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Read approved records, documents, and metadata through connectors or federated access.
Knowledge Graph Engineering
Enterprise Knowledge Graph Services
for Connected Data and AI
Codiste helps data and AI leaders connect records, documents, and business relationships so teams can strengthen data governance, investigate dependencies, discover relevant information, and build evidence-backed AI applications.
Disconnected Data Hides the Relationships Your Teams Need
A supplier appears under one name in procurement, another in contracts, and a third in support records. Teams reconcile identities manually before they can assess exposure or answer a customer question.
A query returns relevant files, but the answer depends on relationships across them: which agreement covers an asset, who owns it, and what changes when a supplier fails.
A passage can be relevant yet refer to the wrong company or an outdated agreement. Codiste’s knowledge graph services connect entities, relationships, and supporting sources so retrieval has business context.
What Is an Enterprise Knowledge Graph?
An enterprise knowledge graph represents business entities, their relationships, and their meaning across data sources. It connects records about customers, products, people, documents, and other concepts through shared identifiers and defined relationships, allowing applications to follow those connections and retrieve the evidence behind them.
Knowledge graph consulting starts with the questions your teams need answered. The model, database, and integration approach follow those requirements. Your warehouse, lake, and operational systems can continue serving their existing roles.
We Deliver Knowledge Graph Development Services
Start with strategy, implement a focused graph application, or extend an existing platform. Each service produces an output your business and engineering teams can review.
01
Knowledge Graph Consulting & Strategy
Best for: Deciding whether a graph will solve a specific business problem. Define priority questions, review source readiness, and compare graph-based retrieval with your existing approach. Scope the first domain and identify ownership, integration, and operating requirements. You receive: A use-case shortlist, feasibility assessment, architecture options, and delivery roadmap.
02
Ontology & Semantic Model Design
Best for: Establishing shared meaning across teams and systems. Define entity types, relationships, identifiers, and business rules with domain experts. Choose the level of formal ontology your use case needs, with validation constraints and a process for model changes. You receive: A documented semantic model, source mappings, validation rules, and model ownership.
03
Knowledge Graph Development & Implementation
Best for: Turning an agreed model into a production application. Build graph storage, loading pipelines, indexes, query interfaces, and deployment automation. Test representative traversals, access restrictions, and recovery procedures before rollout. You receive: A deployed graph, tested queries, source code, configuration, and operating guidance.
04
Entity Extraction & Relationship Mining
Best for: Connecting knowledge held inside documents. Use named entity recognition and relationship extraction to identify people, organisations, assets, and relevant links. Apply entity resolution and disambiguation, with review paths for ambiguous matches. You receive: Extraction pipelines, match rules, reviewed samples, and links back to source passages.
05
Data Integration & Graph Federation
Best for: Connecting information across systems with different data models. Map databases, APIs, warehouses, and document repositories into a common graph model. Select ingestion, data federation, or a hybrid approach based on freshness, query performance, and source permissions. You receive: Connectors, identity mappings, update processes, and defined source-of-truth rules.
06
GraphRAG & Knowledge Graph for AI
Best for: Questions that need context across entities or documents. Our GraphRAG services combine graph retrieval with text, vector search, or summaries to support multi-hop reasoning across connected evidence. Evaluate answers and supporting sources against a simpler retrieval baseline. You receive: A retrieval workflow, evaluation questions, provenance, and application integration.
07
Graph Visualization & Exploration Interfaces
Best for: Teams investigating connections without writing graph queries. Build graph visualization with search, filters, relationship expansion, and source inspection. Add a visual query builder or natural-language querying where it improves the workflow, with query validation and access controls. You receive: An exploration interface, saved views, and role-appropriate access.
08
Graph Analytics & Knowledge Discovery
Best for: Finding patterns that depend on relationships. Apply graph algorithms such as community detection and centrality analysis to defined business questions. Where appropriate, use node embeddings, link prediction, or semantic inference, keeping predicted links distinguishable from recorded facts. You receive: Repeatable analysis workflows, explained outputs, and validation criteria.
