Artificial Intelligence | Tom Sawyer Software

AI systems can struggle with incomplete context, inconsistent results, hallucinations, limited explainability, and disconnected enterprise data.

When an answer depends on relationships, AI needs access to those relationships. Graph technology connects data, context, dependencies, and provenance so AI systems can retrieve better information, ground and validate results, and provide greater visibility into how those results were produced.

From preparing and connecting data to grounding AI models, orchestrating agents, validating results, and helping people explore AI output, graph technology can support the entire AI workflow.

Tom Sawyer Software's graph technology turns scattered enterprise data into connected AI context.

Give AI the context behind your data

AI systems often work with information as individual documents, records, or pieces of content. Graphs add another layer by making the relationships between that information explicit.

Knowledge graphs can connect entities, concepts, events, systems, and other information into a shared model that gives

AI richer context. These relationships can help AI retrieve more relevant information, follow dependencies across multiple steps, and better understand how individual pieces of information relate to one another.

Vectors and graphs provide different types of context for AI. Vector search helps identify information that is semantically similar, while graphs make explicit relationships, dependencies, and paths between information available to AI.

How graph technology supports the AI workflow

Graph technology can support AI from the underlying data layer through AI processing and into the applications people use to understand and act on results.

Graph technology supports the entire AI workflow.

Connect and prepare enterprise data

AI results depend on the quality and context of the underlying data. Graph technology can integrate structured, semi-structured, streaming, and other enterprise data while preserving relationships across sources.

Connecting this information creates a stronger foundation for AI systems and reduces the risk that important context remains trapped in disconnected applications or data silos.

Ground AI with connected knowledge

Knowledge graphs give AI systems access to trusted information and the relationships within it. Rather than relying only on semantic similarity, AI can retrieve connected information and follow relevant paths between entities.

This connected context can improve relevance, support more complex questions, and make it easier to understand the information used to produce a result.

Model and manage complex AI systems

Modern AI systems increasingly include multiple models, agents, tools, data sources, and workflows. Graphs can represent these components and the dependencies between them as a connected system.

Visualizing and analyzing the AI architecture can help teams understand how information moves through a system, identify unnecessary or ineffective components, trace intermediate results, and determine where unexpected output was introduced.

Validate and explain AI results

Graphs create visible connections between AI output and the underlying information that supports it.

Teams can trace relationships back to source data, inspect multi-hop connections, compare results against trusted knowledge, and visualize how information moved through an AI workflow. This can improve confidence in AI-assisted decisions and make results easier to investigate and explain.

Visualize and explore AI output

AI-generated text is not always the best way to understand complex results.

Graph visualization can reveal relationships, dependencies, evidence paths, patterns, anomalies, and agent execution flows that may be difficult to recognize from generated text alone.

Interactive graph applications also allow users to explore AI results directly, investigate connected information, and move from a generated answer into deeper analysis.

Graph technology across your AI workflow

Tom Sawyer Software provides graph technology solutions across the AI workflow, from preparing and connecting enterprise data to building graph-enabled applications and helping end users explore AI results.

Tom Sawyer Data Streams

Tom Sawyer Data Streams integrates, transforms, and processes data from multiple sources to create connected data foundations for AI systems.

Organizations can use Data Streams to move heterogeneous enterprise data into graph-based environments while preserving the relationships and context AI applications need.

Tom Sawyer Perspectives

Tom Sawyer Perspectives provides data federation, graph visualization, analysis, and application development capabilities for building custom AI-enabled solutions.

Developers can create applications that combine enterprise data, graph analysis, AI services, and interactive visualization while maintaining connections to existing systems and data sources.

Tom Sawyer Explorations

Tom Sawyer Explorations gives analysts and other end users an interactive environment for investigating connected data and AI-generated results.

Users can explore relationships, patterns, dependencies, and context visually without having to know query languages, helping them move beyond generated answers to understand the information behind the results.

Graph technology for more effective artificial intelligence

Graph technology can support a wide range of AI architectures and use cases.

Graph retrieval-augmented generation combines retrieval with graph relationships to give AI access to richer connected context.

Graph RAG

Graph retrieval-augmented generation combines retrieval with graph relationships to give AI access to richer connected context.

In addition to finding relevant information, graph-based retrieval can traverse related entities and multi-hop relationships. This helps AI answer questions that depend on how information is connected and makes the supporting context easier to trace.

Knowledge graphs can provide trusted enterprise information and connected context as input to generative AI and large language models.

Generative AI and LLM grounding

Knowledge graphs can provide trusted enterprise information and connected context as input to generative AI and large language models.

Grounding AI with relevant graph data can improve the accuracy and relevance of results while reducing reliance on information contained only within the model.

Graphs can also help organizations compare generated results against known relationships and data.

A graph model of an agentic system can represent these components and their relationships provides a clearer view of how agents collaborate.

Agentic AI orchestration

Agentic AI systems may contain many interconnected agents, tools, models, data sources, tasks, and decision points.

A graph model can represent these components and their relationships, giving teams a clearer view of how agents collaborate, where information is passed, and which components influence a result.

Graph visualization and analysis can also help identify duplicated functions, inefficient workflows, and unexpected dependencies.

