Generative AI Use Cases Archives

How ZBrain enhances knowledge retrieval with intelligent reranking

by ZBrain | May 5, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights How ZBrain enhances knowledge retrieval with intelligent reranking Listen to the article Your browser does not support the audio element. Enterprise search systems, which enable employees to locate information across internal documents, databases, and...

Monitoring ZBrain AI agents: Exploring key metrics

by ZBrain | Apr 29, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights Monitoring ZBrain AI agents: Significance, key metrics, best practices, benefits and future trends Listen to the article Your browser does not support the audio element. In today’s digital era, AI agents are no longer just an option—they’re...

How ZBrain’s multi-agent collaboration works

by ZBrain | Apr 23, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights ZBrain’s multi-agent collaboration framework: How autonomous agents team up to solve complex enterprise challenges Listen to the article Your browser does not support the audio element. Multi-agent collaboration in AI refers to systems where multiple...

ZBrain’s modular stack for custom AI solutions

by ZBrain | Apr 16, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights Building blocks of AI: ZBrain’s modular stack for custom AI solutions Listen to the article Your browser does not support the audio element. In the contemporary digital era, enterprises increasingly invest in artificial intelligence (AI) to maintain...

AI in project and capital expenditure management

by ZBrain | Apr 1, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights AI in project and capital expenditure management (CapEx): Scope, integration, use cases, challenges and future outlook Listen to the article Your browser does not support the audio element. Project and capital expenditure (CapEx) management is...

AI for plan-to-results

by ZBrain | Mar 27, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights AI for plan-to-results (P2R): Scope, integration, use cases, challenges and future outlook Listen to the article Your browser does not support the audio element. Plan-to-results is a critical component of financial operations, encompassing functions...

ZBrain credits and their usage

A credit is a unit of usage on ZBrain. Credits are consumed whenever you perform actions such as embedding documents or querying an app. Here’s how credits are utilized:

Document Embedding:

When you upload a document, it is processed and embedded to create a searchable vector representation. This step consumes credits based on the size of the document and the embedding model used.

Input Processing:

When you ask a question, the relevant context is retrieved from the embeddings and processed by the Large Language Model (LLM). This step incurs a credit cost based on the model and the number of input tokens.

Generating Output

The LLM then generates a response based on the processed input, consuming additional credits based on the number of output tokens.

The cost in credits for various models and processes is detailed below:

Models and Credit Consumption:

Model	Input Cost	Output Cost
GPT-4o	2,500 credits / 1M tokens	7,500 credits / 1M tokens
GPT-4	15,000 credits / 1M tokens	30,000 credits / 1M tokens
GPT-4-32k	30,000 credits / 1M tokens	60,000 credits / 1M tokens
GPT-3.5 Turbo	250 credits / 1M tokens	750 credits / 1M tokens

Embedding Models:

Model	Input Cost
text-embedding-3-small	20 credits / 1M tokens
text-embedding-3-large	130 credits / 1M tokens
ada v2	100 credits / 1M tokens

When you create a knowledge base or query an app, credits are deducted based on the embedding, input, and output token usage. This ensures you only pay for what you use while leveraging the full power of advanced AI models.

How ZBrain enhances knowledge retrieval with intelligent reranking

Monitoring ZBrain AI agents: Exploring key metrics

How ZBrain’s multi-agent collaboration works

ZBrain’s modular stack for custom AI solutions

AI in project and capital expenditure management

AI for plan-to-results

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