Artificial Intelligence has entered a new era where language alone is no longer enough. Multimodal LLMs (large language models) are setting the pace for this shift, enabling AI systems to understand and respond to a wide range of human inputs, including text, speech, images, and even video. According to the Gartner, 2024 Hype Cycle for Generative AI, Multimodal GenAI, and open-source LLMs are two transformative technologies with the potential to deliver substantial competitive advantage.
These models are already transforming customer experience (CX), enabling AI agents to process voice input, trigger an SMS, analyze uploaded images, and resolve issues—seamlessly.
In this article, we’ll explain multimodal large language models (MLLMs), how they work, what distinguishes them from traditional LLMs, and how Quiq helps businesses orchestrate them into powerful, real-world customer experiences.
Multimodal CX vs. Multimodal LLMs
While the terms ‘multimodal’ and ‘multichannel’ are often used interchangeably, it’s important to understand their distinctions, especially in CX. A multimodal LLM can natively process different input types (text, images, speech, video). A multimodal CX experience, however, may involve multiple communication channels—such as voice and digital messaging—being used at once. Quiq brings these together, letting users speak on the phone while texting images, with AI understanding and unifying all the inputs in real time, just like a human agent would.
What is a Multimodal LLM?
A multimodal large language model (MLLM) is an AI model that processes diverse types of data, not just written text but also images, speech, and videos. Unlike traditional LLMs that rely solely on language, these models ‘see’ and ‘hear’ to better understand complex contexts. A large language model becomes a tool that can process textual data alongside image or video content to grasp meaning more holistically.
Industry analysts recognize the impact of this shift. Forrester Research identifies multimodal AI as a key trend reshaping automation, particularly in customer experience, where enterprises must meet users across diverse channels and content formats.
Consider an AI-powered customer support agent. A user might describe an issue verbally, send a photo of the product, or even provide a short video. A multimodal LLM integrates all this input to generate intelligent, human-like responses.
This distinction is crucial in CX. A customer might speak to an agent on the phone while simultaneously texting images. The AI interprets both, delivering a coherent experience as a live agent would.
Models like GPT-4V and Gemini are redefining automation capabilities—from identifying garage door models via photos to streamlining flight add-ons over messaging channels.
How Do Multimodal LLMs Work?
To understand how multimodal LLMs work, let’s look at the types of input they handle and how these are processed together:
- Text: Messages, emails, or voice-to-text transcription
- Images: Product photos, documents, screenshots
- Speech: Real-time audio or pre-recorded messages
- Video: Short clips for product issues or training
Multimodal models rely on a shared architecture—often based on transformers—to encode and unify inputs into a common ‘language’ for reasoning.
Here’s how it typically works:
- Text Processing interprets language using classic NLP.
- Vision Modules analyze visual content like product images.
- Speech Recognition turns audio into structured language.
- Fusion Layers synthesize inputs to generate relevant, personalized outputs.
Picture this: A customer calls to report a stuck recliner. The AI understands the spoken issue and initiates a text message requesting an image of the problem. The customer responds with a photo, and the AI determines whether it’s the correct product, or flags it with something like, ‘Maybe you uploaded the wrong image?’
It’s not just about recognizing the image—it’s about understanding the full context of the conversation and how the image relates to it.
Quiq’s AI Studio orchestrates this complex symphony. It enables enterprises to manage conversations across voice and digital channels, track multimodal inputs in a unified view, and deliver responsive, seamless customer experiences.
From Data Chaos to Clarity: Managing Visual Inputs and Embeddings
A significant challenge in CX is processing image-heavy documentation, like manuals or product guides. Multimodal LLMs enable AI agents to interpret these visual assets by converting them into numerical representations known as embeddings. These allow the AI to understand and retrieve matching visuals or information when customers describe issues via text or upload images. For example, when a customer texts, ‘The blue light is blinking on my garage opener,’ the AI can correlate it to the correct image in a visual troubleshooting guide and offer accurate support instantly.
Top Multimodal Large Language Models
Multimodal LLMs are evolving quickly, and with them, the ways businesses can deliver intelligent automation across customer touchpoints. Each model brings unique strengths: some are designed for high-resolution image analysis, and others excel at parsing spoken conversations or synthesizing insights from video. At Quiq, we believe understanding these capabilities is essential, but what matters even more is having the flexibility to choose the right model for the job. The models below represent some of the most powerful multimodal LLMs in use today.
1. GPT-4V (OpenAI)
- Combines text and visual understanding
- Powers complex visual tasks like document parsing or image captioning
- Enables real-time visual Q&A in AI agents
Use Case: A customer calls about a recliner stuck open. The voice AI processes the inquiry, sends a text requesting an image, and then analyzes the uploaded image to determine if a technician is needed. It sends a follow-up message offering three appointment times to choose from. The result is automation that’s efficient, conversational, and friction-free.
