Agents-internal

About this Agent

The Revenue Forecasting Agent is an AI-driven digital assistant tailored to refine and boost the precision of revenue projections. By utilizing historical data alongside present market trends, this sophisticated Agent autonomously creates detailed revenue forecasts. It aids financial analysts by delivering dependable insights, facilitating informed decision-making and strategic planning. With this AI Agent, businesses can foresee financial results and adapt their strategies proactively, maintaining a competitive edge in the market.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Revenue Forecasting Agent:

Revenue forecast sample

Date	Revenue ($)	Market Growth Rate (%)	Competitor Performance Index	Economic Indicator Index
2023-01-01	10000	3.2	1.1	0.8
2023-02-01	12000	2.8	1.3	0.9
2023-03-01	15000	3.5	1.4	1.0
2023-04-01	17000	4.1	1.6	1.1
2023-05-01	16000	3.9	1.5	1.0
2023-06-01	18000	4.3	1.7	1.2

Deliverable Example

Sample output delivered by the Revenue Forecasting Agent:

Date	Forecasted Revenue ($)
2023-07-01	19948.529411764706
2023-07-31	22107.990316224532
2023-08-30	24501.21639212302

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.

Revenue Forecasting Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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