AI Social Content Correction & Recall Workspace

A retailer may discover that an advertised offer has changed after regional accounts have published it. An events business may need to correct a venue or cancellation notice across multiple campaigns. The proposed workspace would connect the approved change notice to potentially affected owned posts, giving reviewers a structured starting point instead of a scattered search exercise.

Agencies and franchise teams could separate central policy decisions from local account responsibilities. A central reviewer might approve replacement language while account owners handle channel-specific action. The case record would show what was reviewed, what was changed, and what remains unresolved, without suggesting that every public copy of the original content can be recalled.

Each case would preserve the relationship between the triggering notice, candidate post, proposed correction, and reviewer decision. Semantic matching would help find paraphrased claims, while exact identifiers could locate campaign codes or product references. Reviewers would see supporting passages and coverage limitations, allowing them to reject weak matches or add posts that automated retrieval missed.

Approval would apply to a specific action and content version, not a general permission for an AI agent to modify accounts. Changes to the draft or supporting facts would trigger renewed review. The action ledger would separate submission from confirmation, and uncertain outcomes would enter reconciliation rather than being silently marked complete or retried without checks.

Outstand, Ayrshare, Buffer, and PostEverywhere are potential integration options for selected publishing workflows, subject to provider access and implementation. Discovery would verify the actual endpoints, supported accounts, authorization scopes, and response semantics needed for each use case. Publishing support alone would not be treated as proof that historical retrieval, editing, deletion, or reply access is available.

Phyllo or other authorized data services could be evaluated separately for retrieval needs, because data access and publishing are different capabilities. Optional REST or MCP interfaces would expose narrowly scoped application operations with approval checks. Where APIs cannot support a correction, the workspace would assign a manual task and request evidence rather than claim automated completion.

A hosted SaaS deployment could provide separate organizational workspaces with centrally operated application services, queues, storage, and monitoring. Tenant boundaries and agency delegation would be designed explicitly rather than inferred from account labels. Hosting location, backup policies, operational access, and incident responsibilities would be agreed during discovery according to the intended users and data sensitivity.

A client-server deployment could place the application and evidence storage in client-controlled infrastructure, accessed through a browser-based interface. Depending on the selected architecture, outbound connectivity may still be needed for social APIs or hosted models. A self-hosted model can be evaluated separately, while deployment documentation would distinguish local components from external services and their dependencies.

The knowledge layer would organize approved claims, campaign briefs, correction notices, brand terminology, and asset permissions with version dates and owners. Retrieval would prioritize current, authorized material and retain references to the passages used in draft corrections. Superseded documents would remain distinguishable so an outdated statement does not become the basis for another public revision.

Adaptation would start with retrieval rules, structured prompts, controlled vocabularies, and reviewed examples rather than assuming fine-tuning is necessary. Evaluation sets would include ambiguous product names, paraphrased claims, multilingual posts, and unrelated campaign references. Optional fine-tuning would require suitable rights and data, with human review retained because no model can establish complete coverage or factual correctness on its own.

The security design would separate users who identify issues, users who approve corrections, and services permitted to execute external actions. Credentials would be stored outside ordinary case data and scoped to required accounts where provider controls allow. Server-side checks would verify workspace access, approval state, and action eligibility immediately before any modification request is sent.

Imported posts and reference documents would be treated as untrusted inputs, including text that attempts to instruct the model or invoke tools. Retention controls would limit unnecessary personal data in evidence snapshots and exports. Audit logging, credential revocation, backup restoration, and tenant-isolation tests would form part of implementation planning without implying certification or automatic legal compliance.

DOD IT Solutions would deliver the full source code for the custom application, database migrations, deployment configuration, implemented connector code, test assets, and administrator documentation within the agreed scope. The handover would also explain knowledge preparation, model configuration, account authorization, manual fallback procedures, and operational monitoring so the client can understand and maintain the delivered system.

Acceptance would use agreed scenarios rather than a blanket promise of automated recall. Tests would cover false matches, missing permissions, changed drafts, duplicate requests, unsupported deletions, and ambiguous provider responses. The final handover would document implemented capabilities and known limits, with future connector additions or model changes treated as separately assessed development work rather than assumed functionality.