What the buyer should understand.
A Core packages the repeatable AI setup so teams launch the same governed workflow every time. For core reorder, the important buyer proof is simple: Open the core reorder path in the Satinash client, perform the normal user action for the Cores workflow, and verify the visible state, evidence, limits, or artifact output that confirms the capability completed its job. A strong demo narrates the user action, then pauses on the visible state before moving on: the active scope, the eligible sources or tools, the status message, the artifact output, the limit state, and the next action that a normal user can take. The evaluator leaves knowing that this is a supporting capability that makes the larger workflow feel reliable, understandable, and complete, how it is governed, and which adjacent features to test next.
the Core catalog, Core detail pages, prompt editor, model selectors, retrieval controls, starter prompts, tool picker, and launch actions into chat or widgets
Set the Core instructions, default model lane, retrieval strategy, datasets, and tools. In the core reorder documentation, this step includes the user-visible confirmation, the expected state change, and the reason the step matters to the buyer's evaluation checklist.
Primary proof surface: Open the core reorder path in the Satinash client, perform the normal user action for the Cores workflow, and verify the visible state, evidence, limits, or artifact output that confirms the capability completed its job. The evaluator sees the user action and the confirmation in the same flow, then identifies the exact state, table row, message, preview, control, citation, diagnostic, or output that proves core reorder worked.
What Core reorder solves
Core reorder solves the client-side problem described by its product summary: users prioritize frequently used Cores. The feature is documented as a workflow a buyer can run in Satinash, with a visible beginning, a visible state change, and an inspection surface that confirms the work happened.
The strongest use case is not generic AI productivity. It is the specific cores moment where workspace owners, enablement leads, support managers, and subject-matter experts who package repeatable assistants for other people need to decide which assistant should handle a recurring job, which sources and tools the assistant may use, whether an assistant should run direct chat or a planned workflow, and whether a Core is ready to clone, publish, pause, or attach to a widget. The page keeps that decision in view so the reader understands the job, the product surface, and the business reason for the capability.
Where it appears in the client
Core reorder appears around the Core catalog, Core detail pages, prompt editor, model selectors, retrieval controls, starter prompts, tool picker, and launch actions into chat or widgets. Those locations give the buyer a concrete route through the product instead of a feature claim that only exists in a slide deck.
The relevant client objects are system instructions, default model lane, dataset scope, retrieval strategy, starter prompts, MCP tools, and activation state. When the feature is evaluated, each object either provides scope, proves readiness, explains a limit, or shows the next action available to the user.
Proof surfaces and pitfalls
The primary proof surface is the session summary after launching a chat from the Core; the secondary proof surface is locked filters, activation state, and disabled resource messages. Together they show the action, the state, and the evidence path a buyer can inspect during or after the demo.
The main pitfall is allowing all datasets or all tools when a narrow assistant scope would produce clearer answers. A second pitfall is ignoring disabled dataset or over-limit states in the Core setup screen. The documentation names both because long-form feature pages need to explain how a buyer can misread the workflow and how the client UI resolves that confusion.
What the user gets.
What it solves: Core reorder addresses a concrete client-side problem in Satinash: users prioritize frequently used Cores. It keeps the discussion anchored in a workflow a buyer can actually run, not a broad AI claim. The documentation explains the moment of need, the risk of doing the work manually, and the reason this capability belongs in the product rather than in a training note or sales promise.
Where it appears: Core reorder lives around the Core catalog, Core detail pages, prompt editor, model selectors, retrieval controls, starter prompts, tool picker, and launch actions into chat or widgets. The relevant user is usually workspace owners, enablement leads, support managers, and subject-matter experts who package repeatable assistants for other people. During evaluation, the buyer can point to the control, table, drawer, route, preview, or status label that makes the capability visible, then follow it into the next Satinash surface without asking for hidden context.
User outcome: Saved assistants that combine instructions, model choices, dataset scope, live tools, and interaction defaults. For core reorder, that outcome is strongest when the user can start from a real task, see the scope and state, complete the action, and understand what changed. The before-and-after is clear enough that a stakeholder can retell the workflow after the demo.
Operational context: Launch paths from the Core catalog into chat, widgets, or repeatable team workflows. The feature works with system instructions, default model lane, dataset scope, retrieval strategy, starter prompts, MCP tools, and activation state. Those objects matter because they tell buyers what must already exist, what can be configured by a workspace user, and what needs inspection when the result looks different from expectation.
