Finance and Banking Archives

AI-driven Contract Analysis for Informed Financial Decision-making and Risk Reduction

by ZBrain | Nov 22, 2023 | Finance and Banking

Finance and Banking AI-driven Contract Analysis for Informed Financial Decision-making and Risk Reduction Utilizing ZBrain for Enhanced Contract Management Efficiency Book a Demo Example Report Problem Time-consuming Manual Contract Analysis In the finance and banking...

Competitor Insights Unleashed: AI-powered Competitor Analysis for Strategic Decision-making

by ZBrain | Nov 20, 2023 | Finance and Banking

Finance and Banking Competitor Insights Unleashed: AI-powered Competitor Analysis for Strategic Decision-making Leveraging ZBrain for Profound Insights and Strategic Edge in Competitive Landscapes Book a Demo Example Report Problem Complexities in Conventional...

Transforming Credit Scoring Using AI

by ZBrain | Nov 2, 2023 | Finance and Banking

Finance and Banking Transforming Credit Scoring Using AI Optimizing Credit Scoring Analysis With ZBrain Flow Book a Demo Example Report Problem Challenges in Traditional Credit Scoring Practices Credit scoring is a critical aspect of the finance and banking industry,...

Strengthening Financial Regulatory Compliance Using AI

by ZBrain | Nov 2, 2023 | Finance and Banking

Finance and Banking Strengthening Financial Regulatory Compliance Using AI Ensure Seamless Financial Regulatory Compliance With ZBrain Flow Book a Demo Example Report Problem Challenges in Regulatory Compliance Monitoring In the finance and banking industry, adhering...

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.

AI-driven Contract Analysis for Informed Financial Decision-making and Risk Reduction

Competitor Insights Unleashed: AI-powered Competitor Analysis for Strategic Decision-making

Transforming Credit Scoring Using AI

Strengthening Financial Regulatory Compliance Using AI

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