10 years of enterprise system experience across strong-compliance, multi-system integration, and stable delivery contexts.
AI落地内参AI落地内参
Turn AI into real business workflows, not just demos.
AI Implementation Insider serves SMBs with real operational pain, covering scenario diagnosis, technical roadmap design, system delivery, and ongoing iteration.
Submit an AI Implementation RequestFounded by a technical consultant with 10 years of enterprise system experience
Background includes desensitized work around financial regulation, leading securities firms, and healthcare systems.
The technical focus is Python and the LLM application stack: RAG, agent workflows, API integration, data masking, access control, private or hybrid deployment planning, and existing system integration.
The hard part is business integration, not the demo
Companies usually do not fail simply because they cannot call model APIs. They fail because demos do not enter existing OA, CRM, ERP, knowledge-base, and business workflows, or because data, permissions, process design, and iteration are missing.
Implementation method
- 01Business diagnosis
- 02Scenario selection
- 03Technical roadmap design
- 04Fast prototype validation
- 05System delivery and ongoing iteration
Suitable cooperation
- Teams with stable business, real workflows, and a clear business owner.
- Repeatable work around documents, support, sales, audit, reports, or knowledge bases.
- A reasonable budget and willingness to provide process details, data, and samples for validation.
Unsuitable cooperation
- AI trend-following without a concrete business pain or business owner.
- Teams unwilling to provide workflows, data, or samples while expecting deep custom delivery.
- Low-cost outsourcing expectations, automatic revenue doubling expectations, fixed commercial-result guarantees, or non-compliant data, privacy, and risk use cases.
Public service path
L1 AI diagnosis and feasibility assessment
Assess whether the business problem, workflow, data, and team cooperation are ready for AI implementation.
L2 technical architecture and implementation roadmap
Turn model capabilities, RAG, agent workflows, permissions, integration, and iteration into an executable plan.
L3 end-to-end delivery and ongoing iteration
Deliver working systems, documentation, and measurable iteration paths connected to real operations.
Cooperation boundary
AI outcomes depend on workflow, data quality, people cooperation, and technical systems. The service commits to agreed working systems, documentation, and measurable iteration paths, but does not commit to fixed revenue growth, fixed cost reduction, or absolute commercial outcomes.