Manufacturing
Full custom proposals in half a day
Centered on the Tianshu proposal-design AI employee, it chains requirement understanding, BOM generation, cost estimation and scheduling into one automated line — and banks master-craftsman know-how into company assets.
Deals are lost not because you can’t build it — but because the quote takes too long
Custom orders often go to whoever answers first, during the days your team spends "waiting on the proposal".
Proposals built by hand
Sales, engineering and procurement wrangle for 3–5 days to piece together one proposal.
Quotes measured in days
Customers compare three vendors; the slowest quote is out.
BOMs assembled by hand
Specs and material lists keyed in manually — error-prone, hard to check, full of rework.
Know-how walks out the door
The veteran’s methods live in their head; when they leave, methods and clients leave too.
For this industry, here's how the pieces fit
No product laundry list — only the capabilities this industry actually needs.
Tianshu · Proposal-Design AI Employee
Reads custom requirements and generates specs, BOM, cost estimates and schedules in one click.
- Understand → retrieve → BOM → cost → schedule
- Engineer review flow unchanged
- Per-role ROI, easy to justify
Tianshu · Knowledge Base + Skill Library
Structures drawings, parameters and past proposals — sharper with every use.
- Tiered, classified, auto-corrected
- 500+ Agents / Skills banked
- New proposals linked to past ones
STAR · Business System Ops & Monitoring
Keeps the systems design and production depend on up and running.
- Second-level anomaly alerts
- Fast root-cause localization
- Orders and production never silently down
Tianshu is the core for manufacturing; STAR keeps the underlying systems stable.
Real numbers from manufacturing customers
From a deployed customer case (custom manufacturing), anonymized.
Proposal design cycle
Custom-order scenarioFirst-pass approval
Engineer-review basisQuotes & cost estimates
Customer waiting timeProjects per engineer
Engineering capacityData from a deployed manufacturing customer case (anonymized); actual metrics per acceptance testing.
Validate small, then scale at your pace
Pick one product line
Map the custom workflow and knowledge assets; pick the POC scope.
Deliverable: Scenario list + ROI modelProposal POC
Run proposal generation and review on real requirements for one line.
Deliverable: A quantified validation reportRoll out product-line-wide
Extend to all lines; connect ERP and scheduling systems.
Deliverable: A faster proposal line end to endKeep banking assets
Every proposal feeds the knowledge base — sharper over time.
Deliverable: A company proposal asset libraryCommon questions, straight answers
How does it connect to our existing systems?
Via the Tianshu MCP service bus with credential-to-ticket exchange — legacy and third-party systems (OA, ERP, CRM and more) can be called securely. No rip-and-replace.
Is our customer and business data safe?
Four Locks protect it: network isolation (dedicated VPC + private line), a permission gateway (violations blocked on the spot), sensitive-data masking, and sandbox + token throttling. Every step is fully audited, data never leaves your boundary, and the final call stays human.
Can we start small?
Yes — the standard path is a 2-week POC: pick one real business scenario, run it on real data, and get a quantified validation report. Scale if it clears the bar, stop if it doesn't. No big up-front commitment.
Start with a 2-week POC,
turn fear into hard numbers
A 30-minute 1-on-1 solution demo to see how WuCiYuan brings AI employees safely into your workflows. Every step is verifiable, pausable, and reversible.
