AI built into your product's DNA
The architect
I’m Stephan Singh Ryatt — 3x founder and architect of AI-native systems.
To me, product coherence isn’t a byproduct — it’s a commitment. That’s why I work across strategy, architecture, UX, and code — I see them as aspects of the same design process.
I’ve built AI-native systems that bring clarity to complex, human-centered domains—including a psychographic study program recommender for aspiring students, an AI storytelling tool for nonprofits and an astrology interpretation engine for spiritual seekers.
I build from first principles: what’s real, what's emerging, and what the system needs to evolve. My background in astrology taught me to recognize patterns — and to work with them, instead of copy / pasting templates and frameworks.
This allows me to build what I call product-native intelligence: AI systems that simply fit — because they emerge from what’s already true. They're not bolted-on. And they're not chasing trends.
I embed them deeply into your product: into your flows, give them access to your user data, and teach them how to work with your data model.
Simply put, my AI systems enable your product to think with you.
The ākāśa
Every one of my build starts here — beneath architecture, UX and code — in the subtle field where your product's true signal emerges from:
- The core purpose it’s built around
- Your user's raison d'être
- The problems you're solving for them
And it’s where we surface what’s ready to emerge:
- The hidden patterns in your user's data
- The friction in their key flows
- The missing layer your product’s ready for
We don’t begin with features. We begin with looking at what's real.
From there, we shape native intelligence into your product — an AI system that doesn’t just works. It feels like it was always meant to be there.
We make your product AI-native.
The patterns
Intent-to-decision guide
What it is:
An embedded AI system that helps users choose — by turning vague intent into crisp decisions. Think: onboarding flows, complex settings, or branching product paths — guided by brand-aligned dialogue.
Why it matters:
It reduces friction at critical junctures, boosts clarity, and builds user trust. Instead of guessing or abandoning, users move forward with confidence.
From “I think I want to…” to “This is what I need.”
Intent-to-personalization
What it is:
An embedded AI layer that dynamically adjusts product flows, defaults, or pathways — based on user traits, context, and intent.
Why it matters:
Builds instant resonance by shaping the product around the user — not the other way around. Reduces friction, boosts clarity, and scales the feeling of “this was made for me.”
Your product meets them where they are — and moves with them as they grow.
User-sourced idea synthesizer
What it is:
An AI system that clusters user feedback, feature requests, and open-ended input — turning signal into roadmap-ready insights.
Why it matters:
Reveals patterns your team might miss, sharpens prioritization, and aligns your roadmap with what users are really asking for.
Where raw feedback becomes product clarity.
Embedded knowledge chat
What it is:
An AI layer that reflects user progress, choices, or patterns back to them — helping them see how far they've come, and what’s next.
Why it matters:
Reinforces progress, deepens engagement, and drives retention — by making users feel the value they’ve already created.
What they’ve done becomes why they come back.
Contextual guide
What it is:
An AI assistant embedded inside your product — one that knows where the user is, what they’re trying to do, and offers real-time help.
Why it matters:
Guides users through complex interactions, reduces hesitation, and prevents drop-off. Builds trust by showing up with just the right nudge — at just the right time.
Help, when and where it matters most.
Signal-to-reflection loop
What it is:
An AI layer that mirrors meaningful user patterns — showing progress, highlighting choices, and surfacing what’s ready to evolve.
Why it matters:
Turns past actions into present insight. Builds trust and retention by making the user’s own signal visible, valuable, and actionable.
They don’t just see where they’ve been — they see where they’re going.
Signal-to-clarity synthesizer
What it is:
An AI system that takes in scattered, messy, or multimodal inputs—voice notes, Looms, transcripts, threads—and distills them into structured clarity: briefs, specs, or next steps.
Why it matters:
Unblocks product momentum by removing ambiguity, speeding up decisions, and freeing up founder bandwidth. Your team gets what they need—without you repeating yourself.
Structured output. Zero re-explaining.
Intent-to-structure composer
What it is:
An AI layer that transforms vague user intent into structured output — like a content plan, a database schema, or a multi-step action flow.
Why it matters:
It removes the friction between thought and execution — helping users go from “I think I need…” to a fully scaffolded artifact they can run with.
Fast. From fuzzy ask to structured action.
Your next step
In this 45-minute call we'll explore where your product is headed — and how AI can support it's trajectory. I'll get to know your product, your users and the problems you are solving for them. We'll uncover the AI use cases that actually move the needle.
You don't need a brief or a strategy. Just a willingness to look deeper and forward.
