⚪️ Inside the Intelligent Stack:
What Today’s Product Teams Need to Know
In 2019, a retail team deployed a clever algorithm to manage inventory. Then… forgot about it. It quietly broke and went unmonitored for over 3 years.
No alerts. No ownership. No updates.
It’s a real story. And it’s more common than you think.
Because what makes a product intelligent isn’t just AI
it’s the stack around the AI: the data layers, retrieval tools, agents, and feedback loops that keep it learning, grounded, and reliable.
Today’s issue is about that invisible scaffolding and why it matters now more than ever.
What You’ll Get From This Issue
We’ll break down the modern intelligent stack in a product-first way:
Why silver and gold data layers are your real AI foundation
How RAG models ground AI in your content
What role MLOps and agents play in building smarter features
A real example from Morgan Stanley that brings it all together
The most promising intelligent product of the week
Two pulse polls to gauge your team’s readiness
The Stack Is the Strategy
If you’ve ever added an AI feature and found the experience… underwhelming, you’re not alone.
Maybe the data wasn’t ready.
Maybe the model couldn’t adapt.
Maybe users didn’t trust it.
That’s not a model problem. It’s a stack problem.
The modern intelligent stack looks like this:
Signals → Raw inputs (clicks, messages, logs)
Silver Data → Cleaned, structured, enriched data
Gold Data → Curated, business-ready insights
Model Layer → LLMs, embeddings, classifiers
RAG → Retrieval from your data into the model
MLOps → Monitoring, retraining, observability
Agents → Decision-making AI that can act across apps
Interface → Where it all shows up to your users
When you stack it right, things work smoothly. When you don’t, even great models fail.
Real Use Case: Morgan Stanley’s Internal AI Assistant
Problem: Financial advisors at Morgan Stanley had too much research, too little time.
Solution: They built a GPT-powered assistant that surfaces answers from their own research reports. Using a retrieval-augmented model, every answer comes with citations and stays grounded in Morgan Stanley’s knowledge.
What’s smart here:
It uses RAG to avoid hallucinations
Pulls from a “gold” dataset (curated, vetted reports)
Integrates directly into the advisor’s workflow (Outlook, Zoom, etc.)
Outcome: 98% of advisors adopted the tool in just months. Not because of hype because it worked.
Real value isn’t in the model. It’s in the system around it.
Intelligent Product of the Week:
Zapier Agents (Beta)
Zapier launched something interesting: AI agents that act across 5,000+ apps.
You give it a goal (“when a lead comes in, summarize and notify sales”), and the agent pulls in live data, writes summaries, and sends messages all powered by an LLM and controlled by you.
Why it’s intelligent:
Understands goals via natural language
Performs tasks across multiple systems
Learns and adapts based on what it finds
Doesn’t just respond it acts
My take:
If “Excel macros” defined the early productivity boom, Zapier Agents might be the no-code AI employees of this next wave. It’s early, but promising.
Pulse
1. What’s the biggest bottleneck in your intelligent product stack right now?
2. Would you let an AI agent take action in your product or workflow?
Signals from the Field
RAG has gone mainstream
30–60% of enterprise AI deployments now use retrieval-augmented generation to ground model outputs in up-to-date content. (Source: OpenAI, LangChain reports)
→ Andrew Ng on Data-Centric AI
“Data is food for AI.”
Smart models don’t mean much without high-quality, structured data.
→ The Stack is Converging
Product, data, ML, and UX are finally merging into a single strategic layer. And those who get this right? They’re building defensible products, not just features.
One Thought to Leave
We talk about AI like it’s a mind.
But maybe it’s more like a muscle.
What makes it useful isn’t how smart it is.
It’s what we train it on, what we connect it to, and what we trust it to do.
The stack is the body.
The model is just the brain.
Treat the whole system with care.
What’s your favorite intelligent product or use case?
Respond in this week’s pulse check.
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Forward this to someone building with data, AI, or product in mind.