Most Neo4j projects do not fail on the database. They fail on the model. A graph built by translating tables into nodes and relationships one for one will answer the same questions your warehouse already answers, only slower, and the project quietly ends there. The model has to be designed around the traversals you actually need.
That is the part we do first. Before any loading, we work out which entities deserve to be nodes, which facts belong on relationships rather than properties, where to denormalise for read speed, and which questions will be asked often enough to justify shaping the graph around them. Everything downstream, from Cypher performance to how long an algorithm takes to run, is decided in that step.
Five areas. Most engagements use two or three, not all of them.
The property graph model itself: node labels, relationship types and direction, where properties live, and the indexes and constraints that keep it fast under production load. We model against your real queries, not a whiteboard abstraction.
Queries that stay fast as the graph grows. We profile the plans, fix the traversals that expand too early, and replace the patterns that quietly turn into cartesian products at scale.
The GDS library applied to a real question: community detection to find rings, centrality to rank what matters, similarity and node embeddings where a model needs features that a table cannot express.
Neo4j running where you already run things. Aura, self-hosted, or inside Snowflake or Databricks. Backup, monitoring and upgrade paths agreed before go-live, not after.
Your team runs it when we leave. Documented model, documented queries, and working sessions with the engineers who will maintain it.
Every engagement opens with your data and your question, never a slide deck.
Kickstart
$500
Deployment
Customscoped to your data
Embedded
Customfull time
The 60-day guarantee. If a deployed solution is not delivering in 60 days, we keep working at no charge until it does.
No, and you should not. Neo4j earns its place on the connected slice of your data, not all of it. Most engagements start with one domain and one question, with the warehouse staying exactly where it is as the system of record.
Sometimes it is not, and we will say so on the first call. Graphs win when the question is about how things connect across several hops. If your questions are aggregations over one large table, a columnar warehouse is the better answer and cheaper to run.
Aura removes the operational work and suits most teams. Self-hosted makes sense when data residency, network isolation or an existing Kubernetes platform makes it simpler. We have deployed both and the choice follows your constraints, not a preference.
Tim leads every engagement and does the modeling himself. Larger deployments add engineers, and the embedded option places a dedicated consultant inside your team.
Kickstart is a fixed $500. Deployments and embedded consulting are scoped after a call and quoted in writing before any work starts.
Or email info@eastridge-analytics.com
