Platform Overview

Every AI expert Kairos deploys started as a real one.

Kairos puts an AI expert beside every learner and operator, in real time. Here's how one is built for your platform and doctrine, how the runtime engine decides what to say and when, and why every response traces back to a source you can check.
SYSTEM MODEL

The expert, the instruction engine, and the foundation.

LAYER 1

AI Model Training

How the AI expert gets built
Rapid multimodal training
SME knowledge encoding and elicitation
Docs and doctrine ingestion
Domain models tuned or used as-is
Fast additions and edits
LAYER 2

Core Features

What the AI expert does
Real-time proactive guidance
Pre-task briefings
Post-task debriefs
Detailed root cause analysis
Adaptive learning levels
Skill evaluation with assistance off
Operator assistance on the job
LAYER 3

Foundation

Why it can be trusted and deployed
Traceability and guardrails
Cites its sources
Persistent student memory
On-premise, cloud, or hybrid
APIs and integration
BUILDING THE EXPERT

Your platform, doctrine, and expert judgment become the model.

01

Data ingestion and normalization

Kairos perceives telemetry up to 60Hz, audio, and video through a partner-agnostic adapter layer. Inputs are normalized before the runtime engine evaluates them.
02

SME knowledge elicitation

Expert judgment is captured as personas, escalation rules, and tolerances that experts can adjust in plain language. Human review remains part of the workflow.
03

Doctrine and reference grounding

Manuals, SOPs, curriculum, and rubrics are indexed for retrieval. Instruction cites the source behind the guidance.
04

Domain models and fast additions

Domain models can be tuned or used as-is. New maneuvers, skills, and exercises stand up in days as configuration your experts approve, with every change versioned.
THE RUNTIME ENGINE

From live signal to the intervention that matters now.

01 / Sense

Parse the live environment

Kairos parses every stream in real time. Edge and cloud models produce normalized, scored signals from telemetry, audio, and video.
02 / Session PLan

Define the exercise

A machine-readable session plan describes the exercise and the standards against which performance is evaluated.
03 / Kairos Core

Decide what matters now

The ranking model decides what matters now, what can wait, and when silence is correct.
04 / Communication Model

Deliver the right intervention

Reflex cues arrive at sub-second latency. Reasoned guidance is grounded in doctrine and persona. Deferred observations feed the debrief.
WHY THE DESIGN MATTERS

The research favors feedback during the work, with guardrails around assistance.

STEP-LEVEL FEEDBACK

0.76 vs 0.31 SD

Step-level feedback matters

Step-based tutoring reached 0.76 SD while answer-only systems reached 0.31 SD. One-to-one human tutoring was 0.79 SD in the same review.
ASSISTANCE-OFF TESTING

+48% / -17%

Why guardrails and assistance-off testing matter

Unrestricted GPT access improved practice performance by 48% but reduced unassisted exam scores by 17%. A properly guarded tutor removed the harm in the study.
THE FOUNDATION

Designed for environments where review and deployment matter.

Property
How KAIROS implements it

Traceability and guardrails

Every cue is reconstructable from signal to rule to source document, with human-in-the-loop review.

Grounded citations

Manuals, SOPs, curriculum, and rubrics are indexed for retrieval, and instruction cites its sources.

Persistent student memory

One configurable profile per learner is read and written by briefings, live guidance, evaluation, and debrief.

Deployment envelope

On-premise, cloud, hybrid, or fully disconnected deployment, with edge inference and a language model component that can run locally.

APIs and integration

Simulator adapters, xAPI export to existing LMS platforms, and APIs. KAIROS integrates with what exists rather than replacing it.
START A CONVERSATION

Bring us the task your experts struggle to scale.

We can show the live instruction loop, explain the integration path, and identify what an initial evaluation would need to prove.
Talk to our team