2. Gemini (Google DeepMind)
- Supports seamless input from text, images, and video
- Strong at contextual reasoning across formats
- Deep integration with Google tools
Use Case: A customer calls to add a golf bag to a flight. Instead of sharing credit card info over voice, the AI sends a secure payment link via text, avoiding input errors and improving security.
3. Flamingo (DeepMind)
- Optimized for image-to-text workflows
- Learns new tasks with minimal data
Use Case: A customer sends a blurry product image. The model identifies it as the NeuroLift 5500 series and retrieves the right troubleshooting steps—no training is required.
4. LLaVA (Large Language and Vision Assistant)
- Open-source and ideal for experimentation
- Supports image + text prompts
- Great for accessibility and research
Use Case: Customers upload images of issues, such as blinking lights, error messages, and faulty parts. The model interprets and acts on these visuals with precision.
5. Kosmos-1 (Microsoft AI)
- Excels at vision-language and audio-text integration
- Ideal for enterprise voice assistants
Use Case: A voice assistant offers to send a rescheduling link via SMS instead of finishing the task by voice. The shift from synchronous to asynchronous reduces user frustration.
LLM-Agnostic by Design: Why Flexibility Matters
While each model offers unique strengths—some excel at image captioning, others at voice-to-text integration—the most powerful CX platforms aren’t defined by the model they use; they’re defined by how well they adapt to what’s needed.
That’s why Quiq is LLM agnostic. Our AI platform isn’t tied to a single provider or model. Instead, we integrate with whichever multimodal LLM best fits the use case, whether that’s GPT-4V for document parsing, Gemini for seamless video-to-text processing, or open-source models like LLaVA for rapid iteration and customization.
This model-agnostic flexibility means:
- You can select the model that best aligns with your specific CX needs, whether that’s visual accuracy, conversational flow, or speed of response.
- Your AI architecture stays adaptable as new LLMs and capabilities enter the market.
- You’re empowered to build around your existing tech ecosystem and regulatory requirements, without compromise.
This LLM-agnostic architecture is at the heart of how we help enterprise teams future-proof their automation strategies while delivering better outcomes for customers and agents alike.
Learn more about our approach to LLM-agnostic AI.
How Quiq Measures AI Agent Success
Quiq employs a variety of performance metrics to assess AI agents:
- Estimated CSAT: Compares customer satisfaction among human-only, AI-only, and hybrid interactions.
- Automated Resolution Rate: Evaluates whether the customer’s issue was genuinely resolved, going beyond mere containment.
- Sentiment Shift: Monitors the emotional tone from the beginning to the end of a conversation.
- Goal Completion: Measures how often an AI successfully reschedules an appointment, adds a bag, or completes other business-specific outcomes.
These insights help Quiq’s clients continuously refine and improve their AI deployments.
Quiq AI Studio: The Orchestration Layer That Makes It All Work
Multimodal AI agents require orchestration, especially when inputs come from different channels simultaneously. Quiq AI Studio acts as the conductor, tracking and aligning input streams (voice, text, images) in real time, ensuring every message is attributed to the right user session. It supports debugging, prompt testing, and conversation state management. More than a toolkit, it’s a full orchestration layer purpose-built for CX automation.
Learn more about how Quiq’s AI agents enhance customer engagement and streamline support operations.
Future-Proofing with Confidence
LLMs are evolving rapidly—getting faster, cheaper, and smarter. Quiq AI Studio allows businesses to seamlessly upgrade to the latest models using built-in tests, replays, and evaluation tooling. This protects performance while introducing new capabilities like improved visual reasoning, better audio comprehension, or expanded context windows. With Quiq, enterprises can stay ahead of the curve without compromising quality or stability.
What’s Next for Multimodal AI
Looking ahead, multimodal AI will only become more pervasive. LLMs will become true reasoning engines with faster processing, lower cost, and expanded input comprehension. According to Forrester, the rise of predictive and generative AI will transform customer service operations by automating interactions, capturing customer intent, and routing inquiries to appropriately skilled agents. This evolution will allow human agents to focus on complex interactions requiring empathy and personalization, enhancing overall customer satisfaction.
For CX leaders, this opens doors to:
- Proactive support through predictive multimodal inputs
- Enhanced personalization from visual cues
- Smoother handoffs between voice and digital journeys
Yet, as powerful as LLMs are, they are still just tools. The real value lies in how they are orchestrated into real-world customer journeys—removing friction, saving time, and creating brand loyalty through seamless experiences. That’s the promise of multimodal LLMs, brought to life by Quiq.
Explore More
- Learn how Quiq’s AI Studio makes multimodal AI practical for enterprises.
- See Customer Success Stories where multimodal models improve automation and satisfaction.
- Get started with AI Agent Design built for your CX ecosystem.
