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Aqeeq Technologies

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.

ShopLinx
Mush Social
Sahyadri Farms
Netviss

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.

Build 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.