Forecasting
Anticipate changes in demand, revenue, or risk early. LQMs convert historical trends and real-time inputs into actionable forward projections.
What is a Large Quantitative Model?
A Large Quantitative Model is AI trained on numerical data rather than words. While an LLM natively processes and outputs text, images, and audio, an LQM finds the relationships between different types of data, drives connections across signals, uncovers subtle patterns, and forecasts future outcomes.
- 16BNew signals ingested each day
- <15 minutesTo query a real-world signal
- 300+Trained models and growing
What makes a model an LQM?
An LQM is defined by its core purpose, not a specific architecture. These models ingest structured numerical data, for example the actual operational signals of a business, like transactions, movement, behaviors, pricing, and event streams. In turn, they produce quantitative results: predictions, forecasts, pattern linkages, and risk scores. Most organizations have barely tapped the potential of AI adoption, and LQMs represent the logical next step.
LQM vs. LLM: what's the difference?
Both are large AI models, but they're built for different work.
Large Language Model
LLM
Finds relationships across words and documents.
- Trained on words
- Trained to communicate and reason
- Generates and summarizes content
- Answers questions about text
Large Quantitative Model
LQM
Finds relationships across structured signals at scale.
- Trained on numerical data
- Trained to find relationships
- Uncovers hidden patterns in data
- Predicts what is likely to happen next
These systems complement each other well. Use a language model to frame questions naturally, and rely on a quantitative model to deliver data-driven answers.
How an LQM operates.
Find the structure
A foundation model identifies underlying structures within data, discovering how disparate signals connect.
Apply the context
That underlying framework then applies to specific data domains, ranging from market signals to consumer transactions.
Keep learning
This yields a trained model that handles complex quantitative queries and updates as fresh data flows in.
What businesses use LQMs for.
Behavioral Analysis
Project next moves across markets and target groups. Models trained on real-world behavior link past actions directly to upcoming trends.
Simulation
Evaluate strategic moves prior to rollout. Run detailed scenarios for price adjustments or supply chain shifts without risking actual operations.
Optimization
Identify optimal paths across massive option sets. Sorting through endless data combinations far exceeds human capacity, but an LQM resolves those calculations quickly.
Intuizi's Large Behavioral Model and Sovereign AI
Large Behavioral Model
The Large Behavioral Model is Intuizi's primary LQM, trained on de-identified, real-world behavioral signals. It's built to see the relationships between different types of data and surface patterns across thousands of sequences, and every signal feeds back into the model, so your picture of reality gets sharper over time.
Explore Large Behavioral ModelSovereign AI
Intuizi builds custom LQMs through our Sovereign AI offering, uniquely-tailored to your data. The result is a model you own, hosted on your own infrastructure, in the cloud, or on Intuizi's GPUs, whichever you choose.
Explore Sovereign AIFrequently asked questions about LQMs
LQM stands for Large Quantitative Model. It is a category of AI trained on numerical and structured inputs to forecast, find data links, and answer quantitative questions.
An LLM is trained on textual data to process and generate natural language. An LQM is trained on numerical data to analyze relationships, identify underlying trends, and predict upcoming results.
No, they complement one another. Organizations often pair a language model as the conversational user interface with a quantitative model as the analytical calculation engine behind it.
Structured, quantifiable inputs like transactions, locations, behaviors, pricing, and event logs. The model maps relationships between these inputs to project future outcomes.
The Large Behavioral Model is Intuizi's primary LQM, trained on de-identified behavioral inputs. It adjusts dynamically to new incoming signals so insights remain current.
Yes. Through Sovereign AI, Intuizi builds a custom LQM trained on your data. You own the model, and it's hosted on your own infrastructure, in the cloud, or on Intuizi's GPUs.