Introducing Large Quantitative Models (LQMs): The Intelligence Engine Behind Aviso’s Revenue AI
Jul 8, 2025
You've heard of Large Language Models (LLMs), the AI powerhouses behind natural language understanding and generation. But what about the other side of the AI coin, one that reasons with numbers, forecasts outcomes, and drives decisions based on hard data?
For all the buzz around generative AI, the most impactful uses of artificial intelligence have long been grounded not in conversation but in calculation. From risk models in finance to fluid dynamics simulations in aerospace, AI’s deepest roots lie in mathematics, statistical reasoning, and computational simulation, areas where precision matters more than prose.
Now, thanks to breakthroughs in scalability and adaptability, those foundations are being reimagined. A new generation of systems is emerging—Large Quantitative Models (LQMs). LQMs represent a new frontier in AI, designed to analyze structured, numerical data with the same scale, flexibility, and impact that LLMs brought to language.
From predicting sales outcomes to optimizing rep performance, LQMs deliver insights grounded in data, not guesswork. And at Aviso, we’re not just exploring this new frontier, we’re building on it.
What Exactly Are Large Quantitative Models (LQMs)?
Large Quantitative Models (LQMs) are purpose-built AI systems that leverage advanced machine learning techniques to process, analyze, and generate insights from numerical and quantitative data. They are trained to make predictions, simulate systems, and provide deep insights into numerical problems.
Think of LQMs as the analytical counterpart to LLMs. Unlike LLMs, which excel in parsing and generating language, LQMs specialize in mathematical reasoning, statistical analysis, forecasting, and optimization. This data-driven approach allows them to identify subtle patterns, quantify risks, and forecast outcomes with remarkable precision.
If LLMs are your conversational partners, LQMs are your quantitative strategists.
Here’s a deeper dive into what makes LQMs unique:
Focus on Quantitative Data: Unlike LLMs trained on vast amounts of text, LQMs are built to handle numerical datasets, structured data, statistics, and experimental results. They are the masters of the spreadsheet, the database, and the intricate world of numbers.
Distinct from LLMs: This focus on quantitative data enables LQMs to excel in mathematical reasoning, complex calculations, statistical analysis, predictive modeling, and optimization‚—tasks where LLMs, by themselves, would fall short.
Advanced Computational Simulations: LQMs can perform sophisticated simulations, generate insights through computational models, and tackle complex problems like financial forecasting, risk assessment, and decision-making across various industries.
Generative Capabilities with Interpretability: Beyond just analysis, LQMs can generate realistic data and provide interpretable insights, making their conclusions actionable and understandable.
LQMs vs. LLMs: What’s the Difference
Aspect | Large Language Models (LLMs) | Large Quantitative Models (LQMs) |
|---|---|---|
Reasoning Style | Probabilistic and linguistic reasoning | Deterministic or stochastic mathematical reasoning |
Interpretability | Often viewed as “black boxes” with limited explainability | Typically more interpretable, especially in structured scientific models |
Model Architecture | Neural networks (transformers) designed for language understanding | Mathematical or physics-based models, often combined with ML techniques |
Training Sources | Massive collections of text from the web | Scientific theories, simulations, and specialized datasets |
Real-time Use | Detecting patterns, generating and understanding language | Running simulations, forecasting, and optimizing systems |
Error Handling | May hallucinate or generate plausible but incorrect content | Errors tend to be mathematical or computational, often easier to debug |
LQMs at Aviso: The Quantitative Backbone of an Intelligent Revenue Engine
Revenue operations today demand more than just siloed dashboards and backward-looking reports. You need a unified intelligence layer that drives foresight, alignment, and flawless execution. This is precisely what Aviso’s Revenue Intelligence platform delivers, and it's powered by our proprietary Large Quantitative Models (LQMs), which act as a powerful quantitative reasoning layer.
LQMs form the central reasoning layer across all of Aviso's modules: forecasting, pipeline inspection, deal execution, activity intelligence, and coaching. They are a sophisticated suite of advanced forecasting, risk modeling, and causal inference engines, specifically designed to perform deep analysis on structured RevOps data.
They synthesize signals from diverse sources—CRM data, engagement platforms, financial forecasts, and even outputs from other specialized AI models (like sentiment analysis). This allows LQMs to, for example, precisely quantify deal-level risk, forecast revenue with high accuracy, or identify the subtle behavioral patterns that correlate with sales success.
