Graph Recommendation Engine Developers & Solutions

Graph Database Recommendation Engine Developers

We can help you to successfully implement graph database recommendation engines for your website or application. Intech Solutions are recommendation engine developers for companies and governments throughout Australia and New Zealand.

Graph database recommendation engines

Graph databases enable a recommendation engine to model and examine interactions across individuals, objects, and behaviours. Instead of simply depending on prior interactions or similarity scores, recommendation engines that use graph databases investigate multi-hop relationships. In complex data environments like e-commerce, streaming platforms, and social networks, graph databases enable more contextual, accurate, and real-time recommendations.

Graph database recommendation engine solutions & development

At Intech Solutions, we empower governments, enterprises, and forward-thinking organisations with cutting-edge graph database recommendation engines. Our expert team builds advanced graph-based systems that model real-world relationships to deliver highly accurate, context-aware recommendations. Whether enhancing citizen services, optimising product discovery, or improving user engagement, our tailored solutions drive actionable insights and measurable results. By combining industry-leading platforms with open-source innovation, we deliver scalable, high-performance recommendation engines that lead today’s digital transformation.

Intech Solutions serves as the sole distributor of TigerGraph across Australia and New Zealand. TigerGraph is a graph database platform utilised by major corporations and data-centric organisations requiring the analysis of intricate, interrelated data.

Graph recommendation engine implementation

A Gartner survey found over half of consumers unsubscribe due to poor personalisation, while 74% of marketing leaders struggle to scale personalisation —highlighting a major gap between consumer expectations and marketing capabilities.

Learn how Kickdynamic deliver highly personalised and impactful email marketing campaigns using graph based recommendation engines.

Kickdynamic

of Amazon revenue comes from cross-sells and upsells

spend per visit who clicks a recommendation

estimated size of the global recommendation market in 2024

Generic recommendations miss the mark

Most recommendation engines rely exclusively on simple metrics like item popularity or past purchases. This often results in impersonal suggestions that don’t truly reflect user intent or context.

Traditional recommendation engines are falling behind

Traditional recommendation engines often struggle to deliver relevant results because they rely on isolated data points, such as past purchases or ratings, without understanding the deeper context behind user behaviour. These systems typically operate on rigid, table-based structures that make it difficult to capture indirect relationships or emerging trends. As user expectations grow and data becomes more interconnected, these limitations lead to generic, repetitive suggestions that frustrate customers and limit business growth.

How do graph database recommendation engines help improve revenue?

Deep linking with TigerGraph: Powering smarter recommendations

TigerGraph’s architecture is designed for real-time deep link analytics, the ability to navigate multiple levels of relationships within large, complex data sets in milliseconds. This is a game-changer for recommendation engines, where understanding not just direct connections but also indirect and multi-hop relationships leads to far more intelligent, personalised suggestions. Deep linking allows businesses to model and query behaviour patterns, preferences, and affinities across many dimensions, something traditional systems can not do efficiently at scale.

Capture real-time business moments with graph-powered recommendations

TigerGraph’s graph-based recommendation engine delivers highly personalised results at the most crucial moments. By analysing real-time behaviour and deep data connections, it enables businesses to act on fleeting opportunities, turning engagement into conversions.

A major eCommerce provider leverages TigerGraph to serve personalised offers across a 300 million user base and diverse product catalogue. The result? A smarter shopping experience that drives initial purchases, boosts upsells, and increases customer lifetime value.

Smarter recommendations powered by machine learning and graphs

By combining graph analytics with AI, TigerGraph’s recommendation engine goes beyond basic personalisation. It understands not just what users are doing, but why, tracking intent and behaviour in real time to guide smarter suggestions.

Instead of offering similar big-ticket items post-purchase, it recommends relevant accessories or services. It also reads browsing signals to detect where users are in the buying journey, whether they’re just exploring or ready to buy, and tailors recommendations to gently push them towards conversion.

Why graph databases are your best solution to for recommendation engines

Recommendation engines thrive on understanding complex, evolving user behavior, graph databases are uniquely built for this task. They allow businesses to model relationships between users, products, and interactions with unmatched flexibility and speed.

Real-Time, Deep Relationship Analysis: The engine can quickly navigate multiple connections within your data to uncover complex user-item relationships, enabling highly personalised and contextually relevant recommendations in real time.
Scalable Performance for Large Data Sets: Built to handle massive volumes of interconnected data, the system maintains fast query speeds and reliable performance as your user base and product catalogue grow.
Seamless Integration with Machine Learning The platform supports in-database machine learning workflows, allowing you to build, deploy, and continuously update recommendation models efficiently without moving data around.

Talk to our recommendation engine specialists about your project.

Speak to our recommendation engine specialists / consultants about how your organisation can leverage Intech’s ultra-fast graph database solutions

Trusted by leading Australian companies and government

Our recommendation engine experts can help you successfully implement your project

Graph database solutions are popular in a variety of high-impact use cases. Leverage our recommendation engine experts to successfully implement your project.

About Intech Solutions

Intech Solutions prides itself on its knowledgeable and approachable staff. We have over 20 years experience delivering high performance solutions, on time and on budget.