Contextual AI is built around retrieval accuracy: jointly optimizing how the retriever surfaces passages and how the generator responds to them. Meibel is built around what feeds that retrieval step: the document structure, the cross-document citation graph, and the confidence score on the output. Teams that want better accuracy on a corpus they've already indexed and teams that want to change what the index contains are solving different problems.

Contextual AI reduces hallucination at the generation layer through a purpose-trained Grounded Language Model that's optimized for faithfulness. Meibel reduces it upstream: cross-document references are resolved before retrieval runs, and every output is scored across six dimensions before it reaches production. One approach improves how the model generates. The other changes what the model has access to and flags the result before it leaves the system.

No. Document Intelligence handles structured and unstructured retrieval natively. There is no external vector database to provision, tune, or maintain.

That's the core of what Document Intelligence is built for. When a spec references a drawing that references a materials standard, Meibel traces that chain at the corpus level, not at query time. Agents built on Meibel execute across that graph natively. The reasoning follows the document relationships because those relationships were built into the corpus before any query ran.

One corpus, any number of agents on top. Meibel lets you process your documents once and build as many solutions as you need without reprocessing or rebuilding the pipeline for each use case. As the problem grows from one retrieval use case to multiple agents across the same document set, the platform scales with it. A retrieval layer optimized for one workflow doesn't.