How to Build a Voice-Powered RAG Assistant for HubSpot Marketing Analytics with ElevenLabs

Picture Sarah, a marketing manager at a fast-growing tech company. It’s Monday morning, and the CMO has just asked for a “quick summary” of last week’s campaign performance for a board meeting in an hour. Sarah dives into HubSpot, her heart sinking slightly. She opens one tab for email campaign metrics, another for landing page views, a third for blog traffic, and a fourth for social media engagement. She starts filtering dates, exporting CSVs, and frantically pasting data into a spreadsheet, trying to stitch together a coherent narrative. The information is all there, but retrieving it feels like a digital scavenger hunt. As she races against the clock, juggling filters and columns, she thinks to herself, “I wish I could just ask HubSpot for the numbers I need.” This daily struggle is a familiar pain point for marketing teams everywhere. Platforms like HubSpot are treasure troves of data, but unlocking actionable insights quickly remains a significant challenge. Marketing data is often fragmented across different objects—contacts, companies, deals, campaigns, and content assets. Getting synthesized, cross-functional answers requires manual effort, technical know-how, and most importantly, time that teams simply don’t have.

What if you could transform this click-heavy, time-consuming process into a simple, effortless conversation? This is where the power of Retrieval-Augmented Generation (RAG) and advanced AI tools comes into play. Imagine an intelligent assistant that connects directly to your HubSpot portal. You ask a question in plain English, like “What was our email open rate for the ‘Summer Launch’ campaign and how many new leads did it generate?” The assistant understands your request, queries the relevant HubSpot data points, synthesizes a concise answer, and delivers it back to you in a clear, natural-sounding voice. This isn’t a far-off dream; it’s a practical solution you can build today. By combining the power of Large Language Models (LLMs), the flexibility of a RAG architecture, and the hyper-realistic voice synthesis of a platform like ElevenLabs, you can create a bespoke voice-powered analytics assistant. This guide will provide a technical, step-by-step walkthrough to build a prototype of this very system. We’ll explore the architecture, detail the required tools, and provide the foundational code to bring your HubSpot data to life, empowering your team to move from data extraction to strategic action at the speed of speech.

Architecting Your HubSpot Voice Analytics Assistant

Building a sophisticated AI assistant requires a well-designed architecture. Before writing a single line of code, it’s crucial to understand the components involved and how they interact. This system works by creating a seamless flow from a spoken query to a spoken answer, with several key technologies working in concert.

The Core Components: A High-Level Overview

The workflow can be broken down into a logical sequence. An LLM acts as the central orchestrator, using a set of purpose-built tools to interact with external systems. This agent-based approach is a powerful implementation of RAG principles, where the “retrieval” phase involves fetching live data from an API rather than static text from a document.

Here is the essential technology stack:

• Voice Input/Output: For input, a standard speech-to-text library like Python’s speech_recognition can capture the user’s query. For output, ElevenLabs is the star of the show, converting the LLM’s final text response into a natural-sounding voice.
• HubSpot Integration: The official HubSpot API is the bridge to your marketing data. We’ll use it to programmatically access everything from campaign metrics to website analytics.
• LLM & Orchestration Framework: A powerful LLM (like OpenAI’s GPT-4 or Cohere’s Command R+) will serve as the “brain,” interpreting user intent. A framework like LangChain helps manage the complex interactions between the LLM and the other components, including defining the tools the agent can use.
• Data Tools: These are custom functions we’ll write that allow the LLM to perform specific actions, such as get_blog_post_views or fetch_campaign_performance.

This architecture is effective because it’s modular and scalable. You can start with a few data-fetching tools and add more over time as the needs of your marketing team evolve. According to Gartner, 54% of marketing decisions are influenced by analytics, yet our ability to act on that data is often hampered by accessibility. This voice-powered system directly tackles that accessibility barrier.

Setting Up Your Development Environment

To begin, you’ll need to set up your Python environment and gather the necessary API keys. This project relies on a few key libraries that you can install using pip.

pip install hubspot-api-client elevenlabs langchain langchain-openai python-dotenv

Next, you’ll need to acquire API keys for each service:

1. HubSpot: Create a Private App in your HubSpot developer account to get an access token.
2. ElevenLabs: Sign up on the ElevenLabs website and find your API key in your profile settings.
3. OpenAI (or other LLM provider): Get your API key from your account dashboard.

