Eight Ways Enterprises Can Leverage DeepSeek

The author references a recent analysis of DeepSeek R1 inference‑related open‑source projects, noting a surge of community contributions that address many enterprise‑focused capabilities.

Enterprises often ask how to use DeepSeek effectively. Based on varying circumstances, the article organizes eight practical approaches.

Three key dimensions shape the choice of approach:

Model development stage: ranging from companies that have not deployed an offline version, to those that have built Retrieval‑Augmented Generation (RAG) applications, fine‑tuned domain‑specific models, or even trained foundational domain models.

Available corpora: some firms only have document collections for retrieval, others possess annotated Q&A data, chain‑of‑thought (CoT) reasoning data, multi‑model datasets, or process‑knowledge datasets.

Typical business needs and pain points: improving retrieval, solving problems via inference, building agent‑style super assistants, automating workflows, or enabling multi‑model data interaction.

Considering these factors, the article presents eight distinct “playbooks” for DeepSeek adoption, illustrated in the following diagram:

The required capabilities and resources can be grouped into four hierarchical levels:

Basic requirement level : essential for any company with data‑security concerns and must be implemented promptly.

Initial improvement level : suitable for organizations that already have a knowledge base and are hesitant about cost or availability, allowing them to start experimenting confidently.

Advanced improvement level : aimed at building super assistants, extensive automation, and dynamic adaptation, leveraging the latest DeepSeek breakthroughs.

High‑end improvement level : targets low‑cost multimodal solutions or the synthesis of multiple specialized models.

In summary, DeepSeek offers a wealth of ideas that dramatically expand the possible enterprise applications of large language models, and the author expects many new breakthroughs to emerge in the second half of the year.