Decision support: Core reorder helps teams decide which assistant should handle a recurring job, which sources and tools the assistant may use, whether an assistant should run direct chat or a planned workflow, and whether a Core is ready to clone, publish, pause, or attach to a widget. The documentation states those decisions directly so the page works as an evaluation aid, a sales leave-behind, and a product reference for people who were not in the live demo.
Related features: compare Core reorder with Custom AI Cores, Locked Core filters, Starter prompts, and Starter prompt click behavior. Those nearby pages give the evaluator the rest of the workflow: the source setup, the control surface, the evidence trail, and the operational follow-through. Linking the pages this way keeps the 100-feature catalog from feeling like isolated fragments.
Scope boundary: Core pages describe reusable assistant design in product terms, with model and retrieval details tied to visible configuration rather than internal orchestration. For core reorder, that boundary is important because the marketing content describes visible client behavior and buyer evidence while staying out of operator-only setup details unless they explain what the user can inspect.
Workflow documentation.
- Choose or clone a Core that matches the job to be done. Start the walkthrough by naming Core reorder, the user role, and the current client location. Show the buyer exactly where the workflow begins, what object is selected, and which visible state tells the user the page is ready for action.
- Set the Core instructions, default model lane, retrieval strategy, datasets, and tools. In the core reorder documentation, this step includes the user-visible confirmation, the expected state change, and the reason the step matters to the buyer's evaluation checklist.
- Add starter prompts, execution mode, speech behavior, and safe defaults for the audience. In the core reorder documentation, this step includes the user-visible confirmation, the expected state change, and the reason the step matters to the buyer's evaluation checklist.
- Publish the Core to team chat, launch a conversation, or attach it to a widget. In the core reorder documentation, this step includes the user-visible confirmation, the expected state change, and the reason the step matters to the buyer's evaluation checklist.
- Review usage, feedback, disabled states, and source/tool boundaries over time. In the core reorder documentation, this step includes the user-visible confirmation, the expected state change, and the reason the step matters to the buyer's evaluation checklist.
- Check configuration before judging the result. For Core reorder, configuration includes system instructions, default model lane, dataset scope, and retrieval strategy, plus the category-level controls listed in the page. A useful evaluation names which settings were chosen, which were inherited from a Core, plan, connector, dataset, or workspace, and which settings are intentionally not part of this feature.
- Inspect proof before moving to the next page. The best proof surface for this pass is the session summary after launching a chat from the Core. If that surface is absent, the demo stops and explains why, because buyer confidence depends on seeing the evidence trail rather than hearing that it exists somewhere else.
- Close the workflow by comparing the result with Custom AI Cores, Locked Core filters, Starter prompts, and Starter prompt click behavior. That comparison helps the evaluator understand whether core reorder is the entry point, the supporting control, the repair path, or the trust signal inside the larger cores story.
Proof, configuration, and buyer concerns.
Proof to inspect
- Primary proof surface: Open the core reorder path in the Satinash client, perform the normal user action for the Cores workflow, and verify the visible state, evidence, limits, or artifact output that confirms the capability completed its job. The evaluator sees the user action and the confirmation in the same flow, then identifies the exact state, table row, message, preview, control, citation, diagnostic, or output that proves core reorder worked.
- Category proof: Open the Core detail and confirm which datasets, MCP tools, model lanes, and execution path are active. Tie this proof to Core reorder by naming the source object, status, or control that changed. A buyer does not have to infer whether the feature is active; the surface makes the active state legible.
- Evidence trail: the session summary after launching a chat from the Core. This is the surface to pause on during a demo because it shows how Satinash keeps the workflow inspectable after the initial click, message, upload, scan, connection, plan check, or widget preview.
- Secondary evidence: locked filters, activation state, and disabled resource messages. This gives reviewers a second way to validate the same claim, which is useful when the buyer cares about support handoff, source governance, billing transparency, reliability, or daily user adoption.
- Evaluation checklist: Change dataset or tool scope and verify the available chat controls and proof surfaces follow that scope. For core reorder, record the expected result, the state that changed, and the related feature that would be tested next. That turns the page into a reusable checklist rather than a prose-only description.
