Data Visualization From OpenAI API Research

The demo is voice. The product is structured care data.

The deep dive confirms a two-layer strategy: use OpenAI Realtime for the emotional caregiver moment, then use Responses API with Structured Outputs to create reliable care logs, burden signals, safety flags, and Thai family handoff.

Core OpenAI only Hero Realtime voice Proof Structured Outputs HF Future evaluation only
01Thai voice check-in
02Realtime or transcript fallback
03Structured care log JSON
04Safety and burden flags
05Thai family handoff

Five Research Insights

What the OpenAI deep dive changed.

The key insight is prioritization: build structured extraction first, then layer voice on top for the demo.

1

Structured Outputs are the center

The UI needs stable fields for incident type, safety risk, burden score, and family handoff. Free-form chat is not enough.

2

Realtime is the emotional front door

Voice makes the caregiver moment natural, but it should feed the same structured endpoint as every fallback.

3

Fallbacks protect the pitch

Use seeded transcript or transcription if microphone, network, or Realtime setup fails. The same JSON proof remains visible.

4

Multimodal is later

Care cards and hospital PDFs are relevant, but they create medication and clinical interpretation risk. Keep them out of the core demo.

5

Hugging Face is not v1 core

Thai ASR assets are useful for future evaluation. Dementia datasets are mostly non-Thai, access-limited, or diagnosis-adjacent.

OpenAI API Path

One workflow, three reliability levels.

All paths must converge into the same structured care log endpoint, so the product stays reliable even if voice is unstable.

A

Best Demo

Browser microphone -> Realtime API `gpt-realtime-1.5` -> brief Thai conversation -> structured extraction.

Voice
B

Reliable Fallback

Recorded audio -> `gpt-4o-transcribe` or `gpt-4o-mini-transcribe` -> structured extraction.

Audio
C

Safest Fallback

Seeded Thai transcript -> Responses API with Structured Outputs -> same care log JSON.

Text
D

Product Proof

Responses API converts narrative into incident, burden, safety, escalation, and handoff fields.

JSON
E

UI Output

Render risk badge, burden score, non-medical steps, sibling message, and weekly trend.

Demo

Demo Resilience

The fallback ladder is part of the product strategy.

The research makes the fallback architecture explicit instead of treating it as a backup hack.

Realtime Path

Use for the strongest emotional moment: tired caregiver speaks naturally in Thai. Best for judges, highest setup risk.

Transcription Path

Use recorded audio when live conversation is too risky. Still proves Thai voice input and OpenAI processing.

Seeded Transcript Path

Use when everything else fails. It still proves the core transformation from Thai narrative to care log JSON.

Structured Output Schema

The schema is the product contract.

Each field is selected because it can drive visible UI and safer family coordination.

Care Log JSON
Incident + Burden + Safety + Handoff

Incident

`incident_type`, `patient_behavior`, and `trigger_or_context` capture observable facts.

Safety

`safety_risk` and `professional_escalation` separate low-risk support from urgent help.

Burden

`caregiver_stress_level_1_to_5` and `caregiver_sleep_hours` make hidden caregiver wellness visible.

Next Steps

`immediate_non_medical_steps` keeps guidance practical and non-clinical.

Family Ask

`family_help_request` turns exhaustion into a specific task or shift request.

Thai Handoff

`family_handoff_thai` creates a respectful message that siblings can act on.

Safety Boundary

Safe product language is a feature, not a disclaimer.

The MVP wins by being useful without pretending to be a clinician.

Can Do

Wellness support, care documentation, family coordination, education, and escalation guidance.

Incident logging Burden tracking Non-medical steps Sibling handoff Weekly trend Professional escalation

Cannot Do

Diagnosis, treatment, medication decisions, restraints, sedatives, or emergency replacement.

Dementia diagnosis Medication changes Sedative advice Clinician replacement Emergency replacement Medical claims from HF data

Hugging Face Asset Review

Useful for future evaluation, not for the v1 core.

The research found Thai ASR assets and dementia assets, but not a clearly licensed Thai dementia caregiver burden dataset.

Useful Later

Thai ASR Assets

Thai Whisper and Thai elderly speech resources can help future transcription evaluation, especially for elderly voices and accents.

Background Only

Dementia Datasets

DementiaBank-style and Alzheimer Q&A assets are not Thai caregiver workflow data and may have license or medical-quality risks.

Avoid V1

Diagnosis Models

Dementia detection or medical-imaging models pull the product toward diagnosis, which is outside the safety boundary.

Build Decision

Build the OpenAI MVP now, in this order.

The best next task is not more research. It is the structured output endpoint and UI proof.

Priority Order

Start with the product proof, then add the emotional voice layer.

Responses API with Structured Outputs
Seeded Thai transcript demo
Care log UI and family handoff
Realtime API voice
Weekly trend
Optional multimodal context later

Final Insight

The winning demo is not "AI talks in Thai." It is "Thai caregiver stress becomes structured, safe, shareable family coordination."


Evidence caveat: Firecrawl research was completed. Tavily was checked but blocked because `TAVILY_API_KEY` is not configured.

Source Trail

Research files and primary docs behind this visualization.

Final DecisionDemo stack, build order, and architecture. Capability MapCore, optional, and later OpenAI capabilities. Model + SchemaAPI path and care log JSON schema. HF AssetsThai ASR and dementia asset review. OpenAI RealtimeVoice front door for the demo. Structured OutputsSchema-valid care logs and handoff fields. Speech To TextAudio fallback path for reliability. VisionLater context input, not v1 core. Thai ASRFuture evaluation references.