Show: Snowflake Summit · Publisher: Snowflake · Host: Snowflake editorial
Episode URL: https://www.youtube.com/watch?v=MSX_gbIZfmA
Publish date: 2026-04-14
Duration: 135.0s
Default source credibility: MEDIUM — Vendor conference — Snowflake keynotes and product launches are marketing (LOW for claims about own products). Named F500 customer talks with production metrics stay HIGH. Filter aggressively for customer sessions with named speakers and quantified outcomes; skip product demos and partner pitches.
- Landing AI’s Tony Lee discusses how their Agentic Document Extraction tool, powered by AWS and Snowflake, speeds up business processes significantly.
- The partnership with AWS and Snowflake provides a reliable technical backbone for Landing AI’s product development.
- Agentic Document Extraction can understand any document format, offering secure and efficient data processing within Snowflake’s environment.
Extracted quotes
| # | Credibility | Speaker | Org | Timestamp | Topic | Quote |
|---|---|---|---|---|---|---|
| 1 | MEDIUM | Tony Lee (Vice President of Partners and Alliances) | Landing AI | 0:08 | 07-adoption-challenges | We got it done in a little under two months. |
| 2 | MEDIUM | Tony Lee (Vice President of Partners and Alliances) | Landing AI | 1:14 | 01-ai-native-landscape | Having that directly available through Snowflake Cortex and over our AWS infrastructure has been very helpful to our development team to be able to build on top of that. |
Per-quote detail
1. Tony Lee — Landing AI (0:08)
We got it done in a little under two months.
- Credibility: MEDIUM — Named exec with specific claim, but missing denominator.
- Topic tag:
07-adoption-challenges
2. Tony Lee — Landing AI (1:14)
Having that directly available through Snowflake Cortex and over our AWS infrastructure has been very helpful to our development team to be able to build on top of that.
- Credibility: MEDIUM — Named exec with specific claim, but vendor/sponsorship context.
- Topic tag:
01-ai-native-landscape
Extracted 2026-04-14T14:12:27 via scripts/podcast_mine.py (MLX mlx-community/Qwen2.5-32B-Instruct-4bit).