- Table-friendly facts: Core reorder; slug core-reorder; category Cores; fit support; route /features/core-reorder/; works with prompts, models, datasets, retrieval policy, tools, starters, and permissions; primary users workspace owners, enablement leads, support managers, and subject-matter experts who package repeatable assistants for other people; related features Custom AI Cores, Locked Core filters, Starter prompts, and Starter prompt click behavior. These facts are intentionally compact so comparison tables and sales notes can reuse them without rewriting the page.
- Buyer proof question: if a skeptical reviewer asks where core reorder appears, what it depends on, and how to know it worked, the answer points to the Core catalog, Core detail pages, prompt editor, model selectors, retrieval controls, starter prompts, tool picker, and launch actions into chat or widgets, system instructions, default model lane, dataset scope, retrieval strategy, starter prompts, MCP tools, and activation state, and the visible proof surfaces above.
Configuration notes
- Configuration model: Core reorder appears in the Cores client experience through visible controls, status labels, evidence panels, and adjacent workflows that evaluators can inspect without relying on behind-the-scenes implementation details. In practical terms, Core reorder is shaped by System prompt, prompt generator, starter prompts, and click behavior., plus the category objects system instructions, default model lane, dataset scope, retrieval strategy, starter prompts, MCP tools, and activation state. User-facing choices are separated from inherited workspace, Core, connector, dataset, or plan state so evaluators know what can be changed during normal use.
- Setup checklist: Dataset scope, disabled dataset visibility, MCP tool picker, and tool-choice mode. Before a demo, confirm the prerequisites are present and visible. If the feature depends on a Core, dataset, connector, widget, plan, upload, or role, the docs identify how that dependency appears to the user and what message appears when it is missing or inactive.
- Limits, plan context, and table facts: Retrieval strategy, recency bias, rerank lane, temperature, top-p, voice, and model capabilities. The buyer does not need internal limit enforcement details, but they do need to know which capacity, model, connector, upload, document, widget, or team boundary can affect core reorder. Table-ready configuration facts: Route family: Core list, Core detail, Core edit, and chat launch surfaces, Primary evidence: saved instructions, selected model lane, source scope, tool scope, and session summary, Main dependencies: model catalog, datasets, MCP integrations, roles, and plan gates, and Buyer signal: teams can standardize AI behavior without retraining every user on every prompt.
- Pitfall to avoid: allowing all datasets or all tools when a narrow assistant scope would produce clearer answers. Second pitfall to avoid: ignoring disabled dataset or over-limit states in the Core setup screen. The evaluation record captures chosen configuration, visible state before and after the action, proof surface inspected, and related feature tested next so stakeholders can compare the feature across accounts without relying on memory.
Buyer concerns
Where does core reorder show up for an end user? It appears around the Core catalog, Core detail pages, prompt editor, model selectors, retrieval controls, starter prompts, tool picker, and launch actions into chat or widgets. The answer points to the route, panel, table, drawer, composer control, preview, status chip, or action row that makes the capability visible in the product.
Can teams standardize AI workflows without retraining every user? For Core reorder, the answer is visible in the active scope, the category-specific source objects, and the first proof surface. The buyer understands whether the feature uses approved knowledge, selected tools, a Core setting, a connector state, a plan allowance, or a public widget boundary.
Can an owner prove what a Core is allowed to see or call? That concern becomes a concrete evaluation check: Clone a proven Core and confirm the buyer can see what changed before using it. The buyer needs a visible pass or fail condition, not a vague assurance that the product can handle it.
Can a Core be copied, adjusted, paused, and audited as needs change? If the concern appears during a live demo, pause on the pitfall called out above, then show the status or configuration that resolves it. That pattern teaches evaluators how to self-serve the next time they see the same behavior.
How does a buyer compare this with related features? Start with Custom AI Cores, Locked Core filters, Starter prompts, and Starter prompt click behavior. If Core reorder is the control, the related pages usually show the source setup, the output, the repair path, or the trust evidence that surrounds it.
What gets documented after evaluation? Capture the user role, the exact workflow, the dependency objects, the configuration choices, the proof surfaces inspected, the pitfalls observed, and the next related feature to validate. That makes core reorder useful as long-form documentation rather than a short marketing blurb.
Evaluation tables.
These tables turn the documentation into something a buyer, sales engineer, or implementation lead can inspect during a live walkthrough.