Our Knowledge Graph Technology Stack
Platform selection follows your data model, query requirements, security needs, and operating environment. These are evaluation options, not claims of partnership or certification.
RDF
RDFS
OWL
SHACL
SKOS
Protégé
Python
Java
SQL
Cypher
SPARQL
Gremlin
Neo4j
Amazon Neptune
Ontotext GraphDB
Stardog
spaCy
Apache Jena
RDFLib
Apache Kafka
Apache Airflow
Microsoft GraphRAG
Neo4j GraphRAG
LlamaIndex
NetworkX
Neo4j Graph Data Science
Cytoscape.js
Sigma.js
Neo4j Bloom
Tom Sawyer Perspectives
How We Deliver an Enterprise Knowledge Graph
Knowledge graph construction begins with one domain and a set of real questions. Expand once the graph demonstrates useful answers, reliable updates, and manageable operating costs.
1. Frame the questions
Review sources, IDs, permissions, and links for priority questions. Agree ontology and data governance rules. You provide samples, experts, and questions. You receive a model and estimate. Success: it covers the first use case.
2. Connect the sources
Ingest, extract, match entities, and load the graph. Keep source refs and route uncertain records for review. You provide access and reviewers. You receive a graph, queries, and samples. Success: links match source evidence.
3. Test the application
Test queries, exploration, or GraphRAG against expected answers. Check access, speed, updates, and recovery. You provide questions and feedback. You receive results, fixes, and criteria. Success: quality and access meet the bar.
4. Launch & maintain
Deploy the approved scope, monitor it, and train owners on changes and data governance. You provide an owner and approval. You receive code, docs, runbooks, and handover. Success: your team can trace, correct, and update records.
Scope Your Knowledge Graph First
Bring a question your teams struggle to answer across systems. We’ll help define the entities, relationships, and source data needed for a focused first build.
Scope your knowledge graphHow Our Knowledge Graph Architecture Works
The knowledge graph path
Extract and Resolve
Identify entities and relationships, match identities, and review ambiguous or conflicting evidence.
Model and Store
Represent validated relationships using the agreed schema or ontology, with source references and version information.
Query and Analyse
Use graph traversal, semantic queries, graph algorithms, or GraphRAG retrieval to assemble relevant context.
Serve Users and Applications
Deliver exploration views, APIs, and evidence-backed answers within the caller’s permissions.
Validation
Check mappings, entity matches, and relationship constraints.
Provenance
Retain the source and version supporting each recorded claim.
Access
Enforce permissions across retrieval, results, and application actions.
Freshness
Propagate source updates, deletions, and affected index changes.
Where Enterprise Knowledge Graphs Add Value
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Supplier and Dependency Analysis
Connect suppliers, components, products, and contracts so procurement and operations can trace impact before a disruption spreads. Measure dependency coverage, investigation time, and accuracy against reviewed records.
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Customer and Account Intelligence
Resolve organisations, contacts, subsidiaries, agreements, and interactions into one traceable account view. Measure entity-match quality, duplicate rate, and time spent on account research.
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Enterprise Search and GraphRAG
Link concepts and entities across technical docs, policies, and reports so multi-document questions return cited evidence. Measure answer correctness, citation support, retrieval relevance, and query cost.
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Investigation and Network Analysis
Link accounts, transactions, devices, or organisations and surface patterns for analyst review. Measure useful findings, review effort, and false-positive rate.
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Research and Knowledge Discovery
Connect publications, concepts, methods, and findings, and keep source evidence separate from predicted links. Measure relevant discoveries, source coverage, and time to assemble supporting evidence.
Knowledge Graph Applications Across Industries
The model reflects the entities and decisions in your domain. Source access, terminology, and review requirements shape implementation.
How We Validate Your Knowledge Graph
Agree success criteria before the build. A useful graph must represent the right relationships and answer the questions it was designed to support.
Build Your Enterprise Knowledge Graph With Codiste
Share your priority use case and source systems. Define the model, integrations, validation approach, and delivery scope before committing to a wider knowledge graph rollout.
