Generative AI
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Requirement analysis
Product Analysis
Key Points of the Requirement
Define the use cases (content generation, code assistance, customer operations, or enterprise knowledge search), the model strategy (frontier-model APIs, open-weight fine-tuning, or hybrid RAG), and data-privacy requirements that dictate on-premises versus cloud processing. Specify evaluation metrics (accuracy, hallucination rate, latency), integration scope with existing systems, maintenance expectations, and the team that will own the solution after delivery.
Cost Structure Analysis
Costs combine GPU training and inference capacity, data preparation and labeling, engineering for fine-tuning, retrieval pipelines, and guardrails, plus ongoing evaluation and model refresh. API-based approaches shift cost to per-token usage; self-hosted open-weight models shift it to infrastructure and MLOps. The dominant hidden cost is continuous evaluation and prompt or retrieval tuning after launch.
Estimated Cost Range
Indicative pricing: proof-of-concept projects $10,000–$50,000; production RAG or fine-tuned deployments $50,000–$300,000; multi-workspace enterprise programs $300,000 and above. SaaS alternatives run $20–$100 per user per month. Final quotations depend on data volume, integration complexity, and hosting model.
Procurement Considerations
Contract for evaluation on your own data before full commitment, with acceptance criteria on your metrics rather than vendor demos. Clarify IP ownership—who owns fine-tuned weights, prompts, and pipelines—and data-handling terms including model-training prohibitions on your data. Require guardrail and hallucination-rate KPIs, a model-upgrade path as base models improve, and portability (standard formats, documented pipelines) to avoid lock-in.
Supplier shortlist
Top 3 matched suppliers
A comparison-ready shortlist balancing quality systems, product capability, capacity, and commercial flexibility.
#1 recommended
Beijing Zhipu Huazhang Technology Co., Ltd.
A leading Chinese AI startup originating from the Knowledge Engineering Group (KEG) at Tsinghua University. The company focuses on developing large-scale pre-trained models and has released the GLM (General Language Model) series, providing comprehensive generative AI solutions for enterprises.
#2 recommended
Beijing Baichuan Intelligent Technology Co., Ltd.
Founded by Wang Xiaochuan, the former CEO of Sogou, Baichuan AI is dedicated to the development of large-scale language models. They provide high-performance generative models that support both Chinese and English, catering to developers and enterprise clients.
#3 recommended
Beijing Moonshot AI Technology Co., Ltd.
Moonshot AI is a prominent generative AI company known for its 'Kimi' chatbot. They specialize in long-context processing capabilities, allowing their models to handle exceptionally large amounts of text data for analysis and generation.
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