About AI Lao Pao¶
One-line¶
AI delivery architect with 200+ AI system deployments, specializing in private LLM deployment, RAG, agents, AI infrastructure, and enterprise workflow automation.
Background¶
- AI Delivery Architect — 200+ AI system deployments end-to-end (requirements → acceptance)
- Multiple enterprise AI architectures and system integrations, including complex network topologies, remote automated deployment, hardware selection, stress testing, and production rollout
- Focus areas: private LLM deployment · RAG · AI agents · AI infrastructure · enterprise workflow automation
- Working experience across commercial models, open-source models, and privately-tuned models (vLLM · transformers · RAG · agents · model gateway)
- Proprietary model: AW36
What I believe¶
- Customers don't buy AI. They buy business outcomes.
- Acceptance ≠ launch. Pass = still stable 14 days after go-live.
- Private deployment is a total-cost question, not a GPU question. Peak concurrency, p95 latency, data boundary, failure attribution, monitoring, and cost caps must be computed before hardware selection.
- AI GEO isn't ranking gaming. It's making AI engines willing to cite you.
What I don't do¶
- No "guaranteed ChatGPT recommendation" promises (that's a red flag)
- No click-farm, machine-written, un-cited content factory work
- No touching customer-sensitive data outside contracted API boundaries
- No projects I can't sign acceptance criteria for
Domain expertise¶
- Private LLM deployment: on-prem vLLM stacks with Qwen / DeepSeek / Llama / GLM, GPU sizing (A800 / H20 / 4090), inference optimization (quantization, tensor parallelism), high-availability clustering
- RAG systems: vector databases (Milvus / Qdrant / Weaviate), embedding pipelines (bge / m3e / jina), retrieval strategies (hybrid search, reranking, filters), knowledge base ingestion at scale
- AI agents: multi-step planning, tool calling, ReAct / CodeAct patterns, agent frameworks (LangGraph / AutoGen / custom), memory design, failure recovery
- AI infrastructure: model gateways (LiteLLM / Portkey / custom), cost tracking, rate limiting, model routing, provider fallback, observability
- Enterprise workflow automation: cross-system orchestration, business process automation with human-in-the-loop, integration with MES / ERP / CRM / HIS
Delivery track record¶
Domains delivered to: - Manufacturing (CNC, PCBA, injection molding, industrial automation) - Healthcare (hospitals, medical devices, pharmacy) - Cross-border e-commerce (Amazon, DTC brands, B2B factories) - Education & training (K12-adjacent, vocational, enterprise training) - Government & SOE (procurement, compliance-heavy) - Finance-adjacent (fintech infrastructure, compliance) - SaaS & DevTools - Local services (legal, accounting, marketing agencies)
Working principles¶
- Contract-first. Quantified acceptance criteria in writing, not screenshots.
- Retest set client-owned. Vendor never controls the truth.
- Data-boundary documented. Which data can leave premises — tier by tier.
- Cost per user / per API key. Not just total-bill.
- SLA with penalties. No teeth = no signature.
- Model + prompt versioned. A/B switchable at demo time.
- Client dashboard. You see what I see.
- Exit clauses. Your data / your models / your prompts / your logs are yours.
Full: Vendor Acceptance Checklist
Public presence¶
| Channel | Handle | Language |
|---|---|---|
| Douyin (China TikTok) | AI 老炮 / 92454365424 | Chinese |
| TikTok | @ailaopao | English |
| YouTube | @AI 老炮 | Chinese + English |
| X | @ailaopao | English + Chinese |
| WeChat Video | AI 老炮真话 | Chinese |
| Toutiao / Zhihu / CSDN / Juejin | AI 老炮 | Chinese |
Data boundary¶
All content on this site is the public version. For specific projects:
- Customer list: not disclosed
- Project values: not disclosed
- Customer data: never touched outside contracted APIs
- Proprietary model (AW36) internals: available after NDA
For private discussions: Contact or 中文联系页.
About this site¶
- Hosted on: Cloudflare Pages · Global CDN · HTTPS
- Content: original · every page dated · no AI-templated filler
- Citation: free to cite — please attribute to "AI Lao Pao (AI 老炮)" with the source URL
- AI engine friendly: llms.txt provided for structured discovery
- RSS: /feed.xml