An essential best practice is to manage these keys securely. As any seasoned developer will tell you, never hardcode API keys directly in your scripts. Instead, use a .env file and the python-dotenv library to load them as environment variables.

Create a .env file in your project root with the following:

HUBSPOT_ACCESS_TOKEN="your-hubspot-token"
ELEVENLABS_API_KEY="your-elevenlabs-key"
OPENAI_API_KEY="your-openai-key"

Now your environment is ready, and we can move on to the implementation.

Step-by-Step Implementation: From HubSpot Data to Spoken Insights

With the architecture defined and the environment configured, it’s time to build the core logic of our assistant. We’ll connect to HubSpot, create tools for our LLM agent to use, and integrate ElevenLabs to provide the final voice output.

Step 1: Connecting to HubSpot and Fetching Marketing Data

First, we need to establish a connection to the HubSpot API. Using the hubspot-api-client library, this is straightforward. We’ll create a client and then write functions that act as our data retrieval tools. Each function will be responsible for fetching a specific piece of marketing information.

Here’s how you can initialize the client and create a simple function to get analytics for a specific blog post:

import os
from dotenv import load_dotenv
from hubspot import HubSpot
from hubspot.crm.blogs.blog_posts import PublicApi

load_dotenv()

# Initialize HubSpot client
hubspot_client = HubSpot(access_token=os.getenv("HUBSPOT_ACCESS_TOKEN"))

def get_blog_post_analytics(post_name: str) -> str:
    """Fetches view counts for a blog post given its name."""
    try:
        # First, search for the blog post by name to get its ID
        # (This is a simplified example; a robust solution would handle multiple results)
        search_results = hubspot_client.crm.blogs.blog_posts.search_api.do_search(
            query=post_name
        ).results
        if not search_results:
            return f"Could not find a blog post named '{post_name}'."

        post_id = search_results[0].id

        # In a real app, you would use the Analytics API.
        # For simplicity, we'll return a placeholder string with the found ID.
        # Example: api_response = hubspot_client.analytics... .get_by_id(post_id)
        return f"Successfully found blog post with ID {post_id}. Pretend analytics data is here."

    except Exception as e:
        return f"An error occurred: {e}"

You would then expand on this by creating more functions for other data points, such as fetch_campaign_performance(campaign_name) or get_landing_page_conversion_rate(page_name).

Step 2: Building the RAG-Powered Query Engine

This is the brain of our operation. We’ll use LangChain to create an “agent” that can intelligently decide which data-fetching function (or “tool”) to use based on the user’s question. This agent-based framework is a dynamic form of RAG where the context is retrieved from an API in real-time.

First, we define our functions as tools that the LLM can understand:

from langchain.agents import tool

@tool
def get_blog_post_analytics_tool(post_name: str) -> str:
    """Useful for when you need to get the analytics or view count for a specific blog post. The input should be the name of the blog post."""
    return get_blog_post_analytics(post_name)

# Imagine another tool for campaigns
@tool
def get_campaign_metrics_tool(campaign_name: str) -> str:
    """Useful for when you need performance metrics for a marketing campaign, like email open rates or leads generated."""
    # ... implementation for fetching campaign data from HubSpot ...
    return f"The '{campaign_name}' campaign had a 25% open rate and generated 150 new leads."

tools = [get_blog_post_analytics_tool, get_campaign_metrics_tool]

Next, we initialize the LangChain agent and provide it with the tools and an LLM. The agent will parse the user’s query and execute the appropriate tool.

from langchain_openai import ChatOpenAI
from langchain.agents import create_openai_functions_agent, AgentExecutor
from langchain import hub

# Pull the prompt template
prompt = hub.pull("hwchase17/openai-functions-agent")

# Initialize the LLM and the agent
llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0)
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Test the agent
response = agent_executor.invoke({"input": "How did our 'Summer Launch' campaign do?"})
print(response['output'])

When you run this, agent_executor will invoke the get_campaign_metrics_tool with the argument 'Summer Launch' and return the result.

