VIDIZMO integration brief · Cohere · vidizmo.ai/integrations/catalog/cohere

Integrations / Cohere

Cohere

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Cohere builds models aimed at enterprise retrieval and generation, from a Canadian company with deployment options that keep data in-country.

It appears where a buyer's sovereignty requirement is Canadian, and where reranking quality on retrieval matters as much as generation.

Built for Retrieval, Not Just Generation

Open as a two-page brief

Cohere is a Canadian company building models aimed at enterprise retrieval and generation, with deployment options that keep data in-country. Two things bring it into a shortlist here: a sovereignty requirement that is specifically Canadian, and the fact that its models are built for the retrieval half of the problem rather than only the writing half.

Nexus your video, audio, images and documents, indexed under your access rules content the asking user may see AI Intelligence Hub agents, chat, workflows, retrieval with citations; model chosen per agent prompts and embeddings over API Cohere model endpoint, hosted by the provider or on your own servers answers Users chat, search, workflow output Cohere VIDIZMO

How it connects

The model behind an agent or a node is configuration. Cohere is selected per deployment or per agent.

Retrieval quality is where this provider earns its place, and it is worth being precise about why. An assistant answering from a large library fails more often because the wrong passages were retrieved than because the model wrote badly. Cohere's models are built with that half in mind, so the gain shows on a big, mixed library rather than on a small tidy one.

An engagement should note the relationship honestly: Cohere also competes with AI Intelligence Hub in enterprise retrieval, and the competitor library records that. Using it as a model provider and meeting it across the table on a deal are both normal, and neither cancels the other.

What you can do together

  • Keep inference in Canada, where the sovereignty requirement is specifically Canadian.
  • Use models built for retrieval where the library is large and mixed, which is where retrieval rather than writing is the failure.
  • Change which agent uses which provider as configuration, per workload.
  • Combine with a different provider for generation if the evaluation favours splitting them.

A scenario

  1. ScopingThe engagement confirms the residency requirement and which deployment option satisfies it.
  2. SetupCohere is configured as the provider in AI Intelligence Hub.
  3. EvaluationThe body tests answers on its own library, since retrieval gains are library-specific rather than general.
  4. In useAnswers are generated in-country, with retrieval running under the asking user's identity.
  5. ProcurementThe competitor relationship is disclosed rather than discovered, because it is recorded.

What stays where

Cohere supplies the models

The catalogue, the deployment options and the licensing stay with Cohere.

Residency is the reason, and it is confirmed

Which deployment option keeps data in-country is established rather than assumed.

It is also a competitor

Cohere competes in enterprise retrieval, and the competitor library records it; both relationships hold at once.

Content governance does not move

Retrieval runs under the asking user's identity whichever provider generates the answer.

Products and solutions

Next step

See it on your own Cohere instance. We will show the connection made, the data moving and the output, then size it for your deployment.

Request a demonstration  or write to sales@vidizmo.ai

sales@vidizmo.ai  ·  vidizmo.ai/integrations/catalog/cohere

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