Data Panda lands your business data in one warehouse and lets Cohere read it. Command A answers questions over your own documents, Embed v4 turns them into a vector index for retrieval, and Rerank decides which records the model gets to see.
Cohere was founded in Toronto in 2019 by Aidan Gomez, Nick Frosst and Ivan Zhang. Aidan Gomez is one of the eight co-authors of the original Transformer paper that started the modern LLM wave. The company stayed deliberately B2B from day one: no consumer chatbot, no free playground for the masses, just an API and a sales team that walks into financial-services, healthcare, manufacturing, telecom and public-sector accounts. Strategic backers include Cisco, Fujitsu, AMD Ventures, Nvidia and Salesforce Ventures.
The product line is three models and a workplace layer. Command A (command-a-03-2025) is the flagship chat and agent model with a 256K-token context window and 8K output. Command R+ and Command R (command-r-plus-08-2024 and command-r-08-2024) sit underneath at 128K context, and Command R7B is the small fast tier. Embed v4 takes text, images, tables and graphs and returns vectors at 256, 512, 1024 or 1536 dimensions for over a hundred languages. Rerank v4 reorders up to 32K tokens of candidates per query in multilingual mode. North is the enterprise workspace built on top, Compass is the search layer, and the whole stack runs on the Cohere API at api.cohere.com, on AWS Bedrock and SageMaker, on Google Vertex, on Azure AML, on Oracle GenAI Service, or fully private inside a customer VPC or on-premise.
Cohere usage and retrieval performance per workflow, side by side with the warehouse content the model is reading.
Pipe Command A and Rerank decisions straight into the systems where the work gets done.
Command A reads what is in the warehouse, Embed v4 indexes it, Rerank orders it.
Internal tools that sit on warehouse data and call Cohere only for the language work.
A list of concrete reports, automations and AI features we have built on Cohere data. Pick the one that matches your situation.
Can our RAG setup find the right document?
Rerank v4 hit rate per query template, plotted against the warehouse table the answer should have come from. Surfaces the queries where Embed v4 returns plausible-looking neighbours but Rerank cannot push the truly relevant document into the top three, which is the moment a RAG pipeline starts answering with confident wrong information.
Which workflows are spending the most on Cohere, and is the spend producing answers people use?
Token spend per Cohere endpoint joined to the internal workflow that triggered the call and to the user feedback on the answer. Shows the support-bot workflow burning the bulk of the Command A budget while half the answers get a thumbs-down, next to the proposal-drafting workflow that costs less and gets accepted edits on most outputs.
Is our vector index keeping up with the warehouse?
Embed v4 coverage report per warehouse table: rows indexed, rows changed since last embed, rows never embedded. Catches the contract table that stopped re-indexing after a schema change three weeks ago, which is why the assistant keeps citing last quarter's pricing.
Token spend per Cohere endpoint, per workflow and per business unit, joined to the workflow's measured outcome. The AI line on the budget moves from a single Cohere invoice to a number that sits next to the support-time saved and proposals shipped.
Proposal- and RFP-drafting assistant on top of the registry of past winning offers, so the rep starts from the closest matching paragraph instead of a blank document. Time-to-first-draft and acceptance rate of the drafted paragraphs become visible per account team.
Cohere usage, retrieval-hit rate and answer-quality feedback per workflow on one screen, refreshed daily. The RAG pipeline is followed as a curve, not rediscovered the morning a stakeholder forwards a screenshot of a wrong answer.
You keep the reporting tool you already have. We connect it to the warehouse where your Cohere data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Command A (command-a-03-2025) is the chat and agent model with a 256K-token context window and 8K output, used for the actual question-answering. Embed v4 is the embedding model with flexible output dimensions of 256, 512, 1024 or 1536 and support for 100+ languages plus images, used to turn warehouse content into a vector index. Rerank v4 reorders up to 32K tokens of candidates per query in multilingual mode, sitting between the vector search and the chat model. Command R+, Command R and Command R7B cover the cheaper and faster tiers when full Command A is overkill.
Yes. Cohere publishes Command, Embed and Rerank on AWS Bedrock, AWS SageMaker, Google Cloud Vertex AI, Azure AI (AML) and Oracle GenAI Service alongside the direct API at api.cohere.com. Billing and usage telemetry then live in the cloud provider's console rather than in Cohere's dashboard, so cost reporting has to come through the relevant cloud-billing connector. Mixed setups end up with two sources joined in the warehouse on workflow, model and time window.
Yes. Cohere supports private VPC deployments and on-premise installations for customers who cannot let regulated data leave their network, which is the main reason banks, insurers, hospitals and public-sector organisations end up on Cohere rather than a consumer AI provider. The same Command, Embed and Rerank models are available in those modes, although the licensing and infrastructure footprint go through Cohere sales rather than a self-serve API key.
We review your Cohere setup and the systems around it. Together we pick the first thing worth building.