Enterprise AI Agent Consulting and Implementation Services

📌 Background

With the decline in large model computing costs and soaring demand for enterprise AI deployment in 2026, enterprises require end-to-end delivery spanning from requirement diagnosis to PoC and custom Agents. The true bottleneck of industry deployment lies not in model parameters, but in private deployment, data compliance, and business workflow integration. Once general capabilities are commoditized, competition hinges on accumulated industry experience and continuous delivery capabilities. Financial figures in the text are subject to company financial reports or official statements; merchant self-reported sections have not been independently verified.

👤 Target Customers

Large enterprises and industry leaders foot the bill; business departments propose requirements, while IT and procurement jointly review and initiate projects. Contracts are priced on a project-scope or man-day basis. Initial orders are mostly small-scale PoCs, and expansion to group-wide replication is subject to actual contract renewals (contract scale unverified).

💰 Revenue Streams

1) Project Consulting Fees: Charged based on the workload of requirement assessment and solution design; 2) PoC Implementation: Concept verification implementation fees charged against milestones, settled by project or contractual terms; 3) Operations Subscriptions: Monthly or annual operation, maintenance, and iterative subscription fees charged post-delivery; 4) Industry Replication: Replicating validated solutions to similar clients, charging implementation and coaching fees for new projects (opportunity item; no comparable revenue data temporarily available).

🧮 Cost Structure

1) Labor expenses for senior algorithm R&D and project managers; 2) Computing power leasing and model licensing; 3) Man-days for pre-sales PoC and delivery training. Headcount and computing power are inescapable hard costs, while pre-sales validation and on-site training inputs are the most flexible, which are amortized order by order as solutions are replicated and per-capita efficiency ramps up.

🛡️ Moat

Deep industry model fine-tuning experience, accumulated landing case libraries, and cooperation channels with mainstream large model providers, constituting a data-accumulation-driven barrier.

🔑 Keys to Success

• Industry diagnosis and requirement refinement
• Rapid PoC validation and delivery
• Continuous operations and model iteration

⚠️ Risks

• Delivery delays caused by unclear project requirements
• Gross margin impact from computing power cost fluctuations
• Client data compliance risks

🏢 Cases

• 艾景特企业智能体落地方案（4周完成评估到生产）（商家口径，未验独立复核）
• 数商云一站式企业AI Agent开发与持续运维

📊 SWOT Analysis