CRM Insight Agent

About the Agent

The CRM Insight Agent serves as a dynamic support tool for sales teams by offering data-driven insights through its conversational interface. By utilizing generative AI and natural language processing (NLP), this agent is capable of parsing complex CRM data to respond to queries efficiently and accurately. It explores various dimensions of sales data, including customer interactions, sales pipelines, and performance metrics, enabling sales professionals to swiftly access the information they need to make informed decisions. The CRM Insight Agent aids in prioritizing leads and identifying lucrative opportunities for upselling and cross-selling. By handling data retrieval and analysis tasks, it frees sales teams from manual data searches, empowering them to concentrate on closing deals and fostering meaningful customer relationships.

This agent is designed to seamlessly integrate with existing CRM systems, ensuring that the insights and answers it provides are not only relevant but also based on the most current data available. This integration makes it a reliable assistant, offering immediate access to critical sales information without disrupting existing workflows. By minimizing errors and enhancing efficiency, the CRM Insight Agent plays a pivotal role in streamlining sales operations. Feedback from users is continuously integrated into its functionality, ensuring ongoing improvements and alignment with the evolving needs of sales teams. With this tool, sales professionals are better equipped to maximize their efforts, ultimately leading to improved sales performance and customer satisfaction.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for CRM Insight Agent:

CRM - Sample Input

CRM Insight Agent - Input Samples

These are examples of natural language queries a sales team member might ask the CRM Insight Agent.

Query #	Query
1	Show me accounts that haven't been contacted in the last 30 days.
2	How is my pipeline compared to last month?
3	What were the key discussion points from my last meeting with NovaTech Solutions?
4	Which products are selling the most this year?
5	What is the conversion rate for leads coming from LinkedIn?

Deliverable Example

Sample output delivered by the CRM Insight Agent:

CRM Insight Agent - Output Samples

These are examples of natural language queries a sales team member might ask the CRM Insight Agent, along with the corresponding responses by the Agent.

Query #	Query	Agent Response
1	Show me accounts that haven't been contacted in the last 30 days.	Here are the accounts that haven't had any activity in the last 30 days: - Stellar Dynamics: Last contacted by Emily Carter on September 15th (Email). - Quantum Innovations: Last contacted by James Lee on September 20th (Phone Call). - Aether Systems: No recent activity found.
2	How is my pipeline compared to last month?	Your current pipeline value is $320,000 across 15 open opportunities. Comparison to Last Month: - Current Pipeline Value: $320,000 (15 opportunities) - Last Month Pipeline Value: $280,000 (12 opportunities) - Change in Pipeline Value: +$40,000 (+14.3%) - Change in Number of Opportunities: +3 (+25%)
3	What were the key discussion points from my last meeting with NovaTech Solutions?	The key discussion points from your meeting with NovaTech Solutions on October 10th were: - Their interest in the Enterprise Plan's advanced analytics features. - Concerns about the implementation timeline. - Agreement to schedule a follow-up call on October 25th to discuss pricing.
4	Which products are selling the most this year?	The top 3 best-selling products this year are: 1. QuantumCore X1: Sold 1,250 units, generating $1,875,000 in revenue. 2. StellarEdge Pro: Sold 980 units, generating $1,470,000 in revenue. 3. AetherSync Lite: Sold 720 units, generating $864,000 in revenue.
5	What is the conversion rate for leads coming from LinkedIn?	The conversion rate for leads coming from LinkedIn is 18.5%. Details: - Total LinkedIn Leads: 540 - Converted LinkedIn Leads: 100

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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.