Cohere Blog

Compass is Cohere’s retrieval platform for developers building AI applications with their enterprise data. It surfaces the most relevant information from your company’s corpus for use in retrieval-augmented generation (…

Change management has long covered two familiar kinds of change: the rollout of new tools and technologies, and broader human-led transformations such as leadership changes and restructuring. But that distinction starts…

We are announcing the signing of a definitive business combination agreement with Aleph Alpha, following the release of our planned partnership in April of this year. Operating globally as Cohere, the unified company wi…

Cohere and OpenText announced a strategic partnership to help governments and regulated industries move agentic AI from pilot to production. The partnership brings together OpenText’s trusted enterprise data and context…

A perspective from Aidan Gomez, Co-founder & CEO of Cohere Artificial intelligence is remaking the world we live in. Within a generation, the way we discover medicine, manage power grids, and secure our national infrast…

Today, we're releasing North Small Translate, a mixture-of-experts machine translation model with strong performance across 50+ languages. Across WMT26 benchmarks,¹ North Small Translate achieves an 83.6 score across al…

A new serving engine from Cohere wraps the entire decode step for the North Mini Code model in a megakernel, delivering 1.25–1.41x faster end-to-end throughput than vLLM on an H100 and 62% of memory-bandwidth speed of light at batch size 1, without accuracy loss.

Generative AI is now widely available to businesses, but knowing where to use it can still be difficult. Leaders need to separate viable use cases from hype and identify where the technology can address a genuine busine…

Key takeaways Best-in-class value: outperforms leading document parsers and hyperscaler services while remaining cost-effective at enterprise scale. Beyond OCR: understands tables, forms, diagrams, and images to extract…

Over the past year, enterprises and governments have confronted a hard truth: AI systems that rely on external infrastructure can be disrupted without warning by decisions and actions outside their control. Recent model…

Key takeaways Building globally inclusive AI requires moving beyond multilinguality. A model may be fluent in dozens of languages yet still miss the cultural norms, values, and social contexts that shape how people comm…

Today, Cohere and the University of Waterloo announced a new partnership to help students build the practical skills needed to lead AI transformation in the workplace. Through the partnership, Cohere will support the de…

This article contrasts the daily workflow of a senior wealth manager, Dave, with and without AI tools from Cohere's North platform. Without AI, fragmented manual tasks consume most of his time. With AI agents, he can quickly complete client intelligence, research synthesis, compliance, and operations, freeing him to focus on high-value work. Key success factors for AI implementation are also discussed.

Cohere launches North Automations, enabling enterprises to orchestrate end-to-end agentic workflows with built-in simplicity, control, and governance to close the AI ROI gap.

As enterprise AI moves from experiment to necessity, understanding the Total Cost of Ownership (TCO) is critical. This article analyzes visible and hidden AI costs, including token pricing, trade-offs between owning and renting infrastructure, and cost reduction through efficient deployment. Research shows most organizations face unexpected cost overruns, while owning hardware at scale can yield significant savings.

Cohere Labs' Tiny Aya is an open-weight, lightweight multilingual model supporting 70+ languages and capable of running locally. The Expedition Tiny Aya research program brought together global builders to explore applications in education, safety, accessibility, and language understanding, yielding insights and tools that advance multilingual AI.

Cohere proposes Dynamic Speculative Decoding (DSD) that adaptively adjusts the number of draft tokens based on hardware constraints to accelerate LLM inference. It addresses the performance degradation of fixed-k speculative decoding at high batch sizes, validated on dense and MoE models, and is compatible with vLLM's async scheduling and CUDA Graph optimizations.

This article presents a method to automate software fork maintenance using AI coding agents, framing it as a closed-loop feedback system in control theory. Applied to Cohere's fork of vLLM, it reduces the time to absorb upstream releases from weeks to days. The approach includes automated rebasing, measurement collection, and iterative fixing, with a case study on the Cohere Transcribe model.

Cohere automated incident response by connecting its enterprise AI agent platform North to cloud security platform Wiz via a custom Model Context Protocol (MCP) server. The security agent handles the complete workflow from triaging critical findings to generating IR reports, creating tickets, and updating Wiz status, reducing processing time from 30 minutes–2 hours per finding to 20 seconds. The article details architecture, three use cases (toxic combination analysis, assisted incident response, and autonomous weekly posture briefs), results, and implementation steps.

This article discusses the importance of cultural awareness in AI systems, highlighting survey findings that many users face language barriers, cultural misunderstandings, and violations of norms. It calls for AI to be designed with cultural sensitivity to avoid marginalization.

Cohere introduces a new solution for fair scheduling of inference requests across tenants in multi-tenant LLM platforms, combining rate limiting, performance tiers, Deficit Round Robin, and priority selectors to prevent the "noisy neighbor" problem and ensure equitable GPU resource sharing.

Cohere is moving to 100 New Oxford Street, nearly tripling its London office footprint, to support growing R&D and commercial operations. This expansion reflects the company's commitment to the UK AI ecosystem and accelerates its sovereign AI strategy in Europe.

A 2023 paper estimating that 80% of U.S. workers have tasks exposed to large language models has been widely cited by major institutions. However, these scores are based on an older model and U.S. taxonomy, with limitations that compound when applied to policy. Better evidence tools exist but are not reaching policymakers fast enough.

Cohere and Mila announced a new academic research collaboration focused on improving AI evaluation across languages and cultures, starting with French-language cultural context in Quebec. The work aims to help frontier AI models better reflect the linguistic, social, and institutional nuances of Quebec French, moving beyond standardized language performance toward more culturally relevant and trusted AI systems.

As AI adoption expands beyond controlled pilots, mismatches between governance frameworks and actual use can arise. This article explores common AI governance challenges and failure modes, and outlines steps enterprises can take, including building an AI inventory, defining clear ownership, applying risk-based controls, and continuous monitoring.

Enterprise AI adoption typically follows a predictable five-phase progression: experimentation, tool adoption, internal platforms, strategic integrations, and AI-native transformation. Most organizations get stuck between Phase 2 and Phase 3, facing challenges like data access, trust gaps, and fear of model obsolescence. This article focuses on bridging the gap from pilot to production, emphasizing the need for internal platforms, unified data fabric, observability, and model optionality.

Cohere open-sources Command A+, a 218B-parameter (25B active) mixture-of-experts model under Apache 2.0. Optimized for enterprise agentic workflows, it supports 128K input context, 64K generation, and text, image, and tool use. It significantly outperforms prior Command A models in reasoning, multimodal understanding, and multilingual tasks, while enabling efficient deployment via low-bit quantization and speculative decoding. Available on Hugging Face and Model Vault.

Model Context Protocol (MCP) is an open standard that connects AI applications to enterprise systems, simplifying data access and action execution. This guide explains how MCP works, its differences from APIs, RAG, function calling, and agents, common use cases, and security considerations.

AI is increasingly applied to business intelligence to make data more accessible and useful. This article explains what AI in BI means, where it creates value, and key considerations for enterprise adoption.

RWS and Cohere collaborate to build a specialized translation model for Language Weaver Pro, leveraging Cohere's LLM and RWS's language expertise. The model outperforms competitors in 31 of 32 languages, offering cultural intelligence, security, and compliance for enterprise use.