Recommendation Systems
Recommendation system development
The right item in front of the right person, measured, not guessed.
Personalization and recommender engines built against a business metric - conversion, retention, engagement - not just a similarity score, with the cold-start and feedback-loop problems handled explicitly rather than discovered in production.
What this covers
- Collaborative and content-based recommenders matched to your catalog
- Cold-start handling for new users and new items
- A/B-tested against a defined business metric, not offline accuracy alone
- Feedback-loop and popularity-bias monitoring after launch
How we engage
- Define the metric the recommender is meant to move
- Build and offline-evaluate against historical interaction data
- A/B test in production and iterate on the losing variant
More in AI & Data Intelligence
Most engagements draw on more than one capability, built as a single stack rather than separate practices.
- AI & Data ScienceFrom a question nobody can answer to a model in production answering it.
- Machine Learning EngineeringModel development that survives contact with real data.
- Computer VisionTeaching a camera to catch what a person would miss on the tenth pass.
- Natural Language ProcessingStructure out of the text nobody has time to read.
- Generative AILLM systems that answer from your data, not the model's guess.
- Agentic AIAutonomous agents for the decisions that are routine, not the ones that aren't.
- Data Wrangling & Feature EngineeringThe unglamorous 80% of a model's performance nobody budgets for.
- Data Engineering & Systems IntegrationTrustworthy data, and systems that were never designed to talk actually talking.
- MLOps & AI Platform EngineeringThe unglamorous layer that decides whether your models survive contact with production.
Common questions about Recommendation Systems
Can't find what you're looking for? Ask us
Do we need a data platform in place before we can do AI?
Not a finished one, but you need the feeds the model depends on to be reliable. Data engineering and MLOps are capabilities inside this pillar precisely because most stalled AI programmes are actually stalled data programmes - we build the two together rather than waiting on one.
Ready to talk through your next move?
Book a 30-minute strategy session with a ECLACTRA™ lead - no sales deck, just a straight conversation about where AI, geospatial, engineering, or fractional leadership could actually move the needle.