What the buyer should understand.
Satinash answers from approved workspace knowledge, shows source passages, and keeps the activity trail attached to the conversation. For dataset-grounded answers, the important buyer proof is simple: Open the dataset-grounded answers path in the Satinash client, perform the normal user action for the AI Chat 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 flagship proof point shown early in a buyer demo because it anchors the broader Satinash story, how it is governed, and which adjacent features to test next.
the chat route, composer, conversation sidebar, message actions, session summary bar, source drawers, upload previews, and live activity panels
Confirm the active source scope, attached files, web-search setting, and tool availability. In the dataset-grounded answers 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 dataset-grounded answers path in the Satinash client, perform the normal user action for the AI Chat 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 dataset-grounded answers worked.
What Dataset-grounded answers solves
Dataset-grounded answers solves the client-side problem described by its product summary: responses can use approved private knowledge instead of relying on generic model memory. 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 ai chat moment where analysts, support agents, operators, founders, and team members who ask follow-up questions against approved workspace knowledge need to decide whether a user should trust a grounded answer, whether the conversation needs narrower source scope, whether a file or tool output should become part of the next turn, and whether to export, regenerate, speak, or continue the thread. 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
Dataset-grounded answers appears around the chat route, composer, conversation sidebar, message actions, session summary bar, source drawers, upload previews, and live activity panels. 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 selected Core, active datasets, attached files, message history, web-search setting, model capability lane, and allowed MCP tools. 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 activity or reasoning surfaces that explain retrieval, tool calls, and response assembly; the secondary proof surface is the session summary showing active Core, datasets, tools, uploads, and model lane. Together they show the action, the state, and the evidence path a buyer can inspect during or after the demo.
The main pitfall is mixing public web context with private knowledge without making the scope visible. A second pitfall is treating the chat as a generic chatbot instead of checking active Core and dataset scope. 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: Dataset-grounded answers addresses a concrete client-side problem in Satinash: responses can use approved private knowledge instead of relying on generic model memory. 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: Dataset-grounded answers lives around the chat route, composer, conversation sidebar, message actions, session summary bar, source drawers, upload previews, and live activity panels. The relevant user is usually analysts, support agents, operators, founders, and team members who ask follow-up questions against approved workspace knowledge. 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: A persistent workspace thread that keeps questions, files, selected Cores, and follow-up context together. For dataset-grounded answers, 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: Answer controls for source scope, web search, reasoning depth, uploads, exports, and regeneration. The feature works with selected Core, active datasets, attached files, message history, web-search setting, model capability lane, and allowed MCP tools. 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: Dataset-grounded answers helps teams decide whether a user should trust a grounded answer, whether the conversation needs narrower source scope, whether a file or tool output should become part of the next turn, and whether to export, regenerate, speak, or continue the thread. 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 Dataset-grounded answers with Workspace AI chat, Source citations, Citation preview dialog, and Chat activity graph. 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: AI Chat pages stay focused on the buyer-facing conversation experience; implementation details matter only when they explain visible scope, evidence, or recovery. For dataset-grounded answers, 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.
- Ask a question in the workspace chat or start from a Core built for the task. Start the walkthrough by naming Dataset-grounded answers, 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.
- Confirm the active source scope, attached files, web-search setting, and tool availability. In the dataset-grounded answers documentation, this step includes the user-visible confirmation, the expected state change, and the reason the step matters to the buyer's evaluation checklist.
- Satinash retrieves evidence, runs allowed tools when needed, and streams the answer with progress. In the dataset-grounded answers documentation, this step includes the user-visible confirmation, the expected state change, and the reason the step matters to the buyer's evaluation checklist.
- Inspect citations, previews, activity, uploads, generated artifacts, or message feedback. In the dataset-grounded answers documentation, this step includes the user-visible confirmation, the expected state change, and the reason the step matters to the buyer's evaluation checklist.
- Edit, regenerate, export, speak, or continue the conversation without losing the trail. In the dataset-grounded answers 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 Dataset-grounded answers, configuration includes selected Core, active datasets, attached files, and message history, 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 activity or reasoning surfaces that explain retrieval, tool calls, and response assembly. 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 Workspace AI chat, Source citations, Citation preview dialog, and Chat activity graph. That comparison helps the evaluator understand whether dataset-grounded answers is the entry point, the supporting control, the repair path, or the trust signal inside the larger ai chat story.
Proof, configuration, and buyer concerns.
Proof to inspect
- Primary proof surface: Open the dataset-grounded answers path in the Satinash client, perform the normal user action for the AI Chat 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 dataset-grounded answers worked.
- Category proof: Open a citation and verify the quoted passage, document, dataset, connector, and source path. Tie this proof to Dataset-grounded answers 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: activity or reasoning surfaces that explain retrieval, tool calls, and response assembly. 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: the session summary showing active Core, datasets, tools, uploads, and model lane. 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: Stop, regenerate, or edit a response and confirm the thread stays recoverable. For dataset-grounded answers, 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: Dataset-grounded answers; slug dataset-grounded-answers; category AI Chat; fit primary; route /features/dataset-grounded-answers/; works with datasets, uploads, Cores, tools, citations, and conversation history; primary users analysts, support agents, operators, founders, and team members who ask follow-up questions against approved workspace knowledge; related features Workspace AI chat, Source citations, Citation preview dialog, and Chat activity graph. 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 dataset-grounded answers appears, what it depends on, and how to know it worked, the answer points to the chat route, composer, conversation sidebar, message actions, session summary bar, source drawers, upload previews, and live activity panels, selected Core, active datasets, attached files, message history, web-search setting, model capability lane, and allowed MCP tools, and the visible proof surfaces above.
