When relationships drive the answer — KYC chains, supplier-defect networks, account-deal intelligence — vector similarity is not enough. We design and ship GraphRAG systems on Neo4j AuraDB, Amazon Neptune, and TigerGraph that traverse 100M+ entities in under 800ms with cited, auditable answers.
We have shipped GraphRAG into financial services, pharma, legal, and manufacturing. These five failure modes show up every time the data has structure that matters.
Cosine similarity finds related chunks but cannot traverse 'customer → account → contract → supplier' chains
The same vendor exists as 11 different records across Salesforce, NetSuite, Coupa, and Jira
Questions like 'which open Jira issues touch services owned by teams reporting to VP-X?' need 4 joins across 3 systems
Generative answers cannot be audited back to source nodes — compliance, legal, and risk teams won't approve
Confluence, SharePoint, Notion, Slack, Salesforce, GitHub, Jira — each with its own object model and IDs
Cosine similarity finds related chunks but cannot traverse 'customer → account → contract → supplier' chains
Answers look plausible but skip the connective tissue that drives real decisions
Analysts re-do retrieval manually across 6-8 source systems per question
Engineering note: none of these are solved by adding more chunks to a vector store. They require explicit edges, resolved entities, and traversal.
Every component is named, replaceable, and observable. No black-box vendor lock-in.
Neo4j AuraDB for general workloads, Amazon Neptune for AWS-native deployments, TigerGraph for deep traversals on billion-edge graphs, Memgraph for streaming analytics. We pick per workload.
Two-stage resolution — LSH blocking, then a classifier combining string similarity, structural graph features, and Claude reasoning for ambiguous cases. 95% precision at 92% recall on heterogeneous data.
Change-data-capture from Salesforce, Jira, GitHub, ServiceNow, and SQL sources via Kafka or Airbyte. The graph stays fresh incrementally — no full nightly rebuilds, no stale traversals.
LangChain GraphCypherQAChain translates natural language to Cypher, executes against the graph, then feeds structured results plus hybrid Qdrant vector hits into Anthropic Claude for cited reasoning.
Every entity type, relation, and LLM output is validated against Pydantic models. Schema drift fails loudly in CI, not silently in production. Audit-grade citations on every answer.
p50/p95/p99 latency per query type, cache hit rates, Cypher slow-query traces, embedding-call cost. We tune the schema and indexes until SLOs hold under production load.
Seven stages from raw source-system event to cited natural-language answer. Each stage is independently observable and replaceable.
Kafka streams change-data-capture events from Confluence, SharePoint, Notion, Slack, Salesforce, Google Workspace, GitHub, Jira, ServiceNow.
Hybrid NER pipeline — spaCy for high-volume structured fields, Claude for long-form unstructured documents. Output validated against a Pydantic entity schema.
LSH blocking narrows candidate pairs; a classifier (string similarity + structural features + Claude reasoning) merges duplicates. Low-confidence matches route to human review.
Typed edges extracted from text and structured joins. Provenance preserved — every edge cites its source record. Edges are versioned, not overwritten.
Loaded into Neo4j AuraDB, Amazon Neptune, or TigerGraph — chosen per workload. Indexes tuned for the top-20 query patterns identified during schema design.
LangChain GraphCypherQAChain converts the user question to Cypher, executes, and fuses results with Qdrant hybrid retrieval before handing to Claude for reasoning.
Every answer carries citations back to source nodes and edges. Compliance, legal, and risk teams can audit any claim. Hallucination surface shrinks to near-zero.
Five industries where we have shipped graph systems that traverse millions of resolved entities daily.
Resolve beneficial-owner networks across Salesforce CRM, transaction systems, and external watchlists. Multi-hop traversals surface indirect exposure that flat lookups miss.
A multi-family-office SaaS used the graph to compress KYC-refresh cycles from 6 weeks to 4 days per entity.
Unify internal trial data with public ontologies (UniProt, Reactome) into one graph. Researchers ask multi-hop questions like 'which targets in our portfolio share pathways with approved oncology drugs?'
Discovery teams cut literature-triage time from 3 days to 30 minutes per hypothesis.
Graph all internal matters, opinions, and external citations. Paralegals trace precedent chains, conflicting rulings, and judge-jurisdiction patterns in a single Cypher query.
A Fortune 500 legal department cut conflict-check time from 2 hours to 4 minutes per matter.
Link BOMs, supplier contracts, defect reports, and field-failure data. When a defect spikes, the graph instantly returns affected SKUs, customers, suppliers, and warranty exposure.
An industrial OEM reduced defect-blast-radius analysis from 5 days to 90 seconds.
Merge Salesforce, Slack message history, Gong calls, and product telemetry into a single customer graph. CSMs see every touchpoint, every champion, every blocker in one view.
A vertical SaaS lifted net retention by surfacing at-risk accounts 60 days earlier than the prior heuristic.
These are production numbers from systems we have shipped — not whitepaper benchmarks.
A predictable, phased delivery. No multi-quarter discovery exercises.
Workshop the top-20 questions the graph must answer. Design entity types, relation types, and provenance model. Stand up Kafka connectors to 3-5 source systems and load the first 1-10M entities.
Build the LangChain GraphCypherQAChain pipeline, hybrid Qdrant retrieval, and Claude reasoning. Stand up the eval harness with 200+ golden questions across the priority use-cases.
Datadog observability, p95 latency tuning, cache layer, role-based access in Neo4j or Neptune, audit-log export, and cutover. Eval gates block any regression on the golden set.
Add remaining source systems via the same Kafka pattern. Layer in new entity types as new use-cases arrive. The schema and pipeline scale — new questions stop costing engineer-weeks.
The questions that actually matter when scoping a graph project.
30-minute working session with a senior engineer. We map your top-20 questions to entities, edges, and a production pipeline — and tell you honestly whether GraphRAG is the right call.
50+ projects delivered · 96% client satisfaction · Clients in US, UAE & Pakistan