Agolo does one thing exceptionally well: we turn your trusted knowledge assets into a clean knowledge graph that, in turn, improves AI accuracy. Our technology vision is that more accurate knowledge graphs will drive AI application adoption, usefulness and value.
Agolo makes product support AI better. Enterprise use cases range from GenAI Apps to LLMs and RAG Pipelines to Business Intelligence Dashboards and Reports to Support Site Augmentation to Enterprise and Application Search.
Taken together, the business impact is better self-service, improved technical support quality, and lower support, repair and warranty costs.
Agolo's engine extracts, links, aggregates, and analyzes entities within any text-based knowledge asset. Agolo uses contextual and semantic understanding of those entities and their relationships to create a Knowledge Graph data service that, in turn, powers many use cases across an organization.
Automatically identify and resolve any standard or custom product-related entities within any unstructured data source.
The Agolo confidence score is highly tunable and designed to look across multiple attributes for a complete approach to entity disambiguation.
Entities occur with extensive information that Agolo extracts and associates with the entities. Agolo creates a product support-centric Knowledge Graph that provides unparalleled insights into products, issues, and solutions.
Agolo enriches your Knowledge Graph as new information is processed and discovered. Query the graph directly and use the trusted data via API within other tools and workflows, including Generative AI Applications and Chatbots, LLMs and RAG Pipelines, Knowledge Bases, Business Intelligence Dashboards and Reports, Enterprise or Application Search, Customer Support Platforms, and more.
Multi-Tenancy: Application supports multiple, isolated environments and full tenant management functionality.
Human-in-the-Loop Support: Although the system can operate autonomously, it also supports any degree of human oversight to ensure data integrity.
Configuration Settings Allow for Use Case Specific Tuning: Confidence thresholds can be set and later adjusted by system administrators to ensure that for each use case, the data integrity meets SLAs.
Componentized Architecture Provides Integration Flexibility: Many organizations have invested in effective NLP technologies, e.g. entity extraction, knowledge graphs, etc. Agolo’s Entity Analytics componentized architecture is designed with high flexibility to leverage existing NLP technologies when present.
Leverage Existing Structured Data: Ontologies, taxonomies, and other structured entity data from traditional databases and knowledge graphs can be leveraged to seed or enrich the knowledge base.
Enterprise-grade Containerized APIs: Available on-prem, private/public cloud, or via Agolo-hosted API.
Extensible Interface: The included user interface is included with every deployment as white-label, extensible source code that can either be customized per use case or used as a starter application until the APIs can be integrated into the customer’s environment.
Proven at Scale: Proven at Scale: Agolo is proven at enterprise scale. Our solution has been designed, optimized and tested to perform at scale. The platform has been proven with some of the largest unstructured datasets.
