Proven process
of adoption

Value understanding

Together, we assess your siloed data challenges, and evaluate if a graph-powered approach meets your requirements.

Proof of value

We demonstrate the value with a proof-of-concept (PoC) built using a representative subset of your data. This includes data modelling, engineering, and setup to ensure your analysts can fully experience the power of the platform and understand the impact of adopting a graph-powered solution.

Setting up for success

Next, we craft a detailed project plan together for implementation, including detailed information about sizing and licensing. In parallel, we set up the infrastructure and begin training.

Round 1: capability development

After preparation, production begins. This happens in iterative cycles. Each cycle is fast and adds value, progressively advancing your capabilities to the next level.

Data modelling

Together, we design the best graph data model to help you get the answers you need – for example, if a phone call should be a node or a relationship.

Data engineering

Together, we begin ingesting data into GraphAware Hume from your new and existing sources. We transform structured and unstructured data into a connected view of truth — ready for analysis.

Hume setup

We help you customise GraphAware Hume’s functionality to your needs — define access rights, deep dive into the knowledge graph, and preset the most important queries your analysts need.

Analytics

Your analysts can now work with your knowledge graph, getting results with a few clicks. During this phase of advanced intelligence analysis, we help increase adoption across your organisation, training more users.

Round 2: automated analysis

The more you connect and work with the data, the more your capability grows. Your analysts can begin automating their analysis – defining patterns and setting up alerts on key entities and connections.

Round 3: predictive power

Now, your new graph database and knowledge graph are operational, growing with each day. Analysts begin to leverage graph data science and AI for predictive analysis, supplying mission-critical recommendations.