Agents-internal

About this Agent

The Transaction Matching Agent is an AI digital assistant specifically designed to enhance the reconciliation process within corporate accounting departments. This advanced tool automatically matches transactions between the general ledger and bank statements, ensuring both accuracy and efficiency. By significantly reducing manual effort and minimizing errors, it empowers accounting teams to focus on more strategic tasks, thereby improving overall financial management and reporting.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Transaction Matching Agent:

Transaction_ID	Date	Description	Amount	Bank_Account_Number
2001	2024-10-01	Office Supplies	-500.0	1389830502
2002	2024-10-02	Consulting Fees	1200.0	6466876324
2003	2024-10-03	Software Subscription	-300.0	6647348058
2004	2024-10-04	Travel Expense	-700.0	9422161333
2005	2024-10-06	Equipment Purchase	-1500.0	2032893757

Transaction_ID	Date	Description	Debit	Credit	Account_Number
1001	2024-10-01	Office Supplies	500.0	0.0	3749263879
1002	2024-10-02	Consulting Fees	0.0	1200.0	1321114368
1003	2024-10-03	Software Subscription	300.0	0.0	5883813061
1004	2024-10-04	Travel Expense	700.0	0.0	6696147178
1005	2024-10-05	Equipment Purchase	1500.0	0.0	2637236007

Deliverable Example

Sample output delivered by the Transaction Matching Agent:

General_Ledger_Transaction_ID	Bank_Statement_Transaction_ID	Date	Description	Debit/Credit	Matched	Reason	General_Ledger_Account_Number	Bank_Account_Number
1001	2001	2024-10-01	Office Supplies	500.0	Yes	Matched on Description and Amount	3749263879	1389830502
1002	2002	2024-10-02	Consulting Fees	-1200.0	Yes	Matched on Description and Amount	1321114368	6466876324
1003	2003	2024-10-03	Software Subscription	300.0	Yes	Matched on Description and Amount	5883813061	6647348058
1004	2004	2024-10-04	Travel Expense	700.0	Yes	Matched on Description and Amount	6696147178	9422161333
1005	2005	2024-10-05	Equipment Purchase	1500.0	Yes	Matched on Description and Amount	2637236007	2032893757

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.

Transaction Matching Agent

About this Agent

Input Data Set

Deliverable Example

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