Recommendation systems that move revenue
Personalized product, content, and next-best-action recommendations tuned for your catalog and customer behavior.
Recommendation Engines: What we deliver
We build recommendation pipelines from behavioral signals, catalog metadata, and business rules — with offline evaluation and online experimentation.
Why teams choose Aqeeq for recommendation engines
Production programs across retail, supply chain, education, and security — selected logos from recent work.
How we apply Recommendation Engines
01
Discover
Map workflows, stakeholders, data, integrations, and success metrics.
02
Design
Architecture, security model, delivery plan, and acceptance criteria.
03
Build
Production engineering with CI/CD, testing, and observability.
04
Launch
Rollout, training, documentation, and operational handoff.
05
Improve
Tune accuracy, cost, latency, and adoption after go-live.
Product strategy shipped from Figma to production
Global Figma redesign, AI within the social product, and long-term application roadmap recommendations.
Read story
Holiday booking that keeps up with seasonal demand
A customer-facing site for customized travelling services and itinerary plans — so travel teams close packages without drowning in inbox work.
Read storyBuild the future with recommendation engines
Practical capabilities engineered for production — not slide-deck promises.
Signal engineering
Clicks, orders, inventory, and context features.
Ranking models
Collaborative, content-based, and hybrid approaches.
Business rules layer
Margin, stock, and compliance constraints.
Experimentation
Holdouts and uplift measurement.
Recommendation Engines: Questions we get most often
Most engagements run from a few weeks for focused discovery or prototyping to multi-quarter delivery for enterprise programs. We scope timelines upfront against clear acceptance criteria.
Yes. We integrate with your product, engineering, and operations teams — providing architecture leadership, delivery capacity, or end-to-end ownership as needed.
Production-ready systems with documentation, monitoring, and handoff materials — plus decision-ready artifacts when the engagement is strategy or discovery focused.
Move forward with recommendation engines
Book a conversation — we will scope the engagement or tell you if another path is a better fit.