Evaluation checklist
| Check | What to inspect | Why it matters |
|---|---|---|
| Start with a real task | Change dataset or tool scope and verify the available chat controls and proof surfaces follow that scope. The task uses a realistic customer question and the same source, tool, plan, role, or widget context the buyer expects in production. | This proves Core reorder in the context where it will actually be used, rather than as an isolated demo click. |
| Confirm visible scope | Inspect the Core catalog, Core detail pages, prompt editor, model selectors, retrieval controls, starter prompts, tool picker, and launch actions into chat or widgets and identify the active objects: system instructions, default model lane, dataset scope, retrieval strategy, starter prompts, MCP tools, and activation state. | The buyer can see what is eligible, what is excluded, and which setting explains the result. |
| Inspect proof | Pause on the session summary after launching a chat from the Core and locked filters, activation state, and disabled resource messages; record the state before and after the user action. | The feature is accepted on product evidence, not on a verbal promise. |
| Compare adjacent features | Continue into Custom AI Cores, Locked Core filters, Starter prompts, and Starter prompt click behavior after the first pass. | The buyer sees how Core reorder fits into the rest of the cores workflow and which capability answers the next concern. |
Proof matrix
| Evidence | Product proof | Buyer value |
|---|---|---|
| Visible proof | Open the core reorder path in the Satinash client, perform the normal user action for the Cores workflow, and verify the visible state, evidence, limits, or artifact output that confirms the capability completed its job. | Shows the exact client evidence a buyer can inspect during the feature walkthrough. |
| Category proof | Open the Core detail and confirm which datasets, MCP tools, model lanes, and execution path are active. | Connects Core reorder to the broader Cores evaluation story. |
| Failure or limit proof | Pitfall to avoid: allowing all datasets or all tools when a narrow assistant scope would produce clearer answers. | Makes confusing states understandable before they become objections. |
| Related proof | Related features: Custom AI Cores, Locked Core filters, Starter prompts, and Starter prompt click behavior. | Gives the evaluator a next page when they need source setup, output review, repair, or governance evidence. |
Configuration matrix
| Area | Control or dependency | Impact |
|---|---|---|
| Primary configuration | System prompt, prompt generator, starter prompts, and click behavior. | Explains the main control or inherited setting that shapes core reorder. |
| Prerequisites | Required or relevant objects: system instructions, default model lane, dataset scope, retrieval strategy, starter prompts, MCP tools, and activation state. | Keeps the demo honest about what must exist before the feature can prove value. |
| Limits | Retrieval strategy, recency bias, rerank lane, temperature, top-p, voice, and model capabilities. | Connects blocked, unavailable, or over-limit behavior to visible product guidance. |
| Table facts | Route family: Core list, Core detail, Core edit, and chat launch surfaces, Primary evidence: saved instructions, selected model lane, source scope, tool scope, and session summary, Main dependencies: model catalog, datasets, MCP integrations, roles, and plan gates, and Buyer signal: teams can standardize AI behavior without retraining every user on every prompt | Provides compact comparison data for sales notes, buyer checklists, and category pages. |
Workflow map.
Best practices
- Change dataset or tool scope and verify the available chat controls and proof surfaces follow that scope.
- Clone a proven Core and confirm the buyer can see what changed before using it.
- Record the route /features/core-reorder/, proof surfaces, configuration state, and related features Custom AI Cores, Locked Core filters, Starter prompts, and Starter prompt click behavior.
- Use the feature with the user audience daily operators who notice polish, continuity, and small controls during repeated work so the evaluation reflects the intended rollout path.
Limits to discuss
- allowing all datasets or all tools when a narrow assistant scope would produce clearer answers
- ignoring disabled dataset or over-limit states in the Core setup screen
- supporting documentation keeps the feature proportionate, then proves the small interaction clearly enough that buyers see operational maturity
- Core pages describe reusable assistant design in product terms, with model and retrieval details tied to visible configuration rather than internal orchestration.
Terms buyers will hear.
| Term | Definition | Use in evaluation |
|---|---|---|
| Feature route | /features/core-reorder/ | Canonical URL for the buyer-facing documentation page. |
| Feature fit | support: a supporting capability that makes the larger workflow feel reliable, understandable, and complete. | Explains whether the feature is a flagship, focused, supporting, or trust-oriented page. |
| Primary users | workspace owners, enablement leads, support managers, and subject-matter experts who package repeatable assistants for other people | Clarifies who must understand and validate the workflow. |
| Works with | prompts, models, datasets, retrieval policy, tools, starters, and permissions | Lists the adjacent product areas that shape the feature in use. |