Generative AI Archives

Vibe coding

by ZBrain | Mar 11, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights What is vibe coding? AI-powered software development explained Listen to the article Your browser does not support the audio element. Vibe coding is transforming how software is written. Coined by AI expert Andrej Karpathy, the term refers to a...

AI in order management

by ZBrain | Mar 6, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights AI in order management: Scope, integration, use cases, challenges and future outlook Listen to the article Your browser does not support the audio element. The order management process is at the core of business operations, encompassing critical...

AI in record-to-report

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

← All Insights AI in record-to-report: Scope, integration, use cases, challenges and future outlook Listen to the article Your browser does not support the audio element. The record-to-report (R2R) process, encompassing essential financial operations such as journal...

Generative AI for billing

by ZBrain | Feb 24, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights Generative AI for billing: Scope, integration approaches, use cases, challenges, best practices and future trends Listen to the article Your browser does not support the audio element. In today’s fast-paced business environment, billing...

CUA models

by ZBrain | Feb 14, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights Computer-using agent (CUA) models: Redefining digital task automation Listen to the article Your browser does not support the audio element. As artificial intelligence evolves, its ability to interact with digital environments is reaching new levels of...

AI in procure-to-pay processes

by ZBrain | Feb 13, 2025 | Generative AI, Generative AI Applications, Generative AI Use Cases

← All Insights AI in procure-to-pay processes: Scope, integration, use cases, challenges and future outlook Listen to the article Your browser does not support the audio element. The procure-to-pay (P2P) process is at the heart of business operations, ensuring...

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.

Vibe coding

AI in order management

AI in record-to-report

Generative AI for billing

CUA models

AI in procure-to-pay processes

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