Why Choose Codiste for Knowledge Graph Engineering?
Business Questions Shape the Model
The first design review connects each entity and relationship to a question your team needs answered. This keeps the initial build focused and gives reviewers concrete acceptance examples.
GraphRAG Is Tested Against Your Current Approach
Compare graph-assisted retrieval with your existing search or vector RAG on the same questions. Assess answer quality alongside latency, indexing costs, and maintenance effort before expanding.
Your Team Can Inspect and Maintain the Graph
Handover includes mappings, model definitions, extraction rules, queries, and update procedures. Named owners can review uncertain matches and manage source changes. Ongoing support is scoped separately.
Knowledge Graph Services FAQs
A knowledge graph is a representation of entities, relationships, and their meaning; a database is technology used to store and query information. A graph database can host a knowledge graph, but useful knowledge also requires modelling, identity rules, source context, and governance. A graph database alone does not supply those decisions.
RDF represents information as subject–predicate–object statements and works with standards such as SPARQL, RDFS, and OWL. Labeled property graphs attach properties to nodes and relationships and use languages such as Cypher or Gremlin. Choose based on interoperability, inference, application queries, and operating requirements; both can support knowledge graphs.
Consider a graph when traversing relationships, resolving entities, or connecting evidence across sources is central to the application. A warehouse remains useful for reporting and aggregation. The two often work together. Start with representative questions and compare the effort, performance, and maintenance of a graph approach with your existing setup.
You need agreed meanings and relationship rules; the level of formal ontology depends on the use case. Codiste works with your domain experts and data owners to define concepts, identifiers, and constraints. Begin with the vocabulary needed for the first application, then extend it through a documented review process.
Selection depends on the graph model, query patterns, inference needs, cloud environment, and team skills. Neo4j centres on property graphs; GraphDB centres on RDF. Neptune supports RDF and property-graph models with different query languages. Benchmark representative queries and consider security, integration, licensing, and operations before selecting a platform.
Parse documents, identify entities and relationships, and map them to the agreed model. Resolve duplicate or ambiguous identities, validate extracted claims, and retain links to supporting passages. Automated extraction can make mistakes, so uncertain cases need review. Refresh processes must also account for revised documents and removed content.
Scope, source readiness, model complexity, extraction quality, and application requirements determine cost and duration. A focused proof of concept tests a defined question before a wider implementation. The proposal separates engineering from platform, model, and ongoing support costs. If you need knowledge graph as a service, agree maintenance coverage and ownership explicitly.
Yes. A graph can reference or ingest selected information from existing systems while they remain authoritative for their data. Federation may allow some queries without full replication, depending on the platform and source. Define identifiers, permissions, freshness, and failure handling across those boundaries so results remain understandable and reliable.
GraphRAG uses graph structure or graph-derived context to support retrieval and answer generation. Vector RAG typically starts with similarity search over embedded content. Graph-assisted approaches can help connect entities, follow multiple relationships, or summarise linked information. They can also combine vector search. The right choice depends on your questions and measured results.
They can provide explicit relationships and traceable evidence that help an AI answer within supplied context. They do not eliminate hallucinations or correct bad source data automatically. Evaluate unsupported claims, citation accuracy, and behaviour when information is missing. Incorrect extraction or entity matching can otherwise introduce errors into the graph itself.
Yes, through controlled query interfaces and approved tools. Natural-language questions can be translated into Cypher or SPARQL, with validation, query limits, and caller permissions applied before execution. Actions need separate authorisation and business rules. Consequential changes should follow approval and audit requirements rather than relying on model-generated commands alone.
Use scheduled refreshes, change events, or supported change data capture to update affected entities and relationships. Preserve source identifiers and versions so corrections and deletions can propagate. Revalidate impacted links, derived results, and retrieval indexes. Assign owners for model changes and monitor freshness targets and unresolved update failures.
Discuss Your Knowledge Graph Requirements
Tell us which questions your teams need answered, where the information lives, and what your current approach misses. We’ll help scope the next step.