Agents-internal

About this Agent

The CAPEX Compliance Monitoring Agent is an AI-driven digital worker designed to oversee capital expenditure projects meticulously. This intelligent agent ensures that all CAPEX projects stay within the approved budgets and adhere to the set timelines. By continuously monitoring project progress, it identifies and flags any deviations from the planned budget or schedule, allowing for timely reviews and interventions. This ensures efficient capital management and helps prevent cost overruns and project delays.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for CAPEX Compliance Monitoring Agent:

Capex projects

Project_ID	Project_Name	Budget_Approved	Actual_Spend	Schedule_Start	Schedule_End	Actual_End
101	Upgrade Manufacturing Plant	500000	480000	2023-01-10	2023-06-30	2023-06-25
102	New Office Building	1500000	1600000	2023-02-01	2023-12-15	2024-01-10
103	IT Infrastructure Overhaul	800000	790000	2023-03-15	2023-08-30	2023-08-28
104	Warehouse Expansion	1200000	1250000	2023-04-20	2023-09-15	2023-09-18

Deliverable Example

Sample output delivered by the CAPEX Compliance Monitoring Agent:

Project_ID	Project_Name	Deviation_Type	Budget_Approved	Actual_Spend	Schedule_End	Actual_End
101	Upgrade Manufacturing Plant	Under Budget	500000	480000	2023-06-30	2023-06-25
102	New Office Building	Over Budget & Delayed	1500000	1600000	2023-12-15	2024-01-10
104	Warehouse Expansion	Over Budget & On Time	1200000	1250000	2023-09-15	2023-09-18

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.

CAPEX Compliance Monitoring Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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