Agents-internal

About this Agent

The Procurement Spend Analysis Agent is a powerful AI digital worker designed to streamline and optimize procurement expense management. By analyzing procurement expenses across various categories and vendors, this agent provides valuable insights into spending patterns and identifies opportunities for cost savings. With its in-depth analysis, procurement teams can make informed decisions, enhance budget management, and improve overall financial efficiency.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Procurement Spend Analysis Agent:

Procurement spend analysis input

Vendor	Category	Amount_Spent	Transaction_Date
Acme Supplies	Office Supplies	15000	2024-01-15
Global Tech	IT Equipment	45000	2024-02-10
Office Essentials	Office Supplies	12000	2024-03-05
Superior Furniture	Furniture	30000	2024-04-12
Tech World	IT Equipment	60000	2024-05-20

Deliverable Example

Sample output delivered by the Procurement Spend Analysis Agent:

Vendor	Category	Total_Spent	Cost_Savings_Opportunity	Spending_Pattern_Insight
Acme Supplies	Office Supplies	15000	1000	High spending on regular supplies. Consider bulk purchase discounts.
Global Tech	IT Equipment	45000	2500	Significant spending on tech equipment. Explore alternative vendors for cost reduction.
Office Essentials	Office Supplies	12000	800	Consistent purchases with small savings potential. Review alternative supplies.
Superior Furniture	Furniture	30000	1500	Large expenditure on furniture. Potential savings through negotiations.
Tech World	IT Equipment	60000	3000	High tech spend; opportunity for volume discounts or vendor consolidation.

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.

Procurement Spend Analysis Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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