Multi-provider data integration
Ingestion and harmonisation of campaign, audience and measurement data from several providers into one analytical model.
Mentis · Case studies · Media & advertising
AI & analytics platform for advertising effectiveness
A multi-source analytics and AI platform that scores campaigns, runs pre-tests, integrates eye-tracking data and recommends optimisations — later extended with an LLM/RAG engine that explains its recommendations from historical campaign knowledge.
PythonPredictive modelsLLM + RAGCloud APIs / microservices
In short
- Client
- Advertising-effectiveness analytics provider — media & marketing research (name withheld)
- Sector
- Media & advertising
- Timeline
- 2022 – present
- Status
- Ongoing multi-year engagement since 2022; modules evolve with the programme.
- Runs on
- Cloud-based APIs and microservices
The challenge
Campaign data arrived from several providers in different formats. Analysing effectiveness across markets and campaigns meant manual consolidation, inconsistent comparisons and slow decision support before and after launch.
What we built
Mentis designed and built the platform end to end as cloud-based APIs and microservices:
Campaign scoring & pre-testing
Scoring models and pre-launch tests that estimate expected effectiveness, including eye-tracking data.
Predictive analytics & recommendations
Predictive models and a recommendation layer for pre- and post-launch optimisation.
LLM/RAG recommendation engine
A later extension: contextual, explainable recommendations generated from historical campaign knowledge through retrieval-augmented generation.
Results
- Less effort to analyse campaigns across disparate sources
- Consistent comparison of advertising performance across markets
- Better decision support before and after launch
Qualitative outcomes as established with the client — no percentage has been measured, so none is claimed.
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