AI Game Playtest Triage & Release Evidence Workspace

A studio running internal and external playtests often receives fragmented observations across spreadsheets, forms, chat exports, and recordings. The proposed workspace organizes those inputs around builds and test scenarios, helping QA leads investigate recurring problems without losing the original evidence or confusing similar symptoms with a single confirmed root cause.

Publishers and development partners could use the same review process to prepare milestone discussions, organize regression checks, and document unresolved risks. Useful scenarios include progression blockers, controller-specific interface failures, localization inconsistencies, and accessibility observations. The product supports evidence management and investigation coordination; it does not substitute for hands-on gameplay testing or specialist accessibility assessment.

Each investigation would retain a distinction between the tester's observation, extracted metadata, model interpretation, and reviewer decision. Suggested duplicate clusters would remain editable, and reproduction candidates would identify which actions were recorded versus inferred. Missing build identifiers or contradictory reports would trigger clarification workflows rather than being silently resolved by a model.

Release summaries would bring together confirmed issues, unresolved investigations, retest outcomes, and known coverage gaps for a selected build. Every material conclusion should point back to supporting records. An absence of new complaints would not count as verification, and an AI-generated summary would require human approval before distribution as a release review artifact.

Potential integration work includes Unity or Unreal Engine log exports, studio test forms, approved object storage, and issue trackers such as Jira or GitHub Issues. These are proposed options subject to API availability, account permissions, licensing, and implementation. Discovery would determine whether file import, a server connector, or a custom editor extension is appropriate.

Hosted language or vision providers could be evaluated for report summarization and selected attachment analysis, with local models considered where confidentiality requires them. No provider connection is represented as already working. Each approved connector would need field mapping, authentication, error handling, permission tests, and safeguards against exporting evidence beyond the intended project boundary.

The application can be custom-developed for hosted SaaS operation or a client-server installation managed within the client's environment. SaaS scope would define tenant isolation, operational ownership, and storage regions. Client-server scope would define browser access, internal service endpoints, network restrictions, and responsibility for infrastructure updates, backups, monitoring, and incident response.

Deployment planning would account for gameplay file sizes, model availability, expected test-cycle peaks, and disaster recovery requirements. A fully internal processing configuration depends on suitable local models and hardware, not simply an internal application server. Pilot load testing would guide worker sizing, upload limits, retention settings, and any separation between interactive review and background analysis.

Project adaptation would begin with an approved vocabulary of mechanics, locations, controls, issue categories, and platform identifiers. Versioned test plans and known-issue records would supply retrieval context tied to the appropriate build. The system would preserve document provenance and prevent a newer design specification from being treated as authoritative for an older test session.

Reviewer edits would feed a curated evaluation set rather than automatically becoming trusted training data. Prompt changes, retrieval adjustments, and optional fine-tuning would be tested against known examples before adoption. Evaluation would examine incorrect merges, missing evidence, invented steps, and terminology errors, with model changes requiring explicit approval and a practical rollback path.

Unreleased game material may expose intellectual property, tester identities, account details, or private conversations captured during recording. Discovery would establish permitted evidence types and redaction rules before processing. Proposed controls include project-scoped authorization, restricted attachment access, encrypted transport, configurable retention, and external model routing that respects the client's approved data-handling boundaries.

Uploaded reports and retrieved documents would be treated as untrusted content, including text that attempts to direct the model or trigger an export. Model suggestions would not receive authority to change permissions, delete evidence, or publish findings. Audit records would capture sensitive actions, while malware screening and file validation would reduce upload-related operational risks.

The engagement would deliver full source code for the custom application, agreed connector code, database migrations, configuration examples, and deployment instructions. Handover materials would also cover reviewer workflows, administration, backup procedures, and model configuration. Third-party dependencies and model licenses would be documented separately so application ownership is not confused with ownership of external services.

Acceptance would use a representative pilot dataset and criteria agreed with studio stakeholders. Checks could cover evidence attribution, cluster quality, permission boundaries, export correctness, and processing behavior under expected loads. Any unsupported evidence format or incomplete connector would be recorded as a limitation, with subsequent enhancement and maintenance responsibilities defined in the project scope.