Graph technology can provide a connected view of information used throughout AI workflows.

AI data engineering

AI systems often rely on information from many different sources and produce data in formats such as JSON, RDF, and other structured or semi-structured representations.

Graph technology can federate, integrate, and analyze heterogeneous information while maintaining relationships across sources. This helps reduce data siloing and gives engineers and analysts a connected view of information used throughout AI workflows.

Graphs can provide structure and context throughout AI data pipelines.

AI pipelines

Graphs can provide structure and context throughout AI data pipelines.

Organizations can connect source data, transformations, models, agents, intermediate outputs, and final results so that relationships are maintained throughout processing. This creates greater visibility into how data and results move through the AI system.

Persisted graph data can provide AI agents with historical information.

Preserve AI context and history

Graphs can persist relationships between prior interactions, decisions, entities, results, and other relevant context.

AI agents can retrieve this connected history during future interactions, helping preserve continuity and make previously established information available when appropriate.

Graphs provide a natural model for representing highly interconnected AI solution components and their dependencies.

Composite AI systems

Enterprise AI solutions can include LLMs, agentic systems, machine learning models, neural networks, analytics, business applications, and traditional data systems.

Graphs provide a natural model for representing these highly interconnected components and their dependencies. Graph visualization and analysis can help organizations design, understand, and manage increasingly complex AI architectures.

Try out these example applications

Sign up and test drive these example applications built with Tom Sawyer Perspectives. Improve generative AI results with Graph RAG, discover fraud, and analyze workflows.

Example Launch Failure application built with Tom Sawyer Perspectives.

Launch Failure

Improve generative AI results with interactive knowledge graph navigation through the no-code pattern-matching query builder, and apply Graph RAG with links to supporting documentation.

Example Fraud application built with Tom Sawyer Perspectives.

Fraud Detection

Uncover hidden connections through visualization and navigation of AI analytics results. The combination of AI and traditional analysis leads to deeper understanding of the extent of fraudulent activities.

Example Retail Network application built with Tom Sawyer Perspectives.

Retail Network

Follow the support tickets of locations in a global enterprise. Correlation between logical and geospatial data adds context to AI results.

Make AI easier for people to use

Graph technology can also improve how people interact with AI systems and their results.

Ask questions of connected data

Natural-language interfaces can allow users to explore connected information without knowing graph query languages such as Cypher or Gremlin.

Subject matter experts can ask questions using familiar language while the underlying graph provides access to relevant entities, relationships, and context.

Explore results visually

Interactive graph visualization allows users to investigate the information behind an AI result rather than relying only on a generated response.

Users can follow relationships, expand connected information, identify patterns, and understand the dependencies that contributed to an answer.

Extend AI into graph applications

AI assistants and AI-enabled browsers can add another layer of interaction to graph applications.

They can help summarize visualizations, explain relationships, identify relevant information, and provide natural-language access to the data and analytics already available within an application.

Learn more about graph technology and AI

As enterprises and public sector organizations move beyond generative AI toward autonomous, agentic systems, the ability to understand relationships, context, and operational dependencies has become increasingly critical.

Watch this informative video with guest speaker Karthi Subramaniam, Founder of Frizb, as he explores how graph technologies provide the foundation for operational intelligence and next-generation AI systems.

Karthi Subramaniam of Frizb explores how graph technologies provide the foundation for operational intelligence and next-generation AI systems.

FAQs about graph technology and AI

What is graph technology for AI?

Graph technology represents information as entities and relationships, allowing AI systems to use not only individual data points but also the connections, dependencies, and context between them. This can support better retrieval, grounding, explainability, validation, and analysis.

What is the difference between vector search and graph technology for AI?

Vector search is designed to find semantically similar information, while graph technology represents explicit relationships between information. The two approaches are complementary and can be used together to give AI both similarity-based retrieval and connected context.

Can I use graph technology if my AI system already uses vector search?

Yes. Graph technology can complement vector search rather than replace it. Vector search helps AI find semantically similar information, while graphs provide explicit relationships, dependencies, and connected context. Used together, they can give AI a more complete foundation for retrieval and generation.

How can knowledge graphs improve generative AI and LLM results?

Knowledge graphs provide connected, structured information that can be used to ground AI responses in enterprise data. They can help AI retrieve more relevant context, follow relationships across multiple entities, and make the supporting information behind a response easier to trace.

Can graph technology help reduce AI hallucinations?

Graph technology can help reduce the risk of hallucinations by grounding AI systems in trusted, connected data and making relevant relationships available during retrieval and generation. It can also support validation by allowing generated results to be compared with known data and relationships.

How does Tom Sawyer Software support AI applications?

Tom Sawyer Software supports multiple stages of the AI workflow. Tom Sawyer Data Streams helps integrate and prepare connected data, Tom Sawyer Perspectives supports data federation, graph analysis, visualization, and custom application development, and Tom Sawyer Explorations helps end users investigate connected data and AI-generated results.

How can I determine whether graph technology is useful for my AI application?

Graph technology is especially useful when AI results depend on understanding relationships, dependencies, provenance, or context across multiple sources. It can also be valuable when organizations need to connect AI with existing enterprise systems, visualize AI results, trace how results were produced, or manage complex agentic workflows.