content
SensAffect Tagline
sens·au·di·o /ˈsɛnsˌɔːdi.oʊ/ noun
acoustic frequency therapeutic engine
1. An adaptive neuroacoustic synthesizer delivering calibrated Solfeggio grounding frequencies (174 Hz) and Theta binaural differentials (6 Hz) with stochastic brown noise to downregulate autonomic arousal and restore affective equilibrium.

THE EMOTIONAL INTELLIGENCE LAYER

Build applications that respond to emotional context.

SensAffect exists to help applications respond to the person and the moment. Use emotional context to inform agents, support experiences, and adaptive interfaces—with evidence, user preferences, and clear boundaries guiding the response.

01 / SENSE

Explore emotional context

Inspect valence, arousal, and dominance through the affective matrix and the one SensAffect eVAD lexicon.

02 / GOVERN

Evaluate before acting

Explore governance policies and dual authority in the platform playground.

03 / BUILD

Connect to your application

Explore typed contracts, SDK examples, and affective configuration presets.

More context.
A more considered response.

The same response does not fit every moment. A person exploring, feeling overwhelmed, or asking for help may need different pacing, choices, or support. A single sentiment label leaves out much of the picture. SensAffect represents emotional context across valence, arousal, and dominance, alongside the evidence and policies that inform a response.

Valence

How positive or negative is the emotional tone?

Negative Positive

Arousal

How activated or settled is the state?

Settled Activated

Dominance

How much control or agency does the state convey?

Overwhelmed In control

Build for the moment
someone is in.

Inform agents, support experiences, and adaptive interfaces with context that helps them choose a more useful next step.

Conversational agents

Give an agent structured context to consider before its next response. Explore when to clarify, slow the exchange, or offer a different path.

Context-aware conversation

Support experiences

Prototype responses to frustration and uncertainty. Test policies for when an interaction should move to human support.

Escalation and response policies

Adaptive interfaces

Explore affective presets for visual intensity, motion, and interaction pace, with user preferences guiding the experience.

Interface configuration

What could this enable
in your product?

Three application-policy examples show how context can inform a response. Use the source as a starting point for your own integration.

A more considered shopping experience

Tailor a shopping experience with quieter layouts, relevant choices, and optional help.

Learn more about A more considered shopping experience

For a seasonal promotion such as a Blue Monday sale, offer a quieter storefront: fewer competing elements, interest-based recommendations, and easy access to help. Keep offers consistent and let the shopper choose their mode.

Context → focused layout and optional assistance
shoppingExperience(context, {
  quietMode: true,
  interests: ['home', 'music']
});

Listening that follows your intention

Adapt a music experience to the listener’s goal, from focused listening to winding down.

Learn more about Listening that follows your intention

In a music-streaming app, let the listener choose “unwind.” Suggest a calmer mix, soften visual motion, and lengthen track transitions while keeping their queue and playback controls in their hands.

Listening goal → gentler sound transitions and visuals
listeningExperience(context, {
  goal: 'unwind',
  reducedMotion: false
});

Support with a clearer next step

Help an agent offer clearer guidance and a timely route to human support.

Learn more about Support with a clearer next step

When the available context suggests frustration, an agent can offer shorter instructions and a route to human support. Evaluate the evidence and offer a choice before taking action.

Context → clearer guidance and a human-support option
supportExperience(context, {
  requestHuman: false
});

Runnable JavaScript policy examples with supplied context. These illustrate application decisions; they are not a connected SDK or live emotion detection.

Humanoids experiencing emotional context
in the physical world.

Advanced humanoid implementations could bring the same principles to embodied agents: interpret context, adapt the interaction, and check authority before acting. These are potential applications, not currently available SensAffect robot products.

A home-nursing assistant

Support home-care conversations with patient explanations and caregiver coordination.

Learn more about A home-nursing assistant

A humanoid supporting a home-care team could adjust its speaking pace, explain a routine more clearly, or offer to contact a caregiver when someone expresses discomfort or uncertainty.

Emotional context informs communication and requests for help. Clinical decisions, medication, and physical assistance require separate professional oversight and safety controls.

Context → reassurance, clear explanations, caregiver contact

A cruise-ship chef or bartender

Adapt onboard hospitality to each guest’s preferences and conversational pace.

Learn more about A cruise-ship chef or bartender

A hospitality humanoid could adapt its conversational style, explain a menu patiently, or offer a quieter interaction when a guest prefers less stimulation.

Guest preferences guide service. Allergens, food handling, alcohol service, and physical movement remain subject to explicit checks; emotional inference does not establish eligibility or consent.

Context → personalised service and human-staff handoff

An emotional-development companion, ages 6–12

Support supervised emotional learning through play, storytelling, and conversation.

Learn more about An emotional-development companion, ages 6–12

A friendly humanoid could support age-appropriate emotion vocabulary, turn-taking, storytelling, and practising how to ask a trusted adult for help.

