [
    "transformers",
    "torch",
    "accelerate"
]
api = [
    "fastapi",
    "uvicorn",
    "pydantic",
    "httpx"
]
llamacpp = ["llama-cpp-python"]
vllm = ["httpx"]
full = ["kairos[core,api,llamacpp,vllm]"]

Observability

Instrumented for W33KND

Kairos is instrumented with the Kairos Observer for real-time monitoring and analysis.

Router decisions

Real-time tracking of expert selection for specific inputs.

Latency tracking

Processing time across the pipeline for bottleneck identification.

Safety recall analysis

Safety-triggered refusal analysis with recall-optimized classification.

Model registry

Cost and performance tracking across model versions.

Position

Why it is defensible

Kairos occupies a distinct position in the AI infrastructure landscape.

Infrastructure positioning

Sits at the system layer, not the application layer. Switching costs are high once it is deployed across enterprise workflows.

Compliance-first ICP

Healthcare, legal, and financial services carry high willingness to pay and high switching costs — our ideal customer profile.

Incumbent misalignment

Every major AI platform is incentivized to add features and increase engagement. Kairos is incentivized to reduce complexity.

Proprietary signal layer

Cognitive load inference from workflow metadata is a defensible position that deepens with every deployment.

Platform

Product ecosystem

Kairos is the core engine. The broader platform spans wellness, entertainment, monitoring, and model intelligence.

Active development

Kairos

Core cognitive load regulation engine.

In development

Heirloom

AI mental wellness companion, built in React Native.

R&D

eXitZork

Parser horror and therapeutic assessment engine.

Planned

W33KND

Telemetry and analytics console for the Kairos platform.

Planned

NeoPlus

Real-time AI model landscape monitoring.

Power your AI with Kairos

The adaptive intelligence at the heart of the PRJCT LAZRUS ecosystem. Get in touch about enterprise pilots and integration.

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