GCP – From query to cart: Inside Target’s search bar overhaul with AlloyDB AI

Editor’s note: Target set out to modernize its digital search experience to better match guest expectations and support more intuitive discovery across millions of products. To meet that challenge, they rebuilt their platform with hybrid search powered by filtered vector queries and AlloyDB AI. The result: a faster, smarter, more resilient search experience that’s already improved product discovery relevance by 20% and delivered measurable gains in performance and guest satisfaction.

The search bar on Target.com is often the first step in a guest’s shopping journey. It’s where curiosity meets convenience and where Target has the opportunity to turn a simple query into a personalized, relevant, and seamless shopping experience.

Our Search Engineering team takes that responsibility seriously. We wanted to make it easier for every guest to find exactly what they’re looking for — and maybe even something they didn’t know they needed. 

That meant rethinking search from the ground up. 

We set out to improve result relevance, support long-tail discovery, reduce dead ends, and deliver more intuitive, personalized results.

As we pushed the boundaries of personalization and scale, we began reevaluating the systems that power our digital experience. That journey led us to reimagine search using hybrid techniques that bring together traditional and semantic methods and are backed by a powerful new foundation built with AlloyDB AI.

Hybrid search is where carts meet context

Retail search is hard. You’re matching guest expectations, which can sometimes be expressed in vague language, against an ever-changing catalog of millions of products. Now that generative AI is reshaping how customers engage with brands, we know traditional keyword search isn’t enough.

That’s why we built a hybrid search platform combining classic keyword matching with semantic search powered by vector embeddings. It’s the best of both worlds: exact lexical matches for precision and contextual meaning for relevance. But hybrid search also introduces technical challenges, especially when it comes to performance at scale.

Choosing the right database for AI-powered retrieval

Our goals were to surface semantically relevant results for natural language queries, apply structured filters like price, brand, or availability, and deliver fast, personalized search results even during peak usage times. So we needed a database that could power our next-generation hybrid search platform by supporting real-time, filtered vector search across a massive product catalog, while maintaining millisecond-level latency even during peak demand. 

We did this by using a multi-index design that yields highly relevant results by fusing the flexibility of semantic search with the precision of keyword-based retrieval. In addition to retrieval, we developed a multi-channel relevance framework that dynamically modifies ranking tactics in response to contextual cues like product novelty, seasonality, personalization and other relevance signals.

We had been using a different database for similar workloads, but it required significant tuning to handle filtered approximate nearest neighbor (ANN) search at scale. As our ambitions grew, it became clear we needed a more flexible, scalable backend that also provided the highest quality results with the lowest latency. We took this problem to Google to explore the latest advancements in this area, and of course, Google is no stranger to search!

AlloyDB for PostgreSQL stood out, as Google Cloud had infused the underlying techniques from Google.com search into the product to enable any organization to build high quality experiences at scale. It also offered PostgreSQL compatibility with integrated vector search, the ScaNN index, and native SQL filtering in a fully managed service. That combination allowed us to consolidate our stack, simplify our architecture, and accelerate development. AlloyDB now sits at the core of our search system to power low-latency hybrid retrieval that scales smoothly across seasonal surges and for millions of guest search sessions every day while ensuring we serve more relevant results.

Filtered vector search at scale

Guests often search for things like “eco-friendly water bottles under $20” or “winter jackets for toddlers.” These queries blend semantic nuance with structured constraints like price, category, brand, sizes or store availability. With AlloyDB, we can run these hybrid queries that combine vector similarity and SQL filters easily without sacrificing speed or relevance.

Thanks to recent innovations in AlloyDB AI, including optimized filtered vector search and adaptive query filtering, we’ve seen:

• up to 10x faster execution compared to our previous stack

• product discovery relevance improved by 20%

• halved the number of “no results” queries

These improvements have extended deeper into our operations. We’ve reduced vector query response times by 60%, which resulted in a significant improvement in the guest experience. During high-traffic events, AlloyDB has consistently delivered more than 99.99% uptime, providing us with the confidence that our digital storefront can keep pace with demand when it matters most. Since search is an external –facing, mission-critical service, we deploy multiple AlloyDB clusters across multiple regions, allowing us to effectively achieve even higher effective reliability. These reliability gains have also led to fewer operational incidents, so our engineering teams can devote more time to experimentation and feature delivery.

AlloyDB’s cloud-first architecture and features give us the flexibility to handle millions of filtered vector queries per day and support thousands of concurrent users – no need to overprovision or compromise performance.

Building smarter search with AlloyDB AI

What’s exciting is how quickly we can iterate. AlloyDB’s managed infrastructure and PostgreSQL compatibility let us move fast and experiment with new ranking models, seasonal logic, and even AI-native features like:

• Semantic ranking in SQL: We can prioritize search results based on relevance to the query intent.

• Natural language support: Our future interfaces will let guests search the way they speak – no more rigid filters or dropdowns.

AlloyDB features state-of-the-art models and natural language in addition to the state-of-the-art ScaNN vector index. Google’s commitment and leadership in AI infused in AlloyDB has given us the confidence to evolve our service together with pace of the overall AI & data landscape.

The next aisle over: What’s ahead for Target

Search at Target is evolving into something far more dynamic – an intelligent, multimodal layer that helps guests connect with what they need, when and how they need it. As our guests engage across devices, languages, and formats, we want their experience to feel seamless and smart.

With AlloyDB AI and Google Cloud’s rapidly evolving data and AI stack, we’re confident in our ability to stay ahead of guest expectations and deliver more personalized, delightful shopping moments every day.

Note from Amit Ganesh, VP of Engineering at Google Cloud :

Target’s journey is a powerful example of how enterprises are already transforming search experiences using AlloyDB AI. As Vishal described, filtered vector search is unlocking new levels of relevance and scale. At Google Cloud, we’re continuing to expand the capabilities of AlloyDB AI to support even more intelligent, agent-driven, multimodal applications. Here’s what’s new:

• Agentspace integration: Developers can now build AI agents that query AlloyDB in real time, combining structured data with natural language reasoning.
• AlloyDB natural language: Applications can securely query structured data using plain English (or French, or 250+ other languages) backed by interactive disambiguation and strong privacy controls.
• Enhanced vector support: With AlloyDB’s ScaNN index and adaptive query filtering, vector search with filters now performs up to 10x faster.
• AI query engine: SQL developers can use natural language expressions to embed Gemini model reasoning directly into queries
• Three new models: AlloyDB AI now supports Gemini’s text embedding model, a cross-attention reranker, and a multimodal model that brings vision and text into a shared vector space.

These capabilities are designed to accelerate innovation – whether you’re improving product discovery like Target or building new agent-based interfaces from the ground up.

Learn more:

• Google Cloud databases supercharge the AI developer experience

• AlloyDB AI drives innovation for application developers

• Discover how AlloyDB combines the best of PostgreSQL with the power of Google Cloud in our latest e-book.

• Try AlloyDB at no cost for 30 days with AlloyDB free trial clusters!

• Learn more about AlloyDB for PostgreSQL.