AI Development
AI Recommendation System Development
We build recommendation systems that improve engagement and conversion: embeddings, ranking, feedback loops, and evaluation—integrated into your product with production-minded reliability.
Overview
What this service is
This service designs and implements a recommendation system tailored to your product: data model, candidate generation, ranking logic, and serving strategy.
We create an evaluation approach (offline metrics plus rollout strategy) so improvements are measured instead of guessed.
Delivery includes integration into your app/API, monitoring, and guidance for iterating as data grows and user behaviour changes.
Benefits
What you get
Higher engagement and retention
Show users more relevant items so they return and complete more actions.
Improved conversion
Better ranking can increase purchases, signups, and content consumption.
Measurable outcomes
Evaluation and rollout plans so you can quantify lift and avoid wasted work.
Cold-start strategies
Fallback logic for new users or new items to avoid empty recommendations.
Production integration
APIs and data pipelines built to run reliably, not just in notebooks.
Long-term iteration path
Feedback loops and monitoring so quality improves as your dataset grows.
Features
What we deliver
Data modelling + event tracking plan
Define the events and entities needed to train and evaluate recommendations reliably.
Candidate generation
Embeddings and similarity search for generating relevant candidate items at scale.
Ranking strategy
Ranking models or heuristics aligned to your business goal (CTR, purchase, retention).
Offline evaluation harness
Metrics and test sets that let you validate improvements before shipping broadly.
Serving API integration
Real-time or near-real-time recommendation endpoints integrated into your product stack.
Monitoring + drift checks
Observability hooks so you can detect quality regressions and update strategy over time.
Process
How we work
Discovery
We confirm goals, data availability, and constraints—then define the first recommendation milestone.
Data setup
We map entities/events and prepare training and evaluation datasets for experimentation.
Build
We implement candidate generation, ranking, and serving endpoints with integration into your product.
Evaluation
We validate offline metrics and prepare rollout experiments to measure lift safely.
Launch + iteration
We ship monitoring and a roadmap for improving quality as data and usage grow.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
E-commerce product recommendations
Related products, frequently bought together, and personalised ranking across categories.
Content and feed ranking
Recommend articles, videos, or posts based on user history and content similarity.
Marketplace discovery
Rank listings and suggestions based on behaviour, inventory signals, and relevance.
B2B resource recommendations
Suggest documents, templates, or workflows inside enterprise products based on usage patterns.
Personalised onboarding
Guide users to relevant actions and content in their first session to reduce drop-off.
FAQ
Frequently asked questions
Not necessarily. We can start with embeddings and heuristics, then evolve to stronger ranking as data grows and tracking improves.
We define a primary metric (CTR, conversion, retention) and implement offline evaluation plus rollout experiments where possible.
Yes. Serving strategy depends on your needs—some systems are batch-based, others are near-real-time with caching for speed.
Yes. We design fallback logic (popular items, onboarding signals) so recommendations are useful from session one.
Yes. We can integrate recommendation endpoints and pipelines with your existing data stack and APIs.
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Want measurable personalisation outcomes?
Share your data sources and the actions you want to optimise. We’ll propose a recommendation approach and rollout plan.
Evaluation + monitoring included.