Designed for caregiver-supervised use, with clear disclosure that it is a robot, minimal data collection, and easy stop controls. It should support real-world relationships without encouraging secrecy, dependency, or replacing family, friends, teachers, or professional care.

Context → guided play, emotional learning, trusted-adult support

SensAffect would provide interaction context within a broader robotics system. Physical safety, safeguarding, consent, and domain-specific validation must be designed and evaluated separately.

Future scope:
spatial and physical context.

Extending the humanoid concepts above, proposed capabilities could connect what an agent observes, the objects and people around it, and the continuity of an interaction. The following names describe future scope, not released features.

RealVision

Explore visual context from scenes, gestures, posture, and movement.

Learn more about RealVision

Explore consented visual observations of scenes, posture, gestures, and movement. Combine these signals with conversational context and uncertainty rather than treating appearance as proof of an emotion.

RealObject

Understand surrounding objects and their spatial relationships.

Learn more about RealObject

Represent objects, their spatial relationships, and possible interactions. A care assistant could identify a requested item; a hospitality robot could locate tableware, with separate checks before handling anything.

RealContacts

Connect interactions to trusted people, roles, and authorised handoffs.

Learn more about RealContacts

Explore a permission-based model of people, roles, and trusted contacts: who is present, whom the user wants involved, and who may authorise or receive a handoff. Recognition alone would not grant access or consent.

RealDiary

Keep a user-controlled record of context, preferences, and follow-ups.

Learn more about RealDiary

Explore a user-controlled record of interactions, observations, preferences, and follow-ups. Keep observed events distinct from interpretations, with review, correction, deletion, and limited retention built into the experience.

Spatial awareness & detection

Explore proximity, orientation, personal space, movement trajectories, and changes in the surrounding environment to inform when an agent should approach, pause, or request assistance.

Physical expression

Explore how voice, gaze, posture, gesture, and movement pace can make an agent's intentions easier to understand. Physical actions would remain subject to explicit permission and independent robotics safety controls.

Possible combinations include RealVision and RealObject for home-care or hospitality context, RealContacts for a caregiver or staff handoff, and RealDiary for authorised continuity. Child-facing implementations would require caregiver controls and additional safeguarding across every capability.

From a signal
to a governed response.

A continuous loop connects observations, emotional context, policy evaluation, and outcomes.

  1. 01

    Sense

    Bring observations into a structured representation that your application can work with.

  2. 02

    Interpret

    Assemble affective context from evidence and inspect the dimensions behind it.

  3. 03

    Evaluate

    Check policy and authority before an action is allowed to proceed.

  4. 04

    Respond & observe

    Connect an authorised response to the application and observe what happens next.

Emotional context
needs boundaries.

How an application uses a signal matters as much as the signal itself. Explore authority, policy, and evidence together in the platform.

Explore the governance playground ↗

Separate sensing from acting

Use the dual-authority model to distinguish permission to interpret context from permission to take action.

Make policy visible

Inspect evaluation results in the governance playground before wiring behaviour into your application.

Keep evidence in view

Work with typed contracts and explicit context instead of treating an inferred emotion as a certainty.

Explore it in the console.
Build it into your product.

Move from a visual exploration to the contracts and examples used by the platform.

Affective matrix

Explore emotional dimensions in an interactive spatial view.

eVAD lexicon

131 curated feeling words — smaller, more appropriate, and more accurate than a general VAD dump. This site uses that lexicon.

App incubator

Try configuration presets for different interaction styles.

Contracts & SDK

Review structured interfaces and integration examples.

Start with one
interaction.

Choose a moment where better context could help. Explore the signal, define the boundaries, and evaluate the response before expanding.

  1. Choose a use case

    Define the interaction you want to explore and what a useful response would look like.

  2. Explore the evidence

    Use the console to inspect the affective representation and its limitations.

  3. Connect and evaluate

    Review the SDK examples, apply your policies, and test against your own requirements.

A few things
to know.

What does the animated cloud represent?

It is an illustrative multimetric model. Labels cycle through the SensAffect eVAD Lexicon. The homepage does not read your emotions.

Is this a sentiment-analysis tool?

Sentiment is one part of the picture. SensAffect also represents valence, arousal, and dominance, with platform tools for exploring evidence, governance, and application behaviour.

Who is the platform for?

Developers and product teams exploring how emotional context can inform agents, support experiences, and adaptive interfaces.

Where should I start?

Read the eVAD lexicon on this site, then open the platform for the matrix and governance playground.

Does an emotion label describe a person with certainty?

No. An affective representation is context for an application to evaluate, not a definitive statement about a person. Evidence, uncertainty, user preferences, and appropriate boundaries still matter.

Give your next interaction
more context.

Give agents, support experiences, and adaptive interfaces the context to respond more thoughtfully. Start with one interaction and evaluate what improves.