Technology

The architecture beneath the care.

Carely isn't just a chatbot on a screen. It's a multi-layer AI system purpose-built for the nuanced, high-stakes domain of cognitive eldercare.

How It Works

From button press to insight.

Three steps, happening simultaneously. The user experiences warmth,the technology handles everything else.

01

Voice Input & STT

The elderly user presses the button and speaks naturally. Whisper Large v3 delivers best-in-class transcription accuracy across Arabic dialects and English — handling accents, elderly speech patterns, and home acoustic environments with equal precision.

Whisper Large v3 Arabic dialects Real-time
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02

LLM Reasoning & Response

A state-of-the-art language model, purpose-built for eldercare interaction, processes every turn with full longitudinal memory — the patient's history, mood baseline, health context, and evolving topics — to deliver a warm, clinically grounded reply.

Eldercare AI Longitudinal memory Elder-safe guardrails
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03

Voice Output

OpenAI TTS synthesizes a warm, natural voice in the user's preferred language,Arabic or English. The response is spoken clearly and immediately, with no noticeable delay.

OpenAI TTS Arabic & English Low latency
Cognitive Analysis Engine

The 3-layer pipeline.

Every conversation passes through three independent AI analysis layers,each looking at different granularities of cognitive health signal.

L1
Micro-Analysis

Real-Time Conversation Analysis

Analyzes each message and session in real time. Tracks vocabulary richness, sentence coherence, topic continuity, and response latency. Flags immediate anomalies like sudden confusion, repeated phrases, or unusually short responses.

Vocabulary richness Sentence coherence Repetition detection Latency tracking
L2
Macro-Analysis

Weekly & Monthly Pattern Recognition

Aggregates session-level data to surface medium-term trends: changes in conversational engagement, emotional tone shifts, evolving topic preferences, and deviations from the user's established behavioral baseline.

Trend aggregation Mood trajectory Engagement patterns Baseline drift
L3
Longitudinal Synthesis

Long-Term Cognitive Narrative

Synthesizes months of data into a longitudinal cognitive health narrative. Deep multi-turn AI reasoning surfaces slow, gradual decline patterns that no single session would ever reveal — and translates them into structured, clinician-ready caregiver reports.

Sequence modeling CII score Decline prediction Caregiver reports
Proprietary Metric

The Cognitive Interaction Index (CII)

The CII is Carely's core output metric,a composite score computed weekly from all three analysis layers. It gives caregivers a single, trackable number that reflects cognitive engagement health over time.

Unlike clinical assessments that happen once a year, the CII is computed continuously,meaning trends surface weeks earlier than traditional care pathways allow.

Vocabulary Coherence Score,range and precision of word use
Engagement Continuity,ability to sustain topic and narrative
Temporal Orientation,accuracy of time/date/event references
Emotional Consistency,mood stability across sessions
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CII Score
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The Evidence

Grounded in peer-reviewed science.

Carely's analysis method isn't invented in a vacuum. A growing body of peer-reviewed research establishes that everyday conversation carries measurable cognitive signals — and that AI can read them earlier, more objectively, and with less stress for the patient than a once-a-year paper test.

Conversation is a signal

Spontaneous speech carries measurable linguistic markers — vocabulary range, coherence, narrative structure — that track cognitive status in older adults.2,3

AI can read it — gently

Conversational AI can administer cognitive assessment to older adults, who tolerate an AI examiner with no greater psychological burden than a human one.1,2

Continuous beats annual

Computerized, automated assessment detects subtle cognitive change earlier and more objectively than once-a-year paper-and-pencil testing.3,4

Selected Peer-Reviewed Research
1Serafimovska, A., Swavley, K., Ao, A. Z. Q., Challinor, K. L., & Florio, T. (2025). Cognitive status assessment of older adults – test administration by conversational AI chatbot: proof-of-concept investigation. Journal of Clinical and Experimental Neuropsychology, 47(5), 472–484. doi.org/10.1080/13803395.2025.2542248
2Igarashi, T., Iijima, K., Nitta, K., & Chen, Y. (2024). Estimation of the cognitive functioning of the elderly by AI agents: a comparative analysis of the effects of the psychological burden of intervention. Healthcare, 12(18), 1821. doi.org/10.3390/healthcare12181821
3Javed, A. R., Saadia, A., Mughal, H., Gadekallu, T. R., et al. (2023). Artificial intelligence for cognitive health assessment: state-of-the-art, open challenges and future directions. Cognitive Computation, 15, 1767–1812. doi.org/10.1007/s12559-023-10153-4
4Henkel, C., Seibert, S., & Nichols Widmann, C. (2025). Current advances in computerized cognitive assessment for mild cognitive impairment and dementia in older adults: a systematic review. Dementia and Geriatric Cognitive Disorders, 54, 109–119. doi.org/10.1159/000541627
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Signals, not a diagnosis. Carely listens to everyday conversation and flags patterns — a drop in vocabulary, more repetition, a shift in mood or engagement — then shows caregivers what it noticed and when. It points to “this may be worth a closer look,” it does not diagnose, name a condition, or replace a clinician's assessment.
Tech Stack

Built on proven foundations.

Every component was chosen for reliability, Arabic language performance, and elder-appropriate design.

Speech-to-Text
Whisper Large v3

The world's leading open-weight speech recognition model. Handles Arabic dialects, elderly speech patterns, and real-world acoustic noise with best-in-class accuracy across all supported languages.

Language Model
Eldercare-Specialized AI

A leading-generation language model architecturally shaped for the demands of elderly care — calibrated on elder-communication patterns, with built-in safety guardrails, longitudinal memory, and health-context awareness at every turn.

Text-to-Speech
OpenAI TTS

High-quality neural voice synthesis via OpenAI's TTS API. Natural-sounding Arabic and English output with warm voice profiles tuned for elderly users.

Cognitive Analysis
Proprietary Cognitive Analysis Pipeline

A purpose-built, multi-stage AI system designed from the ground up for eldercare signal extraction. Three independent reasoning layers analyze each conversation for vocabulary precision, narrative coherence, and longitudinal behavioral drift.

Infrastructure
Edge-Cloud Hybrid Architecture

Voice capture, session management, and audio playback run locally on the patient's device for instant, zero-latency response. AI reasoning and cognitive analysis operate in the cloud, enabling depth, scale, and continuous improvement.

Privacy Architecture

Trust is a technical choice.

Healthcare-Grade Privacy Design

Built with healthcare data protection principles at the architectural level. Data subjects retain full control. No sale, no third-party sharing — ever.

Encrypted in Transit & At Rest

All data flows are protected with enterprise-grade TLS encryption in transit. Audio, transcripts, and analysis data are handled by infrastructure-layer encryption at rest. No raw patient data is ever exposed externally.

Transparent Data Use

Conversation data has one purpose: generating caregiver insights. That's it. Every data use is documented in plain language in the privacy policy.

Curious to learn more?

Join the pilot program.

We're onboarding families and institutions for early access. Help us shape the product.

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