Live Data Sync

Question

Hypothesis

Can indexing lifecycle state be observable enough to diagnose stale or missing AI evidence?

What this demonstrates

• Create, update and delete operations have visible indexing state.
• Retrieval proof checks whether a known entity is actually searchable.
• The operator can distinguish source count, queue state and vector count.

What this does not demonstrate

• Unlimited ingestion throughput
• Zero-latency consistency

Architecture and data flow

1. 01 Synthetic source data
2. 02 Host application policy
3. 03 AI Fabric capability
4. 04 Observable UI result

Guided steps

1. 01

   Seed the synthetic source records.

2. 02

   Run the synchronization operation and inspect queue progress.

3. 03

   Use retrieval proof, then update and remove a record.

Observable evidence

The running application and its health surface are the public evidence for this experiment. The source repository exposes the implementation path behind the visible result.

Security and data boundary

The application database remains the source of truth. AI Fabric receives explicit lifecycle operations and exposes index evidence.

All source entities are generated for the experiment.

Known limitations

• The single-node demo does not model every distributed failure mode.
• Throughput figures are not presented as benchmarks.

Technologies and products used

• Spring Boot
• AI Fabric 0.4.0
• Lucene
• OpenAI embeddings