AI Tools
Enterprise AI platforms — what each one actually is, what it costs, how to govern it, and the order to adopt it in.
Working references for the managed AI platforms that sit next to enterprise data — what each product actually is underneath the marketing, where the money goes, what the governance story is, and a defensible order to adopt things in.
Every page here is written for the same reader: someone who has to pick a platform, estimate a bill, and answer a security review — not someone shopping for a demo.
AWS Bedrock AI Strategy
41 min agoConsolidated overview of Bedrock capabilities, use cases, architecture patterns, governance, and a sequenced project portfolio for a wealth management firm.
AWS Bedrock Use Cases for RIAs
44 min agoA catalog of 112 concrete Bedrock applications for a wealth management firm, grouped by function — document intelligence, data engineering, CRM, compliance, analytics, backfills, and agents.
Snowflake Cortex AI: A Working Engineer's Guide
44 min agoImplementation-level Cortex reference — product map, setup, AISQL functions, Cortex Search, the token cost model, monitoring queries, governance, and gotchas.
Snowflake Cortex AI: The Big Picture
44 min agoA non-technical orientation to Cortex AI — what it is, why it matters strategically, where the real value is, what it costs, and what to be skeptical about.
The through-line
Both platforms sell the same core promise — models that run next to governed data, under existing IAM, without shipping records to a third-party endpoint. They differ in where the gravity is:
| AWS Bedrock | Snowflake Cortex | |
|---|---|---|
| Center of gravity | AWS services, S3, documents, APIs | Data already in Snowflake |
| Interface | SDK, agents, flows | SQL |
| Strongest at | Application reasoning and orchestration | In-warehouse transformation and retrieval |
| Main cost trap | Idle agent/knowledge-base infrastructure | Model choice, and Search indexes billed while idle |
A hybrid is usually correct: the warehouse stays the governed data platform, the cloud platform provides application reasoning. Decide per workload rather than declaring one universal AI platform.
What goes here
- Platform capability maps and honest comparisons
- Cost models and the levers that actually move the bill
- Governance artifacts, evaluation measures, and adoption sequences
Related: LLM Wiki on building a self-maintaining knowledge base, and RAG for transit planning on grounding answers in an organization's own documents.
Commodity Pricing
AI-synthesized walkthrough of the GS "10 Lessons with Jeff Currie" note — reads commodity price as structural + cyclical components.
AWS Bedrock
Consolidated overview of Bedrock capabilities, use cases, architecture patterns, governance, and a sequenced project portfolio for a wealth management firm.