AI Integration for Roxhill Media

AI integration for Roxhill Media connects at three key functional layers: the journalist database, the outreach workflow engine, and the profile enrichment pipeline. The primary surfaces are the Journalist and Outlet objects, the Pitch and Media List modules, and the inbound data feeds from news sites and social platforms that update profiles. AI models can be triggered via webhook from profile update events, scheduled enrichment jobs, or directly within the search and list-building UI to add predictive intelligence to manual processes.

Implementation typically involves a middleware service that subscribes to Roxhill's webhooks for new coverage or profile changes. This service calls LLMs for interest prediction (analyzing recent articles to infer story angles), profile summarization (creating briefing-ready bios from scattered data), and personalization scoring (rating how well a journalist fits a specific pitch). Results are written back to custom fields via Roxhill's API or stored in a sidecar vector database for fast retrieval during searches. For example, an agent can watch for new articles by a tracked journalist, extract key themes, and automatically update a Predicted_Interests field in their Roxhill profile.

Rollout should start with a single, high-value workflow like automated media list suggestions. Governance is critical: all AI-generated profile suggestions should include confidence scores and source citations (e.g., 'based on 3 recent articles from The Guardian'), and a human-in-the-loop approval step should be maintained for initial campaigns. Audit logs must track which suggestions were used in pitches and their outcomes to create a feedback loop for model tuning. This phased approach allows PR teams to augment their expertise without ceding control, turning a static database into a dynamic, learning asset.

A robust integration connects to Roxhill's core data surfaces via its REST API and webhook system. The primary flow ingests real-time updates to journalist profiles, media outlet changes, and recent article publications. This data is processed through an AI pipeline that performs entity extraction (identifying new beats, locations, employers) and semantic analysis to update interest vectors. The enriched data is then written back to custom fields in Roxhill or to a separate vector database that powers a real-time recommendation engine, keeping the core platform's data model intact while adding an intelligence layer.

For outreach personalization, the system operates as an AI agent workflow. When a PR pro initiates a campaign, the agent retrieves the target journalist's updated profile, analyzes their last five articles for tone and focus, and cross-references this with the campaign's key messages. It then drafts a personalized pitch outline, suggests optimal contact times based on historical response data, and can trigger a sequenced follow-up via Roxhill's email integration. All agent actions are logged with audit trails in a separate operations database for compliance and optimization.

Rollout follows a phased approach: start with read-only profile enrichment to build trust in the data, then activate assisted pitch drafting in a copilot interface, and finally enable automated list building and prioritization. Governance is critical; all AI-generated content and recommendations should include a human-in-the-loop approval step before any external communication is sent. This architecture ensures the integration enhances Roxhill's core value—accurate UK media contacts—without disrupting existing PR workflows, turning a static database into a dynamic, predictive intelligence platform.

A production-ready integration connects to Roxhill's REST API for journalist profile data and uses a secure, isolated processing layer. We architect this as a middleware service that fetches data via authenticated API calls, processes it through governed AI models (e.g., for profile enrichment or interest prediction), and writes structured updates back to designated fields or a separate audit table. This keeps the core Roxhill database stable while enabling AI-driven insights. All data flows are logged, and prompts are version-controlled to ensure outputs align with your firm's messaging and compliance standards.

Rollout follows a phased, value-first approach:

• Phase 1 (Pilot): Connect to a single, high-value data object like the Journalist profile. Implement a single workflow, such as automated profile summarization that ingests a journalist's recent articles and social posts to generate a concise bio update, saving researchers hours per profile.
• Phase 2 (Expand): Add predictive scoring for story interest matching, analyzing a journalist's beat history against your client's narrative to surface the top 5 most relevant contacts for a pitch.
• Phase 3 (Automate): Integrate with your team's outreach platform (e.g., Outlook or a CRM) to trigger personalized pitch drafting based on the enriched Roxhill data, creating a closed-loop from research to execution.

Governance is built in from day one. Access to AI features is managed via role-based permissions within your team's existing SSO. Every AI-generated suggestion or update is tagged with a source trace and can be routed through a human-in-the-loop approval step before any external action is taken. We implement usage dashboards and cost controls to monitor API consumption. This structured approach de-risks adoption, allows for tuning based on real user feedback, and ensures the AI acts as a scalable assistant—not an autonomous agent—within your trusted PR workflows.