GraphTalker
The AI Agent for your Knowledge Graph
Natural Language Access to Your Knowledge Graphs
GraphTalker enables business and technical users to ask natural-language questions of their Knowledge Graphs and receive verified answers as well as data analysis. It removes the complexity of SPARQL and replaces one-shot query generation with an iterative, agent-driven approach that explores schema, tests queries, learns from errors, and refines results until it produces a working answer.
GraphTalker combines multiple enterprise-ready capabilities into a single intelligent interface for working with knowledge graphs and semantic data. It can explore a repository, understand the schema, and suggest meaningful questions; generate and validate queries iteratively for greater accuracy; analyze results across multiple queries to produce higher-level reports; and collaborate with users by explaining its reasoning and clarifying ambiguity. It also preserves context across sessions, captures lessons learned to improve future use, and exposes its capabilities programmatically through an API and evaluation server, making it more than a chat interface—it is a scalable platform for building knowledge-driven AI agents.Why GraphTalker is key to your Knowledge Graph centric application
Eliminate schema archaeology
A major barrier to enterprise AI is the time spent figuring out where data lives, how it is structured, and whether it aligns across systems. GraphTalker automates much of this discovery process, reducing the engineering effort required to navigate complex, siloed environments.
Deliver a natural-language queries and analytics
GraphTalker lets users query across data sources in plain English, without switching tools or depending on specialists to write queries. The ontology becomes the enterprise interface, while the knowledge graph provides the structure and grounding.
Turn questions into governed pipelines
GraphTalker can transform answered questions into reusable, schedulable, and governed data workflows. That means insights discovered conversationally can become durable enterprise processes.
How it works
GraphTalker does not simply translate a prompt into a query and hope for the best. It uses an AI agent to iteratively explore the semantic layer by examining schema, reviewing example queries, testing hypotheses, interpreting failures, and improving its approach step by step. This closed-loop process makes it far more effective on real-world enterprise data than traditional natural-language-to-query approaches.
The platform is schema-aware by design. It uses SHACL-based shape descriptions and navigable schema overviews so the agent can understand classes, properties, and relationships before generating queries. A library of proven query patterns gives the system a foundation to adapt from rather than forcing it to invent from scratch.
GraphTalker also supports stateful, multi-turn conversations, allowing users to refine questions, pursue follow-ups, and maintain context across longer analytical sessions.Neuro-Symbolic AI in Production Form
GraphTalker is the practical embodiment of the Neuro-Symbolic vision Franz has championed for many years. Pure language models are fluent but ungrounded — they hallucinate facts, invent schema, and produce queries that look right and return nothing. Pure symbolic systems are precise but brittle, demanding hand-written queries from people who understand the data model. GraphTalker fuses the two.
The neural side — a large language model — contributes natural-language understanding, hypothesis generation, and the ability to interpret error messages and refine its approach. The symbolic side — RDF, OWL, SHACL, SPARQL, and the AllegroGraph engine — contributes formal schema, deterministic execution, and verifiable retrieval against ground-truth data. The agent loop binds them: the model proposes, the graph validates, the system iterates until the answer is correct.
Every answer GraphTalker produces is grounded in a real query against real data. The user can inspect the SPARQL, the path through the graph, and the results. There is no opaque generation step, no statistical approximation of an answer. This is what Neuro-Symbolic AI looks like when it is engineered for the enterprise.Who Should Use GraphTalker
A knowledge graph is only as valuable as the people who can use it. GraphTalker was designed to broaden that audience without diluting the rigor of the underlying graph.
Business analysts and domain experts ask questions in the language of the business and receive answers backed by traceable queries. They no longer wait on a SPARQL specialist for ad-hoc analysis, and they can save successful questions to a shared library for the next person to reuse.
Data architects and knowledge engineers see how their ontology is actually used. Every question, every successful query, and every failed attempt becomes feedback on schema design. The query library becomes institutional memory — a record of which patterns work and which gaps remain.
AI engineers and application developers embed GraphTalker into larger systems through its Python client and HTTP evaluation server. Stateful sessions, conversation save and restore, and programmatic access make GraphTalker a building block for higher-order agents, dashboards, and workflows — not just a chat window.
Data and analytics leaders (CDO, CIO, CTO) see the return on their knowledge-graph investment. The graph stops being a specialist asset and becomes the conversational interface to enterprise data. Adoption is measurable, governance is preserved, and ad-hoc questions become reusable, scheduled pipelines.
Governance, compliance, and audit teams get full transparency. Every query GraphTalker generates is visible, every tool call is logged, and every answer can be traced to the underlying triples. There is no black box between the question and the data.How GraphTalker Is Different
The market is crowded with tools that promise natural-language access to data. GraphTalker is built on a different premise.
It is not a one-shot translator. Most natural-language-to-query tools generate a single query from a prompt and present whatever comes back. GraphTalker iterates — it tests, observes, recovers from errors, and refines. The closed loop is the product.
It is graph-native, not text-native. Retrieval-augmented chatbots search documents and stitch together passages. GraphTalker operates on a structured knowledge graph with formal schema, multi-hop relationships, aggregation, and transitive reasoning. The expressive power of SPARQL is preserved, not flattened into prose.
It works above the catalog, not in place of it. Modern data catalogs do excellent work cataloging assets, lineage, and access. GraphTalker complements them by sitting above the catalog as the conversational and execution layer. Catalogs describe the data; GraphTalker queries it.
It learns and gets better. Every successful query is stored with its natural-language description. The next time a similar question is asked — by anyone in the organization — GraphTalker finds the proven pattern first. The system improves with use rather than drifting.
It is grounded with an Enterprise Semantic Graph Database. AllegroGraph provides the scale, transactional guarantees, security model, and reasoning engine of an enterprise-class graph database. GraphTalker is not a research prototype wired to a sample dataset; it is a production agent on production infrastructure.
Use as an Independent Agent A documented HTTP evaluation server and Python client mean GraphTalker can be embedded, automated, and orchestrated. It is not a closed chat product — it is a platform component.Proven across domains
GraphTalker has been validated across enterprise HR analytics, product catalogs, biomedical knowledge graphs, and geospatial data. Without retraining or domain-specific tuning, it adapts by exploring schema and data at query time, giving organizations a practical way to scale natural-language access across multiple domains.Overview Video to learn more about GraphTalker.