Question 1

What is a Knowledge Graph Transformer?

Accepted Answer

A Knowledge Graph Transformer is a neural architecture that operates directly on graph-structured data rather than token sequences. Instead of converting text to tokens, Inertia's model traverses knowledge graph nodes and edges, preserving relational structure and enabling full provenance tracking of every output.

Question 2

How does Inertia eliminate hallucinations?

Accepted Answer

Inertia's outputs are grounded entirely in the knowledge graph. The Knowledge Graph Transformer can only produce results that trace back to existing nodes and relationships. If information doesn't exist in the graph, it cannot appear in the output causing hallucination to be architecturally impossible.

Question 3

What is graph provenance?

Accepted Answer

Graph provenance is a complete attribution chain showing exactly which knowledge graph nodes and edges contributed to each part of an output. Every claim in the model's output links back to its source data, providing full traceability and auditability upfront.

Question 4

How is this different from RAG (Retrieval-Augmented Generation)?

Accepted Answer

RAG retrieves text chunks and feeds them to a standard LLM, which can still hallucinate or misinterpret the retrieved context. Inertia operates directly on the graph structure itself. There is no retrieval step, no token conversion, and no generation from probability distributions. The output is deterministically derived from the graph.

Question 5

What kind of output does Inertia produce?

Accepted Answer

Inertia's AI model produces code snippets, files or entire codebases depending on the user's request. These outputs are designed to be fully functional and easily deployable.

Question 6

If I want Inertia to build an AI-model for my domain specifically, do I have to provide a Knowledge Graph?

Accepted Answer

No, Inertia provides ingestion tools that automatically construct knowledge graphs from your existing data sources such as databases, document stores, APIs, and file systems. At the enterprise level, we offer custom models to suit your environment.

Question 7

Is Inertia suitable for real-time applications?

Accepted Answer

Yes. The Knowledge Graph Transformer's graph traversal is highly parallelizable and typically completes in milliseconds. Inertia is designed for production workloads where hallucinations aren't just a bottleneck, but an unnacceptable outcome in every case.

Question 8

What industries is Inertia best suited for?

Accepted Answer

Inertia is particularly valuable in industries where accuracy is non-negotiable: healthcare, financial services, legal, regulatory compliance, and safety-critical systems. Any domain where hallucinations or illegal outputs carry real consequences benefits from our architecture.

Question 9

How does Inertia handle data privacy?

Accepted Answer

Your knowledge graph data is isolated in dedicated infrastructure. Inertia does not use customer data for model training.

Question 10

What does 'no tokenization' actually mean?

Accepted Answer

Traditional LLMs convert text into tokens, numerical representations that lose structural and relational information. Inertia's Knowledge Graph Transformer bypasses tokenization entirely, operating on the native graph structure. This preserves relationships, eliminates token-boundary artifacts, and enables the provenance tracking that makes hallucination-free output possible.

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