Multimodal AI Use Case

Multimodal AI Observability

Every inference decision, every grounding source, every safety filter—tracked across text, image, video, and code. Finally answer: "Why did the AI cite that source?"

Syntax Decimal SD.32.04
Multimodal

Live Multimodal Event Stream

STREAMING

Multimodal Processing Pipeline

Input
Input
Text/Image/Video
->
Encode
Encode
Modal embeddings
->
Ground
Ground
Search retrieval
->
Reason
Reason
Cross-modal fusion
->
Safety
Safety
Filter and validate

Multimodal Query Examples

Grounding Source Audit

Track exactly which sources the AI used for grounding and why they were selected.

SELECT query, source_url, relevance_score,
retrieval_rank, freshness_date
FROM events
WHERE event_id = 'multimodal.grounding.retrieved:1'
AND session_id = 'search_overview_123'

Image Understanding Trail

See how the AI interprets and reasons about visual content in multimodal queries.

SELECT image_id, detected_objects,
ocr_text, scene_classification
FROM events
WHERE event_id = 'multimodal.vision.analyzed:1'
AND confidence > 0.8

Safety Filter Decisions

Full transparency on what triggered safety filters and how content was modified.

SELECT input_hash, filter_triggered,
category, action_taken, reason
FROM events
WHERE event_id = 'multimodal.safety.filtered:1'
AND timestamp > NOW() - INTERVAL '1h'

AI Overview Attribution

Trace how AI Overviews in Search are generated and which sources they cite.

SELECT search_query, overview_text,
cited_sources, click_attribution
FROM events
WHERE event_id = 'multimodal.search.overview:1'
AND has_citations = true

Without Event Model

AI decisions hidden in black boxes

With Event Model

Complete multimodal transparency

AI Overview question
"Where did this answer come from?" — no source attribution visible
Image analysis
"How did it identify that object?" — reasoning opaque
Safety filter
"Why was my query blocked?" — no explanation given
AI Overview question
multimodal.grounding.retrieved:1 -> sources=["mayo.clinic", "nih.gov"], scores=[0.94, 0.89]
Image analysis
multimodal.vision.analyzed:1 -> objects=["car", "pedestrian"], confidence=[0.97, 0.92]
Safety filter
multimodal.safety.filtered:1 -> category="medical_advice", action="add_disclaimer"

Why Observable Multimodal AI?

Link

Source Attribution

Track exactly which sources informed each AI-generated response for proper citation.

Image

Vision Transparency

Understand how the model interprets images, videos, and visual content.

Balance

Publisher Fairness

Ensure fair attribution and traffic distribution for content creators.

Shield

Safety Auditability

Complete visibility into content filtering and safety decisions.

Refresh

Cross-Modal Reasoning

Trace how text, image, and code inputs are fused for reasoning.

Chart

Quality Metrics

Monitor hallucination rates, factual accuracy, and citation quality.

Regulatory and AI Governance Compliance

EU

EU AI Act

General Purpose AI (GPAI) transparency requirements

Doc

GPAI Code

General Purpose AI model documentation standards

Search

Search Neutrality

Fair ranking and source attribution compliance

News

Publisher Rights

Copyright and content licensing transparency

Lock

GDPR Art. 22

Automated decision-making explainability

NIST

NIST AI RMF

AI risk management framework compliance

Make Multimodal AI Transparent

Build trust through complete observability across text, image, video, and code modalities.

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