About this Agent

The Follow-Up Reminder Agent is an AI-powered digital assistant crafted to boost the efficiency of technical support teams by automating follow-up reminders. This intelligent agent ensures that both customers and support agents are promptly notified about pending or unresolved tickets, facilitating timely updates and resolutions. By streamlining the follow-up process, it significantly reduces response times and enhances customer satisfaction within technical support services.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Follow-Up Reminder Agent:

Customer support tickets

Ticket ID	Customer ID	Customer Name	Customer Email	Contract ID	Contract Start Date	Contract End Date	Support Agent ID	Support Agent Name	Support Agent Email	Issue Description	Issue Category	Ticket Status	Priority	Date Created	Last Updated	Resolution Date
TKT-0090	CUST-46	ChilledFoods Direct	customer404@example.com	CON-403	2021-04-04 08:36:33.186372744	2025-07-09 04:19:43.166332664	42-8	Emily Johnson	emily.johnson@valantir.com	Sample issue description	Account Management	Resolved	Low	2024-10-04	2024-10-04
TKT-0012	CUST-474	Healthy Harvest Foods	customer392@example.com	CON-489	2022-09-07 03:50:51.703406816	2026-11-06 03:22:00.240480960	33-3	Ava Harris	ava.harris@valantir.com	Sample issue description	Technical Error	Pending	High	2024-10-04	2024-10-04

Deliverable Example

Sample output delivered by the Follow-Up Reminder Agent:

Follow-Up Reminder: Your Support Ticket [TKT-0012] Is Pending

Dear Healthy Harvest Foods,

We hope you are doing well. We are reaching out to remind you that your support ticket [TKT-0012], regarding the issue "Technical Error," is still pending resolution. Our team is committed to providing timely assistance, and we would like to ensure that your concerns are addressed as soon as possible.

If you have any additional information or updates related to this issue, please reply to this email, and our support agent Ava Harris (ava.harris@valantir.com) will assist you further.

Thank you for your patience, and we look forward to resolving this matter promptly.

Best regards,

Technical Support Team

Valantir

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.

Follow-Up Reminder Agent

About this Agent

Input Data Set

Deliverable Example

Follow-Up Reminder: Your Support Ticket [TKT-0012] Is Pending

Company

Product

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