AI Product Review Workspace
How intelligent suggestions fit inside an existing SaaS product without turning every feature into a chatbot.
- Reference architecture
- Concept
- Application evidence
- Not implemented
- Data
- Synthetic dataset proposed
- Test boundary
- Plan only · tests not run
This is an unbuilt reference concept. The architecture and verification structures below are proposals, with no running application, test results or client deployment claimed.
Keep the suggestion
beside the decision.
The proposed reviewer sits inside a product’s work item. It preserves the source, the suggestion and the user’s decision as different records. The product’s typed domain layer validates writes; a recommendation is not write authority.
Proposed state contract · no running application
- ProposedSource-linked suggestion
- Edited / rejectedReason + versioned history
- ApprovedAuthorized field snapshot
- Committed → reconciledDomain validation + destination evidence
Inference unavailable? Manual entry must remain usable.
A product contract
has to survive interruption.
Proposed acceptance plan. No implementation or executed tests exist. A synthetic comparison with a generic chat panel could test where the decision belongs; it would not establish a productivity percentage.
- Boundary
Change the tenant in the URL.
The server must refuse another tenant’s records and actions. UI filtering is insufficient.
- Recovery
Disconnect during a correction.
Restore the draft and its version on return, with a clear error state and a safe way to resume.
- Traceability
Reject an incorrect suggestion.
Keep the rejected suggestion in history while keeping it out of the committed output.
- Release
Change the model or the product.
Run separate versioned acceptance tests. Include timeouts, manual fallback, structured errors and user overrides.
The interface has to earn its complexity.
An inline reviewer is most justified when decisions repeat, carry value and depend on product state. An occasional exploratory question may need only a static summary. That choice should precede the inference gateway, evaluation harness and workflow build.
Imagine a B2B software platform in which users spend time interpreting customer documents, reviewing line items and correcting mismatches. A standalone chatbot can explain the data, but it pulls the user away from the actual workflow. An intelligent product feature should live at the decision point, preserve structured state and make uncertainty manageable.
This reference application demonstrates an AI-powered reviewer embedded in a simulated SaaS product. Its purpose is to show product judgment, software architecture and evaluation, not a generic language model interface.
The product experience
A user opens a work item and sees a structured panel with data extracted from an input artifact. Suggestions appear next to the relevant field, not in a separate chat transcript. Each suggestion carries a source, a reason or constraint, an edit action and a confidence category only if supported by real evaluation.
The user can accept, edit, defer, or reject a suggestion. Corrections are visible in a versioned state history. When a user leaves and returns, the work item remains intact. A second team member with an appropriate role can review the change and complete it. The UI includes a first-use explanation of what AI does and what it does not do.
Technical considerations
Multi-tenancy: Every item, document, retrieval operation and action is scoped to a tenant. Server-side enforcement, not only UI filtering, protects boundaries.
Product API: AI recommendations are suggestions, not authoritative writes. Validation happens in the product's typed domain layer.
Inference gateway: The product uses a replaceable provider adapter, cost controls, timeouts, retries and model-version pinning.
Workflow states: Proposed → edited → approved → committed → reconciled, with clear error states and resumption.
Evaluation: Offline labeled examples, runtime quality samples, structured-error checks, harmful-output tests and instrumentation of user overrides.
Usability: Keyboard operation, visible source and suggestion differentiation, and a correction path that does not require the user to phrase a prompt.
Demonstration scenarios
Use a synthetic line-item data set with ordinary inputs, ambiguous product codes, conflicting documents, malformed inputs, an intentionally wrong suggestion and an API timeout. Compare the same task using a generic chat panel and a structured inline review interface. The point is not to prove a numeric productivity benefit; it is to articulate where the user decision should happen.
What must be demonstrated
- Changing the tenant ID in a URL must not expose another tenant's records.
- An unreviewed model suggestion must never update the system of record.
- The interface must handle network interruption and restore the draft state.
- A rejected suggestion remains traceable without contaminating the final output.
- When model inference is unavailable, the product remains usable with manual entry.
- Model and product releases have separate versioned acceptance tests.
When this is the wrong solution
A static AI-generated summary may suffice for occasional exploratory questions. A complex inline reviewer adds engineering cost and is most justified when the decision is repeated, high-value, integrated with existing state, and directly affects a product's usefulness.