AI agents search. LuaPR monitors.
Claude or ChatGPT can generate a useful news table from a prompt. LuaPR is built for continuous media monitoring: configured sources, deterministic keyword matching, deduplication, alert history, team notifications and client-ready reports.
An AI task is a one-off report. LuaPR is infrastructure.
Claude Tasks, ChatGPT Tasks or a Make workflow can run a scheduled prompt and return a news table every morning. That is useful for ad hoc review. It is not the same as a monitoring system designed to operate continuously over known sources.
The difference is not whether AI can do it. It is what happens when a source fails, when a keyword generates too much noise, when a client asks why they were not alerted, or when you need to prove coverage from the last six months. That requires infrastructure, not a prompt.
Use Claude or ChatGPT to analyze coverage. Use LuaPR to make sure that coverage is detected, stored, alerted and reported.
Direct comparison
What separates a generic AI agent from a professional monitoring system.
| Capability | AI Agent (Claude / ChatGPT Tasks) | LuaPR |
|---|---|---|
| Monitored sources | Web search at execution time; depends on what the search engine indexes | Configured RSS feeds and sources; direct and persistent access |
| Deduplication | Not guaranteed: the same article may appear across multiple runs | Automatic deduplication by normalized URL |
| Keyword filtering | The prompt decides the criteria; can vary between runs | Explicit positive and negative keyword rules; auditable result |
| History and traceability | Conversational output or table; no structured log of what was reviewed | Article history, sent alerts, feed errors and monitoring run logs |
| Alerts and recipients | Task notification to the user who set it up | Email to multiple recipients configurable per project |
| Client reporting | Generated table; requires manual editing before sending to client | Executive PDF automatically generated with all impacts for the period |
| Operational cost | AI reads before filtering: more tokens, higher cost per processed article | Deterministic filtering first, AI only when there is a relevant signal |
| Maintenance | Prompts, sessions, platform limits, connectors that break | No technical maintenance for the user; managed infrastructure |
What an AI agent does not guarantee
An AI agent can cover a one-off case. Where it starts to fail is in real operation: multiple projects, multiple markets, continuous coverage.
- Variable results between runs: The criteria depend on the prompt. If the prompt changes or the web search varies, the result changes. Not auditable.
- No guaranteed access to sector media: Generic agents do not reliably access RSS feeds or outlets that block external crawlers.
- No verified real alerts: An AI-generated table may include inferred mentions, not articles with a real URL and confirmed date.
What LuaPR does that the agent does not
- Persistent 24/7 monitoring of the specific feeds and sources you configure.
- Every alert has a real URL. If LuaPR notifies you, the article exists, is linked and is verifiable. No inferences.
- Searchable history (30 to 365 days) to audit past coverage and answer client questions.
- Executive PDF reports automatically formatted for management or clients.
- Multiple recipients without depending on sessions, chats or platform limits.
AI is our engine, not our guard.
At LuaPR we use advanced AI to read, contextualize and summarize the story once detected. Continuous monitoring is handled by our deterministic infrastructure: fixed sources, explicit rules, deduplication, article states and full traceability.
No hallucinated alerts
If you receive an alert, it is because the article exists and the link is real and functional. AI summarizes; it does not invent the mention.
Zero technical maintenance
No rewriting prompts or fixing broken Zapier or Make integrations every week.
Predictable cost
We filter first, use AI after. Processing cost is included in your flat fee from €29. No surprises.
Stop wasting time on agents that fail when it matters most.
Use professional monitoring infrastructure powered by AI where it adds value.