Configuration notes
- Configuration model: Dataset-grounded answers appears in the AI Chat 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, Dataset-grounded answers is shaped by Default Core, model lane, reasoning effort, and retrieval strategy., plus the category objects selected Core, active datasets, attached files, message history, web-search setting, model capability lane, and allowed MCP tools. 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: Per-chat dataset filters, tool filters, uploads, web search, and context size. 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: Composer behavior, output rendering, export options, speech, voting, and recovery state. 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 dataset-grounded answers. Table-ready configuration facts: Route family: /chat and /chat/:conversationId, Primary evidence: citations, previews, activity, uploads, and message state, Main dependencies: Core defaults, datasets, model capabilities, and plan limits, and Buyer signal: users can inspect answer provenance without leaving the conversation.
- Pitfall to avoid: mixing public web context with private knowledge without making the scope visible. Second pitfall to avoid: treating the chat as a generic chatbot instead of checking active Core and dataset scope. 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 dataset-grounded answers show up for an end user? It appears around the chat route, composer, conversation sidebar, message actions, session summary bar, source drawers, upload previews, and live activity panels. 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.
How do users know an answer came from approved company material? For Dataset-grounded answers, 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 a team inspect why the model answered the way it did? That concern becomes a concrete evaluation check: Ask the same question with broad and narrow dataset filters and confirm the source set changes visibly. The buyer needs a visible pass or fail condition, not a vague assurance that the product can handle it.
Does long-running AI work remain understandable and recoverable? 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 Workspace AI chat, Source citations, Citation preview dialog, and Chat activity graph. If Dataset-grounded answers 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 dataset-grounded answers 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 | Stop, regenerate, or edit a response and confirm the thread stays recoverable. The task uses a realistic customer question and the same source, tool, plan, role, or widget context the buyer expects in production. | This proves Dataset-grounded answers in the context where it will actually be used, rather than as an isolated demo click. |
| Confirm visible scope | Inspect the chat route, composer, conversation sidebar, message actions, session summary bar, source drawers, upload previews, and live activity panels and identify the active objects: selected Core, active datasets, attached files, message history, web-search setting, model capability lane, and allowed MCP tools. | The buyer can see what is eligible, what is excluded, and which setting explains the result. |
| Inspect proof | Pause on activity or reasoning surfaces that explain retrieval, tool calls, and response assembly and the session summary showing active Core, datasets, tools, uploads, and model lane; 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 Workspace AI chat, Source citations, Citation preview dialog, and Chat activity graph after the first pass. | The buyer sees how Dataset-grounded answers fits into the rest of the ai chat workflow and which capability answers the next concern. |
Proof matrix
| Evidence | Product proof | Buyer value |
|---|---|---|
| Visible proof | Open the dataset-grounded answers path in the Satinash client, perform the normal user action for the AI Chat 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 a citation and verify the quoted passage, document, dataset, connector, and source path. | Connects Dataset-grounded answers to the broader AI Chat evaluation story. |
| Failure or limit proof | Pitfall to avoid: mixing public web context with private knowledge without making the scope visible. | Makes confusing states understandable before they become objections. |
| Related proof | Related features: Workspace AI chat, Source citations, Citation preview dialog, and Chat activity graph. | 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 | Default Core, model lane, reasoning effort, and retrieval strategy. | Explains the main control or inherited setting that shapes dataset-grounded answers. |
| Prerequisites | Required or relevant objects: selected Core, active datasets, attached files, message history, web-search setting, model capability lane, and allowed MCP tools. | Keeps the demo honest about what must exist before the feature can prove value. |
| Limits | Composer behavior, output rendering, export options, speech, voting, and recovery state. | Connects blocked, unavailable, or over-limit behavior to visible product guidance. |
| Table facts | Route family: /chat and /chat/:conversationId, Primary evidence: citations, previews, activity, uploads, and message state, Main dependencies: Core defaults, datasets, model capabilities, and plan limits, and Buyer signal: users can inspect answer provenance without leaving the conversation | Provides compact comparison data for sales notes, buyer checklists, and category pages. |
Workflow map.
Best practices
- Stop, regenerate, or edit a response and confirm the thread stays recoverable.
- Ask the same question with broad and narrow dataset filters and confirm the source set changes visibly.
- Record the route /features/dataset-grounded-answers/, proof surfaces, configuration state, and related features Workspace AI chat, Source citations, Citation preview dialog, and Chat activity graph.
- Use the feature with the user audience executive evaluators, department leads, and first pilot teams so the evaluation reflects the intended rollout path.
Limits to discuss
- mixing public web context with private knowledge without making the scope visible
- treating the chat as a generic chatbot instead of checking active Core and dataset scope
- primary documentation proves the happy path, the visible limits, and the recovery behavior because the capability shapes trust in the rest of the platform
- AI Chat pages stay focused on the buyer-facing conversation experience; implementation details matter only when they explain visible scope, evidence, or recovery.
Terms buyers will hear.
| Term | Definition | Use in evaluation |
|---|---|---|
| Feature route | /features/dataset-grounded-answers/ | Canonical URL for the buyer-facing documentation page. |
| Feature fit | primary: a flagship proof point shown early in a buyer demo because it anchors the broader Satinash story. | Explains whether the feature is a flagship, focused, supporting, or trust-oriented page. |
| Primary users | analysts, support agents, operators, founders, and team members who ask follow-up questions against approved workspace knowledge | Clarifies who must understand and validate the workflow. |
| Works with | datasets, uploads, Cores, tools, citations, and conversation history | Lists the adjacent product areas that shape the feature in use. |