Beyond Text: AI Models for Every Data Type

Generative AI is bigger than ChatGPT.

Large Language Models are one type of generative AI. They generate text. But generative AI also includes models that generate predictions from tabular data, forecasts from time-series, insights from relationship networks, and structured queries from natural language.

These specialized models share the same foundation: neural networks trained on massive datasets to recognize patterns and generate useful outputs. The difference is what they're trained on and what they produce.

• LLMs trained on internet text. They generate text.
• Tabular models trained on structured datasets. They generate predictions.
• Time-series models trained on sequential data. They generate forecasts.
• Graph models trained on relationship networks. They generate pattern insights.

Each model type works best on the data it was built for. Enterprise runs on all of these data types. Using the right model for each unlocks capabilities that text-only AI can't deliver.

Tabular Data: Rows and Columns

Tabular data from any source. Excel files, CSV exports, database tables, analytics platforms. If it has rows and columns, it counts. Customer records, transaction logs, sensor readings, financial statements. This is the most common data format in business.

Several production models now handle tabular prediction without the setup time of traditional machine learning. They learn patterns from your data and generate predictions instantly.

Time-Series Data: Sequences Over Time

Revenue by month. Daily website traffic. Hourly energy consumption. Stock prices. Sensor readings. Any measurement tracked over time creates a sequence with trends, seasonality, and patterns.

Time-series foundation models learned patterns from billions of data points across industries. They forecast accurately even with limited history, recognizing trends they've seen before in other contexts.

Knowledge Graphs: Connecting Information

Documents, databases, and systems across your organization contain related information that isn't explicitly linked. A customer mentioned in a support ticket, a sales opportunity, a contract, and an invoice are all connected. But that connection lives in people's heads, not in your systems.

Knowledge graph platforms extract entities from documents, identify relationships, and create a queryable network of organizational knowledge. They turn scattered information into connected structure.

Graph Analytics: Patterns in Relationships

Some data is best understood as connections. Customers linked to products through purchases. Accounts linked through transactions. Suppliers linked to manufacturers linked to distributors.

Graph AI models analyze your database as a connected network. They find patterns that exist in relationships rather than individual records.

Structured Queries: Natural Language to Database

Databases store enormous value. Accessing that value requires SQL or similar query languages. This creates a bottleneck: business users need data, but query languages require training.

Text-to-SQL models translate plain English into accurate database queries. They understand table structures, handle joins, and respect your schema.

Connecting Models: Orchestration

Specialized models need to connect with LLMs and each other. A fraud detection workflow might pull transaction data with SQLCoder, run predictions through a graph model, then have an LLM summarize findings for analysts. A forecasting app might combine TimesFM projections with knowledge graph context about market conditions.

Different orchestration tools serve different needs.

LangGraph from LangChain suits developers building custom workflows. You define nodes, edges, and state transitions in code. Good for complex logic with branching and loops. Requires engineering effort but gives full control.

Amazon Bedrock Agents works for teams already on AWS who want managed infrastructure. Handles tool routing, memory, and scaling. Less flexible but faster to deploy.

Vertex AI Agent Builder offers similar managed orchestration on Google Cloud, with built-in grounding to enterprise data sources.

AutoGen from Microsoft takes a different approach: agents as conversational participants that collaborate through dialogue. Better suited for research and exploratory workflows than production pipelines.