Agents-internal

About this Agent

The Supplier Consolidation Suggestion Agent is a powerful AI digital worker designed to optimize your procurement process by analyzing procurement data to identify potential opportunities for supplier consolidation. By reducing vendor complexity, this agent helps improve procurement efficiency, streamline operations, and potentially lower costs. Ideal for procurement departments looking to enhance their supplier management strategies, this AI worker ensures you maintain a lean and effective vendor base.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Supplier Consolidation Suggestion Agent:

Supplier Consolidation Input Data

Supplier Name	Total Spend ($)	Number of Purchases	Product Categories	Contract Expiry Date
Acme Corp	15000	25	Office Supplies, IT Equipment	2024-12-31
Bright Solutions	12000	30	Office Supplies	2024-05-15
Eagle Industries	18000	22	IT Equipment	2024-10-20
Blue Sky Inc.	10000	28	Office Supplies, Furniture	2023-11-10
Sunrise Supplies	22000	35	Furniture, IT Equipment	2025-01-30

Deliverable Example

Sample output delivered by the Supplier Consolidation Suggestion Agent:

Consolidation Suggestion	Supplier Name	Consolidation Reason	Estimated Savings ($)
Yes	Acme Corp, Bright Solutions	Similar product categories and overlapping supplies	5000
No	Eagle Industries	Only supplies IT Equipment	0
Yes	Blue Sky Inc.	Handles unique categories not overlapping with others	3000
No	Sunrise Supplies	Supplies furniture and IT equipment, consolidation can reduce complexity	0

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.

Supplier Consolidation Suggestion Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

Contact us

Get more information

In this personalized demo, you will:

Explore ZBrain Platform

Discover Workflow Integrations

Engage in Q&A Session