With this LQM-powered architecture:
Forecasts become dynamic: No longer static projections, but adaptive models that evolve in real-time with pipeline reality.
Deal execution is guided: Benefit from real-time WinScores and next-best actions shaped by true engagement and risk signals.
Rep behavior is measurable: Coaching and performance benchmarks are driven by AI, not guesswork.
Activity intelligence is actionable: Tying emails, meetings, and buyer signals directly to outcomes.
Rather than treating each module in isolation, Aviso’s LQM-powered stack interconnects them into one cohesive system. This enables your organization to operate with precision, agility, and unified context across every role, region, and quarter.
This is Revenue Intelligence 2.0: not just better reporting, but orchestrated GTM execution, led by LQMs and built for scale.
The Power Of Fusion: Integrating LLMs with LQMs
The fusion of LQMs with LLMs presents an intriguing possibility for tackling even more complex challenges in the business world. Such hybrid models combine:
The Quantitative Analysis Strengths of LQMs: Excelling at statistical analysis, predictive modeling, and optimization from structured, numerical data.
The Contextual and Interpretive Abilities of LLMs: Capable of understanding natural language, extracting key information from unstructured sources, and facilitating intuitive human interaction.
In a hybrid system, an LLM acts as an important module, leveraging its natural language processing power to extract critical data from raw, unstructured sources like emails, meeting notes, or customer conversations. This unstructured information is then transformed into numerical representations. These numerical representations are then passed to an LQM for deep analysis, modeling, and prediction, enabling real-time analytics, scenario simulations, and optimized decision-making. This powerful combination not only enhances the analytical power of the models but also significantly improves communication and decision-making processes, ensuring that complex data-driven insights are accessible and actionable.
Aviso's Hybrid AI Layer
At the heart of Aviso’s intelligence engine lies this advanced hybrid AI layer. Foundational LLMs provide sophisticated natural language understanding, allowing our AI to comprehend complex business queries and user intent with remarkable accuracy. But understanding the 'what' is only half the battle. To deliver truly game-changing insights, this is fused with Aviso’s proprietary Large Quantitative Models (LQMs).
This fusion of LLM intuition and LQM precision is further amplified by patterns learned from anonymized, large-scale usage across diverse deployments.
For our LLMs, this means unparalleled fluency in sales-specific jargon and intent. For our LQMs, it translates to continuously refined predictive accuracy, benchmarked against real-world sales cycle dynamics. This synergy delivers a depth of contextual understanding that no generic AI can replicate.
Our hybrid approach therefore drives:
1. Accurate time series forecasting and deal-level risk assessment, powered by the robust analytical capabilities of our LQMs.
2. Behavioral signals and sentiment-driven coaching, where LQMs quantify impacts and LLMs help craft effective communication.
3. Contextual recommendations grounded in real-world sales patterns, identified by LQMs and made accessible via intuitive LLM interactions.
From “What’s stalling this deal?” to “What will we close this quarter?”, our AI answers not just with precision, but with insights that genuinely move the needle. While LLMs grasp the business context of your questions, it’s the LQMs that provide the rigorous, data-driven backing, transforming raw data into unified, prescriptive actions grounded in your operational reality.
What makes this entire AI architecture uniquely powerful is its model-agnostic design. We’re not bound to a single model or vendor—instead, we intelligently orchestrate the optimal combination of best-in-class LLMs, our proprietary LQMs, and other task-specific models based on performance, cost, and your specific business context. This ensures you always benefit from the leading edge of AI, tailored to your GTM workflows.
The Takeaway: LQMs Are Redefining Revenue Execution
Large Quantitative Models (LQMs) are not a future concept—they’re the new standard for enterprise AI. And at Aviso, we’ve already operationalized them to transform how global revenue teams forecast with precision, execute with confidence, and scale with intelligence.
While others are still exploring what LQMs could be, we’ve built them into the core of our platform, powering real-world impact across every role, region, and revenue motion.
LQMs are here. They’re real. And they’re changing how revenue gets done.
Ready to see LQMs in action? Book a demo and discover how Aviso’s LQM-powered platform can help your team win smarter, faster, and at scale.