The integration process is quick. You can connect your data to Shaped in minutes, train your first model in hours, and fully integrate into your app in days.

Prior to discovering Shaped many of our customers were using Algolia or AWS Personalize. To learn why they switched you can browse a short summary below, read a case study describing the challenges they faced or simply know that we have in many cases doubled the performance of these products.  
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1. Multi-modal unstructured data understanding
Shaped uses state-of-the-art AI technologies like transformers and Large Language Models (LLMs) to make complex data types, including text, images, and video, usable for ranking systems. Using all types of your data significantly improves ranking performance. Algolia and AWS Personalize don't support unstructured data understanding.

2. Built for data scientists and ML engineers
Shaped connects directly to all of your data warehouses and application stores. This means no extra work for you and instant deployment via our CLI. In contrast, Algolia and AWS Personalize require manual transformations and for you to push your data. This adds extra work for the initial setup and ongoing maintenance.

3. Real-time ranking systems
Shaped specializes in real-time ranking systems. This means you can easily use ranking in dozens of places throughout your company, products and apps. Algolia primarily focuses on search, while AWS Personalize offers a batch based recommendation system.

4. White-glove support
We’re as hands on as you’d like us to be with your data, offering advice and helping you build your models.
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For more information see these blog posts comparing Shaped with Algolia and Shaped with AWS personalize.

Certainly! Shaped is designed to be used for all of your ranking use-cases. Typically the companies we work with deploy dozens. Once your data is connected to Shaped creating additional ranking models is easy. See our docs for more information.

There is no minimum amount of data required. Collecting interactions, for example clicks, views and or impressions is the only requirement.
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For more information see this blog post How much data do I need for a recommendation system?

The high cost of hiring multiple machine-learning engineers, the long time required to build and the on-going full-time maintenance required. We handle scalability and reliability without the worries. Shaped can take you from 0 to 1 in a few days at a fraction of the cost.

Our pricing is flat-fee monthly determined by usage. Once we understand your approximate number of monthly active users, items counts, and particular implementation details, we’ll be able to provide a pricing estimate.

Shaped was built from the ground-up with security as a top priority. We operate as a cloud software-as-a-service (SaaS) platform, and only retrieve from your connected datastores when necessary to build your ranking algorithms. After training, most of your data is discarded, other than specific non-identifiable encoded features that are used at inference. We only require read access to your datasets and customer data never has to be persisted within Shaped. Furthermore, Shaped works on encrypted data if you need an added layer of security.

Shaped uses best physical, virtual, network and operational security practices. We rely on role-based authentication for all data access and records audit logs for every action. We avoid data replication and have clear multi-tenant isolation policies to ensure sensitive and critical systems are separated. Shaped uses isolated VPCs for all production deployments. Data is encrypted at rest using AES-256 encryption or higher and all ingress and egress traffic is encrypted via TLS 1.2+.

For more information see here.