Orbis
From Signals to Understanding; Building the AI Personalization Layer
How a memory architecture reduced time-to-insight from 6–8 hours to under 15 seconds.
Context
Product Type
AI personalization and memory layer
Scale
Led 60+ people across Product, Data Science, Applications, Platform, Design, and QA
Role
Product Lead: drove vision, built the prototype, aligned 6 teams, restructured org into pods
The Bridge:
Before Orbis, users were the personalization layer. Every insight manually connected, every pattern manually recognized, every report written from scratch. This is the same problem facing every AI product today, not a lack of data, but a lack of synthesis. The same memory architecture powering Orbis applies directly to consumer personalization, content recommendation, and AI-driven engagement across sports, media, and commerce.
The Real Problem
What we initially believed:
The path forward was smarter individual products, better models, richer signals per tool. More model capability meant better outputs.
What we realized:
We didn't have a model problem. We had an infrastructure problem. Without shared memory, every product was stateless. Every user was a stranger to the system every single session.
Users could see individual signal, but they couldn’t:
Pick up where they left off, every session started from zero
Get context on why something mattered now versus before
Trust the system to surface what was relevant without going to find it themselves
The risk wasn’t underpowered products. The risk was overwhelming users with intelligence they couldn’t synthesize.
The Moment: Personalization isn't a feature. It's a foundation. When you give a system memory, context, and the ability to connect what it knows, it stops being a tool and starts being a thinking partner.
The Strategic Tradeoff
We faced a foundational decision:
Option A:
Continue embedding intelligence separately inside each product
Faster short-term wins
Increasing fragmentation over time
Option B:
Invest in a shared reasoning layer above all products
Slower initial delivery
Clear path to coherence, reuse, and scale
…
We chose Option B, invest in a shared reasoning layer above all products
The pushback was not about vision, but readiness.
Data Science wanted to build foundational models. I thought we needed to build AI infrastructure. I didn't win that argument with a slide deck, I pulled two engineers and built a prototype on our actual data. When we could show the system connecting signals, tracking change over time, and surfacing what mattered next, the room changed. After that I restructured the teams into pods: memory architecture, guardrails and security, data orchestration, application layer. 60+ people moving in parallel without stepping on each other.
We codified a platform standard:
Personalization is a shared capability, not a local feature…
Once this decision was made, local intelligence implementations were deprecated by design
This became a structural requirement, ensuring coherence at scale.
Orbis unified memory, context, and reasoning across products instead of duplicating intelligence inside each one.
What that meant in practice:
We prioritized:
Shared memory layer: episodic context, knowledge graph, vector retrieval, one foundation all products inherit
Personalized surfacing: alerts, recommendations, and summaries routed to the right user at the right time
User annotations as signal: when a user flagged something, that judgment became part of the system
We deferred:
Polished UI surfaces: the memory worked before users could see it working
Per-product model optimization: foundation first, features second
The goal was not solving insight problems one product at a time and instead designed a foundation that could unify memory, context, and interpretation across the platform. The short-term cost was speed; the long-term gain was coherence.
Outcome
This decision set a precedent for how platform bets are evaluated, and would have been the failure point had coherence not emerged.
Impact:
6–8 hours → under 15 seconds time-to-insight
60+ person org restructured around platform architecture
$300M pipeline - Orbis was a core differentiator in deals
Multiple products shipped personalization without rebuilding memory logic
Org strategy shifted from model-first to infrastructure-first permanently
What I’d Do Differently
I'd have built visible UI surfaces earlier. The memory was working before users could see it working which slowed trust. Earlier touchpoints showing the system's memory in action would have built confidence faster and shortened the feedback loop.
Orbis defines how I think about AI personalization. Site Sentry shows how that thinking shipped under real-world constraints.