Everyday

Spørsmål og svar og kunnskapsplattformer

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This system integrates your existing document sources into a compact, ground-truth knowledge base stored within an open large language model, enabling cost-effective and accurate retrieval-based answering. It offers features such as seamless connection to various cloud storage platforms, incremental updates to keep the knowledge base current, and a one-time document training process resulting in a flat per-query pricing model; it addresses challenges related to scalable, private, and affordable document-centric inquiry, making it ideal for organizations seeking persistent, grounded answers without recurring training costs.

Everyday finnes i Q&A & Knowledge Community Platforms kategorier.

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Detaljer

Everyday Hovedfunksjoner

• Scalable
  It can handle large amounts of data by splitting it into many parts, so it still works well with big collections.
• Easy to Use
  You can see how it works step by step: connect your sources, train once, and get grounded answers.
• Cost-Effective
  It costs about 16 times less per query compared to RAG, making it cheaper to use.
• Easy to Connect
  It works with common tools like SharePoint, Confluence, Google Drive, Amazon S3, and Notion, or you can upload files.
• Fast and Simple
  It quickly retrieves the right information from your documents with semantic search and gives clear answers.
• Grounded Answers
  Answers come directly from your own documents and include sources, ensuring reliable information.
• Incremental Sync
  It keeps your document database up-to-date automatically as your documents change.
• Private and Secure
  It runs in your own cloud, so your data stays private and is not shared or used to train other models.
• Read-Once Learning
  It learns from your documents only once and then answers questions forever without needing retraining.
• Runs in Your Cloud
  All processing happens inside your own cloud environment, avoiding shared models.
• Transparent and Predictable Costs
  It has a flat, predictable cost per question that stays the same as your data grows.

Merker

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