Step 3: Integrating ElevenLabs for Voice Output

Now for the final, and most engaging, step: giving our assistant a voice. ElevenLabs makes it incredibly simple to convert the text response from our agent into high-quality, spoken audio. The low latency of their API is critical for creating a natural, conversational experience.

First, initialize the ElevenLabs client. Then, take the text output from the agent_executor and pass it to the generate and play functions.

from elevenlabs.client import ElevenLabs
from elevenlabs import play

# Initialize ElevenLabs client
elelabs_client = ElevenLabs(api_key=os.getenv("ELEVENLABS_API_KEY"))

# Get the text response from the agent
agent_response_text = response['output']

# Generate and play the audio
audio = elelabs_client.generate(
    text=agent_response_text,
    voice="Rachel", # You can choose from a variety of pre-made voices or clone your own
    model="eleven_multilingual_v2"
)

play(audio)

With just a few lines of code, your assistant can now speak its findings. The realism of the voice makes the interaction feel significantly more polished and intuitive than just reading text on a screen. To get started with high-quality, low-latency voice generation for your own projects, you can try ElevenLabs for free now.

Enhancing Your Assistant: Advanced Techniques for Production

A working prototype is a great start, but a production-ready system requires more robustness, efficiency, and intelligence. Here are a few advanced techniques to take your assistant to the next level.

Caching for Speed and Cost-Efficiency

Making repeated API calls to both HubSpot and your LLM provider can be slow and costly. A simple caching layer can dramatically improve performance. For frequently asked questions like “What was our website traffic yesterday?”, the system can return a stored result instead of re-fetching and re-processing the data.

Using Python’s built-in functools.lru_cache decorator is a quick way to implement in-memory caching for your data-fetching functions. For more persistent caching, a Redis database is an excellent choice. Implementing a smart caching strategy can reduce API costs by up to 70% for common, recurring queries.

Handling Ambiguity and Follow-up Questions

Users won’t always be precise. A query like “How are we doing on leads?” is ambiguous. A more advanced assistant should be programmed to ask for clarification: “Do you mean total new leads this month, or leads from a specific campaign?”

You can achieve this by designing your LLM prompts to encourage clarification. Furthermore, by incorporating conversational memory (e.g., using LangChain’s ConversationBufferMemory), your assistant can handle context-dependent follow-up questions. If a user asks, “How many new contacts did we get last month?” and then follows up with “What about the month before?”, the assistant will understand the context and retrieve the correct data without the user needing to repeat the full query.

Proactive Insights and Alerting

The ultimate goal of an AI assistant is not just to be a reactive tool but a proactive partner. You can elevate your assistant by creating a scheduled script (e.g., a cron job) that runs daily. This script could ask the assistant to check for significant anomalies, such as a sudden drop in landing page conversion rates or a spike in email unsubscribes.

“The true power of an AI assistant isn’t just answering questions, but providing unsolicited, critical insights that a human might miss,” notes an expert in the field. When an anomaly is detected, the system can use ElevenLabs to generate a concise voice memo summarizing the issue and automatically send it to the marketing team’s Slack channel. This transforms the assistant from an on-demand tool into a vigilant, always-on analyst for your team.

By layering these advanced features, you evolve the system from a simple Q&A bot into an indispensable member of your marketing operations, one that saves time, uncovers hidden insights, and fosters a more data-driven culture.

Remember Sarah, our marketing manager drowning in dashboards? With this system, her urgent request from the CMO becomes a simple 30-second voice command: “Hey HubSpot, give me the performance summary of last week’s campaigns, focusing on new leads and email engagement.” The assistant delivers a spoken summary almost instantly. This is the future of data interaction. We’ve walked through how to architect this system, connect to HubSpot’s API, use a RAG-style LLM agent to interpret queries, and deliver a natural voice response with ElevenLabs. By building tools like this, you aren’t just saving clicks; you are fundamentally changing your team’s relationship with its data, making it more accessible, immediate, and powerful. Ready to stop clicking and start talking to your data? The first step is getting the best-in-class voice for your assistant. Try ElevenLabs for free now and discover how realistic voice synthesis can transform your custom AI applications.