Turn your documents, tickets, and product manuals into a conversational knowledge system: retrieval-augmented generation, access control, and answer sourcing — accurate enough to put in front of your customers.
Hand repetitive business processes (review, routing, report generation) to agents: tool calling, human-in-the-loop points, and failure fallbacks — from demo to production with confidence.
Domain data governance → fine-tuning → private deployment and inference optimization, suited for scenarios with data-security requirements.
Ground-up design of backend, data, and AI system architecture. 15 years of architecture experience + 8 years of an ops perspective — what I design accounts for the 3 a.m. alert three years out, not the boxes on a slide.
Independent review of your team's existing plan: single points of failure, scalability traps, cost structure, and AI feasibility — with an actionable improvement list.
Rationale for choosing frameworks, cloud services, middleware, and model vendors — avoiding tech decisions made "to look good on a résumé".
From manual deploys to automated pipelines: Git workflow, build & release, K8s containerization, and rollback mechanisms.
Logging, metrics, and tracing — turning incident diagnosis from "guessing through logs" into "reading a dashboard".
Resource profiling, capacity planning, and architecture optimization. I once built the maintenance system from scratch and significantly reduced the annual ops cost of comparable systems (around RMB 0.4M).
The initial call is free — let's confirm AI is the right solution before discussing the plan.