Agents-internal

About this Agent

The Project Timeline Generation Agent is an advanced AI digital worker designed to revolutionize project planning and execution. By leveraging AI-driven scheduling algorithms, this agent automatically generates detailed and accurate project timelines. It considers the project scope, milestones, and deadlines to provide a comprehensive timeline that ensures efficient project management. This tool is essential for IT project managers looking to streamline processes and enhance productivity.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Project Timeline Generation Agent:

Project Input

Task Name	Duration (days)	Dependency	Resource
Project Planning	5	Client Meeting	Project Manager
Requirement Gathering	10	Project Planning	Business Analyst
Technical Design	7	Requirement Gathering	Tech Lead
Development	20	Technical Design	Development Team
Testing	10	Development	QA Team
User Acceptance Testing	7	Testing	End Users
Deployment	3	User Acceptance Testing	Deployment Team
Project Closure	2	Deployment	Project Manager

Deliverable Example

Sample output delivered by the Project Timeline Generation Agent:

Task Name	Start Date	End Date	Duration (days)	Assigned Resource	Dependency	Status
Project Planning	2024-10-10	2024-10-14	5	Project Manager	None	Not Started
Requirement Gathering	2024-10-15	2024-10-24	10	Business Analyst	Project Planning	Not Started
Technical Design	2024-10-25	2024-10-31	7	Tech Lead	Requirement Gathering	Not Started
Development	2024-11-01	2024-11-20	20	Development Team	Technical Design	Not Started
Testing	2024-11-21	2024-11-30	10	QA Team	Development	Not Started
User Acceptance Testing	2024-12-01	2024-12-07	7	End Users	Testing	Not Started
Deployment	2024-12-08	2024-12-10	3	Deployment Team	User Acceptance Testing	Not Started
Project Closure	2024-12-11	2024-12-12	2	Project Manager	Deployment	Not Started

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.

Project Timeline Generation Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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