Practical answers on AI projects — manufacturing adoption, factory agents, data access, costs and vendor screening — plus embodied AI cases. Every figure is cited from a public source with a date.
A factory agent is a layer that reads data, forms judgements and follows up on issues on top of existing plant systems. It does not replace ERP or MES and does not change shop-floor work. This article defines it, lists three categories of work it can take over, and cites public data on how far deployments have actually gone.
The first step of AI adoption in manufacturing is not choosing a platform but picking one scenario whose value can be counted. This article sets out the three starting tasks, with a table mapping candidate scenarios to countable value, a comparison of the three data-access routes, and public data on how far deployment has gone.
Eight questions decide whether an AI deployment plan is workable: is the starting position and data access clear, is the scope bounded, who settles definitions, what exactly is delivered, are acceptance metrics measurable, how phases run, who operates it afterwards and how the engagement ends. Includes a table converting vague wording into testable terms.
A workable agent deployment plan runs in five phases: requirement analysis and scenario selection, data access and definition registration, build and go-live, trial-to-routine transition, and operations and iteration. This article breaks down each phase, its outputs and the client-side cooperation it needs, and flags the three points where projects most often stall.
An agent project quote normally has five parts: requirement analysis, data access, build and configuration, hardware, and annual operations. This article explains what each includes, how it is usually charged and where cost hides, with three public market benchmarks for comparison.
Shenzhen's public data: about RMB 220 billion in core AI industry revenue in 2025, more than 2,600 AI enterprises above a designated size, and five batches of nearly 300 "city + AI" scenarios. More choice means more packaging. This article lists five questions to ask, how to verify a vendor from public records, and three warning signals.
The first decision when adopting an enterprise AI agent is not which model to use but which use case to build first: question answering, workflow or analysis. This article compares the three, gives three criteria for choosing, covers the four tasks of corpus governance and the usual causes of failure.
Three conditions decide whether an AI employee system is worth adopting: whether the role is high-frequency and rule-based, whether data already sits in systems, and whether the cost of errors is manageable. Includes a table of roles that fit and do not, how to run the three-part cost calculation, and public adoption data.
Five screens decide which company can actually build a factory AI agent: a referenceable similar site, a clear data-access plan, who owns definitions, who operates it afterwards, and how the engagement ends. Includes how to verify from public records and three warning signals.
There are three main delivery models for AI agents: metered cloud service, on-premises deployment and source-code handover. Which fits depends on data sensitivity, in-house IT capability and long-term cost expectations rather than headline price. Includes a comparison, three selection criteria and the hidden costs of taking the source code.
The order that matters when building an AI and robotics lab in a school: decide the curriculum before the kit, the room/power/network requirements, a five-layer equipment stack, a 12-16 week delivery timeline, and the four most common mistakes.
A working equipment list for a school AI and robotics lab in three tiers (entry, standard, advanced): terminals, robot platforms, sensor kits, software platform and curriculum — how much of each, what order, and what to postpone.
The three real uses of a humanoid robot in a school (a teaching carrier, competitions and clubs, open days and admissions), plus the space, safety and maintenance requirements for running one in a classroom.
A practical checklist for sourcing robots, stage and education equipment from China: verifying the supplier (business licence, Unified Social Credit Code), samples and first-article inspection, certification and battery shipping (CE / FCC / RoHS / UN38.3), voltage and plug standards, Incoterms, after-sales and spare parts, payment risk control and the classic traps.
What an AI lab really costs in 2026, rebuilt from public tender and award notices: from a RMB 845,700 teaching platform to a RMB 2,638,500 embodied-AI training base. Budget vs. price cap vs. winning bid, plus six variables that move the quote.
A plan that survives review and passes acceptance answers four questions: what to build, who uses it, what it costs and how completion is judged. Six steps with deliverables, anchored to real 2026 tender and award amounts (RMB 845,700 / 1,726,535 / 2,638,500 and up).
An AI lab equipment list is not a pile of product names but a table where scenario, course and device line up. Three configuration tiers, with the real procurement content and award amounts published in 2026 Chinese tenders, plus the seven fields every line item needs.
Acceptance trouble usually starts in the tender document, not on site. Three ground rules for parameter drafting, a process checkpoint table, an acceptance deliverables list and ten pre-tender self-checks, referenced to real 2026 award notices.
In the first half of 2026, 218 humanoid robot contracts worth over RMB 1.723 billion were awarded in China, up 163% in count and 422% in value. Three base formats, with public award amounts of RMB 4,396,900, RMB 59.62m and RMB 77.8m.
Public prices for humanoid robot performances have fallen from RMB 20,000 a day in early 2025 to the RMB 2,500–5,000 range. Unit prices, six pricing variables, and the eight cost lines to clarify before booking.
Public daily rental sits at RMB 2,500–5,000, while ownership carries four hidden cost lines. A decision rule by annual usage days, when each option wins, and the platform route with over 3,000 schedulable robots across 100-plus cities.
A robot guide is really a vertical knowledge base plus voice, vision and positioning; the robot is only the shell. Verified capabilities from public 2026 projects, four unsolved boundaries, and three prerequisites before deployment.
A purchased robot is only the body; whether it works depends on secondary development. Twelve technical questions with common answers and what the buyer must confirm, plus a delivery path and cost and schedule estimate.
A first-year budget for enterprise AI agents and knowledge bases has three parts: compute and models, data and knowledge governance, and scenario development plus integration. Public 2026 market data for each part, plus a three-phase budget split.
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