GenAI Platform
Build, test, and optimize Generative AI applications that unlock the value of your data.
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Optimize LLMs For Your Most Important Use Cases
Optimize LLM performance for your domain-specific use cases with our advanced retrieval augmented generation (RAG) pipelines, state-of-the-art test and evaluation platform, and our industry-leading ML expertise.
Custom CopilotsIncrease Employee Productivity
Boost employee productivity with custom Copilots that use optimized retrieval augmentation (RAG) to accurately summarize and cite your knowledge base.
Customer SupportEnhance Customer Experience
Improve customer satisfaction, retention, and conversion with helpful chatbots, which use models fine-tuned to your data and reliably act as an extension of your brand.
Data AnalysisExtract Insights
Turn every employee into a data scientist by enabling them to use natural language to extract insights from data using Scale’s state-of-the-art Text2SQL technology.
Your Data, Any Model, Your Cloud
Accelerate and scale your Generative AI journey with the full-stack platform to build, test, and deploy enterprise-ready Generative AI applications, customized with your own data. Equip your entire organization to build optimized solutions using your data, with any model, on your cloud.
Your Data
Optimizing LLMs starts with your data. Connect popular data sources and transform your data with the Scale Data Engine to implement optimized RAG pipelines and models fine-tuned for your domain-specific tasks.
Any Model
Customize, test, and deploy all major closed and open-source foundation, embedding, and reranking models from OpenAI, Cohere, Meta, and more.
Your Cloud
Securely customize and deploy enterprise-grade Generative AI applications in your own VPC, including AWS and Azure. Now available on the Azure Marketplace.
Scale GenAI Platform
Scale GenAI Platform (Scale GP) is the platform to build optimized Generative AI applications. Test models and applications, implement advanced RAG pipelines, and fine-tune models to optimize performance. Accelerate your time to production and scale your Generative AI use cases across your organization.
Advanced Retrieval Augmented Generation (RAG) Tools
LLMs can accurately reference your knowledge base with Scale’s tools for optimized Retrieval Augmented Generation (RAG).
Convert knowledge base data into embeddings to serve as long-term memory, which the model can retrieve.
Our comprehensive toolset includes data connectors, custom embedding models, vector stores, chunk summarization, chunk and metadata extraction, advanced reranking, and RAG and reranker fine-tuning.
Test and Evaluate
Generative AI Applications
Optimize the performance of your applications by testing different data, prompts, RAG pipelines, models, and fine-tuning strategies.
Compare and evaluate base models and customized completion, embedding, and reranking models to determine the best model mix for your use case.
Perform automated and human-in-the-loop benchmarking of the performance, reliability, and safety of your customized models or entire Generative AI applications.
Create and manage test cases, define evaluation metrics, perform evaluations with subject matter experts, and analyze evaluation results.
Custom Model Builder
Fine-tune LLMs using your proprietary data or Scale expert data to improve performance & reliability on your unique use cases, while reducing latency and token consumption.
Choose from any leading closed or open-source foundation models, including OpenAI’s GPT-4 Cohere’s Command, and Meta’s Llama 2.
Leverage the Scale Data Engine to transform your data, and generate the highest quality training data for any use case.
Deployments
Deploy, manage, and monitor your custom models with enterprise-grade safety and security built-in.
Easily create new deployments with the required compute resources, adjust settings, and toggle deployment status.
Monitor token consumption and API calls with convenient dashboards for all deployments or individual model instances.
Enterprise-Ready
Securely customize and deploy Generative AI applications in your own VPC, including AWS and Azure.
Enterprise-grade RBAC and SAML SSO built-in.
Secure centralized management of API keys.
Why Scale GenAI Platform?
We built Scale GenAI Platform to help enterprises make more cost-effective investments, realize value from more use cases, and accelerate the time it takes to build, test, and deploy customized Generative AI applications.
Unlock the value of your data and experts
Scale transforms your proprietary data assets into powerful training datasets, and delivers customized models that are tailor-made for your unique use cases.
For retrieval augmented generation (RAG), manage and sync all your data at the source using ETL jobs.
Tap into Scale’s industry-leading data expertise
Leverage the Scale Data Engine to transform your data, and generate the highest quality training data for any use case.
GenAI Platform’s tooling enables subject matter experts to train, align, and evaluate models.
Maximize performance with customized models
Improve model performance on your domain-specific tasks, reduce hallucinations, and increase accuracy.
Work with Scale to fine-tune embedding, reranking, and completion models.
Focus on scaling to more use cases, not building infrastructure
Scale GenAI Platform does all the heavy lifting by providing streamlined and centrally managed infrastructure to accelerate use cases into production and effortlessly scale up the number of Generative AI applications.
Avoid lock-in and remain model agnostic
Agile and scalable infrastructure to train, host, and monitor any model.
Scale GenAI Platform is the only enterprise platform that supports ALL major commercial and open-source models including those from OpenAI, Cohere, Meta, MPT, and Mistral.
Test and eval for safe and responsible AI
Be confident that your customized models are as safe and responsible as possible with human-in-the-loop testing, evaluation, and monitoring.
Keep your enterprise data in your VPC where it is private and secure.
Resources
We've worked with the world's leading AI teams for years, and delivered more high-quality data than anybody else.