Choose by task
State whether accuracy, expression, speed, or cost matters; compare candidates on real output.
XUNYE Technology · MODEL ORCHESTRATION
Model Orchestration
Fit the AI division of labor to the work in front of you.
Material handling, analysis, creation, and checking ask different things of models. XUNYE designs model combinations around tasks, cost, and data conditions — with the team keeping the division of labor and adoption standards explicit.
How it runs
Split the work first, then assign models.
Model division is designed with the business steps: understanding material, phrasing text, executing tools, and reviewing each carry their own requirements — then candidate setups are compared on the same task conditions. A team-configured division can start simple and adjust.
01
Task requirements
Quality · latency · cost · data
02
Role setup
Draft · execute · independent review
03
Models & tools
Each step per the agreed configuration
04
Adopt & look back
Record differences · human decides
One task basis · different roles · the owner adopts the result
In practice
Model choice is designed with the working process.
Selection, collaboration, and record-keeping move together.
Collaborate by role
Let roles draft, execute, or review — with defined inputs, outputs, and handoffs, so multiple models do not just generate twice.
Keep the record
Log model choice, output, and adoption for key tasks so differences can be reviewed and configuration adjusted.
Re-verify on change
When models or flows change, check that key tasks still meet the bar.
A sample handoff
One collaboration, a clear handoff record.
What the roles exchange, and what the owner decides.
- Task
- Prepare two positioning directions for a product description
- Drafting role
- Same approved material → alternative drafts
- Review role
- Checks specs, facts, banned phrasing
- Handoff
- Material, drafts, issue locations, edit reasons
- Decision
- Owner picks a direction and approves the final
- Compared on
- Usability, rework, waiting time, total call cost
Verification & boundaries
Another layer of collaboration must show a checkable gain.
01
Same-condition output comparison
02
Facts and expression checked
03
Retries and total cost logged
04
Key tasks re-checked after changes
Independent checks need shared ground
The reviewer sees original material and the acceptance bar. Another model rating fluency proves nothing about facts.
Division is not auto-routing
Today's division is team-configured. Automatic selection, failover, or budget control would be separate work, verified in the flow.
Models change under the same name
Same-name models and APIs still upgrade. Key tasks keep inputs and expected behavior; re-validate after configuration changes.
FAQ
Mechanisms, conditions, and boundaries.
01If several models agree, can we just use the output?
Agreement shows similarity, not correctness. Models may share the same wrong material or repeat one inference. Facts still go back to sources; trade-offs still get an owner's decision.
02How do we compare models beyond price per call?
Same task, same material, same acceptance bar — compare final usable output, counting retries, human edits, waiting time, and total task cost. A cheap call may not reduce total effort.
03Must every step use AI?
No. Fixed computation, field validation, and format conversion belong to deterministic code. Models are for reading, drafting, and comparing — keep uncertainty out of stable processes.
04Can we plug in models the company already buys?
We first verify usage rights, interfaces, data-handling terms, and deployment environment, then define what can be connected. Model procurement and orchestration are agreed separately.
Make AI a force that moves your business forward.
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