Cohere Blog ·
A source describing Compass, Cohere’s retrieval system, covering its cloud deployment rationale, cost benefits from precise retrieval, design requirements for agentic workflows, and accuracy gains—benchmarked on a financial RAG task. Lisez 4 points de vue avec leurs éléments à l’appui et les liens vers les sources.
Teams consistently asked for a managed Compass offering—wanting its best-in-class retrieval without the operational overhead of self-hosting.
Lire le moment probant · Paragraphe 5More precise retrieval delivers smaller, higher-quality inputs to generative models, cutting inference costs and task completion time. Retrieval is among the most effective cost levers available to businesses today.
Lire le moment probant · Paragraphe 10Agents may issue dozens of queries per task. Retrieval systems must therefore perform reliably across sequences of machine-generated queries—not just single-shot searches—while maintaining relevance, enforcing permissions at every step, and avoiding latency accumulation.
Lire le moment probant · Paragraphe 11On the High Finance benchmark—a Cohere-built investment-banking RAG workload—Compass improved accuracy by 14–16 points over Azure Search (64.8 → 81.1). That gap can distinguish between unsatisfactory and great end-user answers.
Lire le moment probant · Paragraphe 25Passages attribués et accompagnés du contexte nécessaire à leur vérification. Ouvrez le texte original pour vérifier la source.
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Customer demand for a managed option has been clear and consistent: teams want Compass' best-in-class retrieval capabilities, but many do not want the operational overhead that comes with self-hosting.
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Token economics: Every irrelevant result passed to a model consumes tokens and occupies limited context space. More precise retrieval creates smaller, higher-quality inputs, reducing inference costs and cutting the time needed to complete a task. Retrieval is one of the most effective cost levers available to businesses today.
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The figure below is one instance of the accuracy gain over traditional or standalone search infrastructure on a representative financial-industry RAG workload: embed a query, retrieve presentation materials from an index, and score the top results. On High Finance, a Cohere-built investment-banking benchmark, Compass achieved a 14-16 point improvement on Azure Search (from 64.8 to 81.1). A gap of this size can be the difference between an unsatisfactory answer and a great one for the end user.
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Agentic access patterns: Agents may issue dozens of queries while completing one task, reformulating requests and traversing multiple sources. In these multi-hop loops, latency accumulates, relevance can drift, and permissions must be enforced at every step. Retrieval must therefore perform reliably across sequences of machine-generated queries, not only single-shot searches.
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