Building Intelligent Consumer Experiences

Consumer software has a discovery problem. Not a data problem — there is more product data available today than any single person could process in a lifetime. The problem is that the interfaces designed to surface that data were built for a different era, and most of them haven't caught up.

Search boxes return keywords. Filters narrow by attributes. Recommendation engines push what's popular or what generates margin. None of this is what a thoughtful friend with domain expertise would do. A thoughtful friend would ask what you already own. They'd understand the difference between "I need a winter coat" from someone who walks to work in Chicago and someone who hikes in New Mexico. They'd know which brands run small, which are overrated, and which ones quietly make the best product in a category without much fanfare.

That gap — between the interfaces that exist and the experience that's actually possible — is what Starseek is building toward.

Discovery should be conversational

The best product recommendation you've ever received probably came from a conversation. Someone asked the right questions. They listened. They synthesized what they heard and came back with a suggestion that was specific, reasoned, and useful.

Most digital discovery experiences don't work this way. They're designed for retrieval, not reasoning. You provide inputs; they return outputs. The system doesn't ask follow-up questions. It doesn't weigh trade-offs. It doesn't say "those two things you want are actually in tension — here's how to think about that."

We believe conversational AI, when properly grounded in structured product data, can close this gap. The key phrase is grounded in structured data. A language model that's reasoning about products purely from training knowledge will hallucinate specifications, confuse model years, and invent features that don't exist. The conversational layer has to be anchored to accurate, real-time product information — which means the search and retrieval infrastructure underneath it matters as much as the interface on top.

Getting this right requires serious engineering across both dimensions: the quality of the retrieval system and the quality of the reasoning layer. We're investing in both.

Comparison is broken

Comparison shopping is one of the most common behaviors in consumer decision-making and one of the worst-supported by current tools. The typical experience involves opening ten browser tabs, reading ten different product pages with ten different information architectures, and trying to hold all of it in your head while you decide.

There are comparison tools, but most are narrow in scope, outdated in their data, or optimized for affiliate revenue rather than user clarity. They tell you that Product A has a higher star rating than Product B without explaining what the reviewers who left those ratings were actually evaluating.

We think comparison should work across brands and across stores, normalized to the attributes that actually matter for a given category, enriched with structured review signals, and presented in a way that maps to how humans actually make decisions. That's harder to build than a side-by-side spec table. It requires understanding which attributes are meaningful in which contexts, how to weight reviewer credibility, and how to surface the right level of detail without overwhelming the user.

The goal isn't a better spreadsheet. It's a system that helps you think.

Personalization that earns trust

Personalization has a reputation problem. For many users, it means ads that follow them around the internet. It means recommendations that feel invasive or opaque. It means the platform knowing things about you that you didn't consciously share.

We think personalization can work differently. The kind we're building is transparent and functional: the system gets better at helping you because it understands your preferences, price range, past decisions, and context — not because it's constructing a behavioral profile to maximize engagement.

The distinction matters. Engagement-optimized personalization is designed to keep you in the app. Decision-optimized personalization is designed to get you to the right answer faster, even if that means the interaction is short. We're building for the second kind, because we think it's what users actually want, and because it's what builds a product people trust enough to use for consequential purchases.

Personalization also has to work at cold start — before the system has learned much about you. That requires good defaults, clear preference-setting mechanisms, and a model that makes reasonable inferences from minimal signals without overreaching.

The interface layer matters

It's easy to underweight design in a product that's fundamentally technical. But the interface is where users decide whether to trust the system, whether to engage with its output, and whether to come back.

Conversational interfaces introduce specific design challenges. How do you show sources? How do you let users correct the system when it misunderstands them? How do you handle ambiguity without making the interaction feel like an interrogation? How do you present structured product information — prices, specs, availability — inside a conversational flow without breaking the experience?

These are not solved problems. They require careful iteration and genuine design thinking, not just UI polish. We're approaching them as first-class product problems, not afterthoughts to the AI infrastructure.

Why this, why now

The technical conditions for building this kind of product have only recently come together. Retrieval systems are fast enough and accurate enough to support real-time, multi-source product search. Language models are capable enough to reason about trade-offs and conduct coherent multi-turn conversations. The tooling for building and evaluating AI systems has matured enough to support serious product iteration.

We're not building features on top of existing commerce infrastructure. We're rethinking what the interface between people and products should look like when you start from user intent rather than category pages.

That's a large surface area, and we're approaching it methodically — with strong technical foundations, a design sensibility that respects the user's time and intelligence, and a view of personalization that's grounded in usefulness rather than retention metrics.

We're early. We're serious. And we're just getting started.