Welcome to Objective Public Beta

October 4, 2024
Team Objective

If there’s ever been a big week here at Objective HQ, this week is it. Today we officially move the platform into Public Beta! 🎉

Before we jump into what that means to us and for you, we want to say thank you to all of you in the community that have started building this wild, weird new search future with us. From AI-powered fashion discovery, to food search, to art discovery, to really (really) good ecommerce search, to news search, we’re lucky to have a front-row seats to see what you all are building. And we’re so excited to explore this huge new frontier with you.

What is AI-native search?

It’s easiest to start with what came before “AI-native” search. The roots of search technology are in keyword-matching search. There are lots of ways to do keyword lookup search really well, and lots of companies that will help you do it. Increasingly, you’ll see these legacy platforms augmented with AI abilities.

AI-native search is platforms (like Objective!) that are built from the ground up around AI. We’ve found that this creates a fundamental difference in how search problems get solved — forcing us to ask much different strategic questions about how to build tools for folks who build search. That exploration has led us to some really interesting discoveries.

AI-native search is two components — an AI search core, and AI agents. What AI-native search gives your team is the ability to build, monitor and iterate search like any other critical part of your technology stack.

An AI Search Core with Learning at Every Layer

Fundamentally, every AI-native search platform starts with an AI Search Core. This is the abstraction layer that manages the heavy lifting of vectors & embeddings for the developer.

This starts with semantic understanding of what a user is searching for, and flows through the retrieval, ranking, and rendering of search results, with learning built in at every layer. Later this week we’ll be publishing another article that goes deeper on how all of our feature launches over the last year fit into this big vision.

AI Agents that Unlock the Black Box of Search

When you start with AI and learning as a foundation, you don’t stop looking for better answers, but you also start looking for better questions. Should we be building a better horse? Or should we be building a bicycle? Should we be educating every developer on loss curves? Or should we be building the Iron Man suit that every developer — and every business — needs in order to bend the loss curve toward the outcome they need?

The search landscape really starts to change when every developer — and every business — has access to an Iron Man suit for search that encodes decades of search tools & techniques. These agents can understand a company’s dataset, user search behavior, and let any engineering team manage the deep complexity of an AI Search Core.

From automatically evaluating search relevance at huge scale, to simulating user search behavior, to automating the end-to-end process of training finetuned models, it’s the combination of a powerful AI search core and agent intelligence that really starts to create new possibilities for developers.

The Result: Putting teams back in control of search quality

We encounter a lot of new customers at a common inflection point. They’ve implemented a legacy search solution or tried to work with barebones vector search, but eventually start to hit a wall trying to iterating search relevance. And feel like the only solution is brute-forcing the problem with more manual work.

What you get with AI-native search is search that works for you. Not the other way around.

Param Jaggi is the CEO of a company we work with called Agora, a really slick new marketplace experience. He described their situation - “We were coming from a self-hosted world, where we were dealing with hardware configuration, data sharding, memory management — all of those headaches. We think it probably would have taken us 6 months to a year to build on our own what we ended up integrating in 3 days with Objective.”

At it’s core, AI-native search is built to augment your team, not create new work. Another company we work with, Lilo, is able to leverage Objective’s platform as an extension of their engineering team.

Search that scales with you

In the past there’s been a strict divide between the search relevance that large, global enterprises can create, and the search relevance available to everyone else. It’s a divide that was created by an older generation of software that needed a ton of specialization and expense to deploy and operate it successfully. Unless you were a search engine or a Fortune 500, you were just stuck.

AI-native search is engineered for every scale. From a one person startup to the biggest search installations in the world, it’s built to understand your data, understand your users, and deliver the relevance you’d expect from the best search companies in the world. And it comes out-of-the-box with the tooling and intelligent agents to help your team build great user experiences.

For years now, companies and engineering teams have been asked to make a heartbreaking decision: deliver enterprise-grade search relevance, or iterate at the speed of a startup. You deserve both.

We’re Objective. We’re building AI-native search. And we can’t wait to see what you build!

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