May 13, 2025

How to Build a Sales Call Compliance Classifier

How to Build a Sales Call Compliance Classifier

Sales call compliance is a costly and time-consuming endeavor. The task can involve manual analysis, dedicated teams, and sifting through thousands of hours of recorded calls. Errors in the compliance process can also create business challenges or ethical dilemmas that are difficult to solve.

However, with the rise of AI agents, companies can now automate the process of sales call compliance. Instead of spending countless hours analyzing sales calls, teams can focus on selling and enabling customers.

In the following blog, we’ll show you how to build a sales call compliance classifier, and explain how it can benefit your team.

Sales Call Compliance: Methods & Challenges

Sales call compliance presents several significant challenges, particularly for large organizations with extensive customer bases. One major issue is navigating the complex web of legal requirements across various jurisdictions. Failing to comply with these regulations can result in hefty fines and damage to a company’s reputation, making it critical for businesses to stay up-to-date with evolving laws.

Another challenge is maintaining accurate and comprehensive customer records to ensure that consent requirements are met. This includes keeping track of when and how consent was obtained, as well as promptly updating contact preferences when customers opt out. 

Finally, balancing compliance with effective sales practices can be difficult. Sales teams are often under pressure to meet aggressive targets, which can lead to overly persistent or improperly timed outreach if not carefully managed. This creates a tension between maximizing sales opportunities and respecting customer boundaries. 

To address this, companies must implement robust monitoring systems to detect and prevent violations, often requiring additional headcount for compliance oversight, training, and audit functions. This can be expensive, particularly for larger firms, where compliance teams must scale alongside sales operations to ensure that every interaction remains within legal boundaries. 

How AI Agents Improve Sales Call Compliance

AI agents are transforming sales call compliance by acting as intelligent, always-on assistants that help sales teams avoid costly regulatory mistakes. These agents can automatically verify customer consent, screen numbers against do-not-call lists, and monitor call timing to ensure compliance.

Unlike human agents, AI systems can instantly assess whether a call is legally permissible, reducing the risk of accidental violations. This automation not only improves compliance but also speeds up the calling process, allowing sales teams to focus on building relationships instead of regulation.

Beyond just call screening, AI agents excel at real-time conversation monitoring. Using natural language processing (NLP) and machine learning, these systems can analyze the tone, language, and content of calls as they happen, flagging non-compliant language. 

AI agents also significantly streamline record-keeping and audit preparation, which are critical for maintaining long-term compliance. They can automatically log call details, store consent records, and track customer communication preferences, ensuring every interaction is properly documented. 

This reduces the need for large compliance teams and minimizes the risk of human error in data management. This approach not only lowers operational costs but also reduces the likelihood of expensive regulatory fines, making compliance more efficient and less burdensome for businesses.

AI Agent Overview: Sales Call Compliance Classifier

This AI agent allows the user to upload an audio file (.mp3, .wav, etc.) of a sales call. The sales call is then analyzed for compliance based on documentation. 

The documentation is not adjustable by the user. The AI agent outputs a compliance score on a scale from 0 to 1, with 1 being the highest compliance. 

Industry

Horizontal

Persona

Compliance Officer

Problem

Sales call compliance is a time-consuming and costly endeavor for companies. 

Solution

This AI agent analyzes a sales call based on user-provided documentation and determines compliance.

User Interface

Form

LLM

Mistral Large 2

Data Sources

File Upload (Sales Call)

Actions

  1. The compliance officer uploads a sales call.

  2. Sales call and compliance documentation are fed into the LLM.

  3. The LLM analyzes the sales call for compliance based on the information in the documentation.

  4. The call is assigned a compliance score by the LLM.

  5. The compliance score is outputted to the user.

Time to Launch

Medium




Benefits

  • Cut time spent reviewing compliance calls from 1000 hours a month to 8 hours a month.

  • Allows compliance officers to focus on more high-functioning analysis.

  • Enables companies to invest in salespeople as opposed to sales compliance.

In the following section, we’ll show you how to build a sales call compliance classifier using StackAI.

Sales Call Compliance Classifier: How to Build

Here’s how to build the Sales Call Compliance Classifier. First, make sure to sign up for a free StackAI account

Navigate to the account dashboard. Click ‘New Project’.

Click the ‘Workflow Builder’ option.

From here, choose the Sales Call Compliance Classifier template. 

The template will launch a pre-built workflow for the AI agent.

Let’s examine each component of the workflow one at a time. First, we have the Audio-to-Text node. 

This node transforms the audio file (in this case, the sales call) into text. The text is then fed into the LLM for analysis.

The node has four options to choose from. The first is the Provider. This is the speech-to-text provider. 

Next is the model. This is the model that you’ll use from the provider to turn the sales call into text. 

The submodel allows for further customization of the model.

The final option allows you to choose the source of the audio file: URL, recording, or upload. 

That’s all for the Audio-to-Text node. The next component of the workflow is the Documents node. 

This allows you to upload compliance documentation that the LLM can use to assess the compliance of sales calls. 

The next node is the LLM. The model of the LLM is Mistral Large 2. 

You can find the Instructions for the LLM below:

You review sales calls to check for compliance. You will receive a set of guidelines on how to review it and a user message with the call.

You can only respond with a JSON.

And the Prompt is as follows:

Guidelines to review the call:

<guidelines>

/undefined

</guidelines>

Sales call:

<transcription>

/undefined

</transcription>

Respond with a JSON containing the following key-value pairs:

- "compliance": float betweeen 0.0 and 1.0 (0.0 means not compliant)

- "violations": list of strings.

The final node is the Output node. This is the JSON output that assigns a compliance score.

Now let’s go to the Export tab.

Give the AI agent a name and a description.

Now launch the AI agent. 

Upload a call recording.

Press Submit. The compliance score will appear in the output box.

This allows you to perform sales call compliance in seconds, without requiring an expensive staff. 

Launch the Sales Call Compliance Classifier Now!

Sales call compliance is a costly and time-intensive process. However, with an AI agent, you can greatly reduce the time and resources it takes to perform call compliance.

You can launch the Sales Call Compliance Classifier with our pre-built template! Sign up for a free account now with StackAI. 

Toni Lopez

Software Engineering at Stack AI

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