Infrastructure

Human context as a service.

Chaorda converts raw interaction signals into a high-fidelity context layer that agents use to adapt behavior in real-time.

Architecture Flow

01Multimodal Signal Ingestion
02Privacy-Preserving Normalization
03Preference Drift Prediction
04Adaptive Policy Runtime

The Stack

Four layers of human understanding.

Our infrastructure handles the complexity of signal processing, model inference, and policy execution so you don't have to.

Layer 01

Consented interaction signals

Chaorda starts with explicit user permission and captures only the modalities a deployment enables: text rhythm, voice tone, interaction pacing, corrections, and optional visual vectors.

Layer 02

Human state prediction

Signals are converted into uncertainty-aware predictions for frustration, overload, trust readiness, preference drift, and escalation risk.

Layer 03

Adaptive policy runtime

Prediction outputs flow into a runtime layer that controls how an AI agent responds, pauses, escalates, summarizes, or asks for clarification.

Layer 04

Privacy-preserving deployment

Sensitive raw inputs stay outside the application layer where possible. Deployments use tokenized telemetry, retention controls, and audit-ready consent events.

Capabilities

Platform Features.

Runtime controls

Set thresholds for escalation, pacing, confidence, tone adaptation, and reduced-adaptation modes.

Agent stack compatibility

Designed to sit beside existing model providers, orchestration frameworks, CRMs, support systems, and product telemetry.

Privacy modes

Configure edge-tokenized, enterprise-retained, or minimal telemetry modes by deployment requirement.

Outcome feedback

Connect completion, escalation, retention, satisfaction, and recovery signals back into the improvement loop.

Research

Scientific Pillars.

Our models are grounded in peer-reviewed research on emotional state prediction and preference drift modeling.

Emotional state prediction

Model uncertainty around frustration, trust, overload, and disengagement instead of forcing brittle emotion labels.

Preference drift

Study how human preferences change within a session as context, confidence, and interaction quality shift.

Consent-first datasets

Create collection protocols that make modality, retention, deletion, and audit boundaries visible by design.

Human-agent evaluation

Measure whether adaptive policies improve outcomes without increasing false adaptation or manipulation risk.

SDK

Low-latency runtime SDK.

Deploy human context in minutes. The runtime handles edge-tokenization and real-time inference.

import { Chaorda } from "@chaorda/runtime";

const runtime = Chaorda.initialize({
  apiKey: process.env.CHAORDA_API_KEY,
  product: "support-agent",
  privacyMode: "edge-tokenized",
  adaptation: {
    emotionalStatePrediction: true,
    preferenceDrift: true,
    escalationThreshold: 0.74
  }
});

const policy = await runtime.predictHumanState({
  sessionId: "session_91d",
  userMessage: "I'm fine. Just send the result.",
  interactionSignals: {
    pacing: "erratic",
    textInflection: "compressed",
    correctionCount: 3
  }
});

agent.respond({ strategy: policy.responseStrategy });

Use Cases

Market expansion.

The same human layer supports customer experience, digital health, and interactive entertainment.

Enterprise customer experience

Help support agents detect frustration earlier, escalate at the right moment, and avoid cold transactional loops.

Digital health and adaptive coaching

Support sensitive interactions where pacing, uncertainty, encouragement, and handoff timing matter.

Interactive entertainment

Give companions and characters a runtime understanding of trust, engagement, hesitation, and session momentum.

Education and learning support

Adapt explanations when learners show confusion, overload, repeated corrections, or rising confidence.

Evaluation

Measuring interaction risk.

Metric	Focus	Risk Mitigated
Prediction confidence	Model certainty index	False adaptation
Escalation accuracy	Handoff timing optimization	User frustration
Runtime Latency	Decision speed (<50ms)	Interaction drag
Consent compliance	Audit-trail integrity	Privacy breach

Data Moat

The compound effect.

Every interaction strengthens the state-prediction models, creating a defensible barrier against general-purpose LLMs.

Enterprise deployments

Human-agent interaction data

Better state prediction

Adaptive runtime policies

Higher retention

More deployments