Leyli
From enterprise data to explainable AI—one reusable knowledge infrastructure.
Leyli connects enterprise data, domain ontologies, knowledge graphs, reasoning workflows, GraphRAG, and AI agents into one explainable knowledge architecture.
Leyli transforms data into structured knowledge, ontology-grounded concepts, explainable graphs, reasoning-ready context, GraphRAG packages, and API-ready outputs for trustworthy AI systems.
Leyli can ingest knowledge from documents, files, APIs, databases, emails, cloud storage, and domain-specific enterprise systems.
PDFs, Word files, Google Docs, Gmail, databases, Snowflake, APIs, clinical reports, manuals, policies, research papersInput includes raw documents, records, and enterprise source metadata. The stage produces normalized source objects so later pipeline steps can preserve provenance.
Leyli prepares raw sources for knowledge extraction through parsing, cleaning, normalization, chunking, metadata handling, and pipeline orchestration.
Document parsing, text preprocessing, metadata extraction, chunking, source selection, pipeline trackingInput is source content and metadata. Output is clean, chunked, traceable text that can be analyzed consistently across documents and systems.
Leyli extracts entities, relations, semantic types, evidence spans, and candidate knowledge from unstructured and semi-structured content.
NER, relation extraction, entity resolution, semantic classification, evidence tracing, annotation generationInput is prepared text. Output is structured entities, relationships, spans, and evidence candidates that turn text into reusable knowledge objects.
Leyli grounds extracted concepts in domain ontologies and controlled vocabularies to reduce ambiguity and improve semantic consistency.
UMLS, SNOMED CT, FMA, MeSH, legal taxonomies, financial taxonomies, product catalogs, internal enterprise vocabulariesInput is extracted terminology. Output is normalized concept grounding, semantic context, and hierarchy information that help AI systems avoid ambiguous meanings.
Leyli transforms extracted and grounded knowledge into inspectable graph structures that expose relationships, communities, paths, and context.
Nodes, edges, communities, neighborhoods, relation paths, graph exports, entity detail viewsInput is resolved entities, relations, and ontology context. Output is an inspectable graph that makes connections visible instead of leaving knowledge as isolated chunks.
Leyli prepares knowledge for reasoning by combining graph structure, ontology context, rules, constraints, and source evidence.
Multi-hop reasoning, rule-based checks, ontology reasoning, risk pathways, contradiction detection, knowledge validationInput will be evidence-linked graphs and domain constraints. Output is planned reasoning support for validation, multi-hop discovery, and explainable decision context.
Leyli packages graph-based knowledge into grounded retrieval contexts that LLMs can use to answer questions with evidence and citations.
Evidence chunks, relation paths, entity context, ontology context, grounded prompts, suggested questions, JSON context packagesInput is source-traceable graph knowledge. Output is grounded context that LLMs and assistants can use with citations, related concepts, and relation paths.
Leyli exposes structured knowledge through API-ready outputs that AI agents and enterprise applications can inspect, query, and act upon.
Entity APIs, relation APIs, graph APIs, GraphRAG context APIs, export APIs, agent-ready knowledge bundlesInput is structured knowledge and delivery packages. Output is API-ready data that agents can inspect, retrieve, and pass into downstream workflows.
Leyli's knowledge infrastructure can power AI products across healthcare, legal, finance, education, manufacturing, retail, government, and enterprise operations.
Medical training, surgical education, compliance review, enterprise search, research intelligence, decision support, AI assistantsInput is reusable knowledge infrastructure. Output is domain-specific applications that can show evidence, reuse context, and adapt to expert workflows.
Leyli runs a modular workflow that turns source material into structured knowledge, ontology context, evidence, graphs, GraphRAG packages, and API-ready outputs for AI systems.
Leyli connects to documents, files, APIs, databases, cloud sources, emails, and domain-specific data repositories.
Leyli identifies entities, relations, semantic types, evidence spans, and candidate knowledge from unstructured and semi-structured content.
Leyli links extracted concepts to domain ontologies and controlled vocabularies to reduce ambiguity and improve semantic consistency.
Leyli transforms extracted and grounded knowledge into inspectable graph structures with nodes, edges, communities, neighborhoods, and relation paths.
Leyli packages graph-based knowledge into grounded retrieval context that LLMs and AI agents can use with citations, source evidence, and ontology context.
Leyli provides export-ready and API-ready outputs for enterprise AI applications, GraphRAG assistants, agentic AI workflows, and expert review systems.
Every entity, relation, and answer can remain connected to source text and evidence chunks.
Domain vocabularies reduce ambiguity and help normalize extracted concepts.
Knowledge is represented as nodes, edges, communities, neighborhoods, and paths.
Outputs are prepared as GraphRAG context, annotations, exports, and API-ready knowledge objects.
Structured knowledge and source evidence help AI systems avoid unsupported answers.
Every output can be traced back to entities, relations, ontology context, and source text.
The same architecture can be adapted to different industries by changing ontologies, sources, and domain rules.
Leyli is designed for both human expert review and machine-to-machine AI workflows.
Most AI systems stop at retrieval.
Leyli turns retrieval into structured, explainable, reusable knowledge.
Leyli's Biomedical Demo proves the core architecture in one of the most knowledge-intensive domains: medicine. It shows how clinical and biomedical text can become ontology-grounded entities, relations, evidence chunks, graphs, GraphRAG packages, and export-ready knowledge.
These roadmap items describe planned expansion of the Leyli architecture and are labeled as future direction.
Graph-based and rule-based reasoning over ontology-grounded knowledge.
Quality checks, contradiction detection, confidence scoring, and expert review workflows.
Support for more biomedical, legal, financial, industrial, and enterprise ontologies.
Persistent knowledge context for AI agents working across documents, tools, and workflows.
More integrations with cloud storage, databases, enterprise systems, and collaboration platforms.