What is Tilores Identity RAG
Discover Tilores IdentityRAG - an AI-driven platform that unifies scattered customer data across CRM, support, and financial systems to enhance LLM responses. Offers real-time search, fuzzy matching, and seamless LangChain integration.
Frequently Asked Questions about Tilores Identity RAG
What is Tilores Identity RAG?▾
It is a Retrieval-Augmented Generation (RAG) solution focused on identity-related knowledge, combining vectorized document retrieval with generative models to answer queries about identity, access, and related documents.
What common use cases does it address?▾
Typical uses include answering questions from identity documentation, automating onboarding FAQs, supporting access request workflows, and providing contextual help for identity management teams.
How does the RAG workflow generally work?▾
Relevant documents are indexed into a vector store, a retrieval step finds context for a user query, and a generative model composes an answer informed by that retrieved context to improve accuracy and grounding.
What types of data sources can be connected?▾
Similar systems commonly support structured databases, cloud storage (files), document stores, and vector databases, plus the ability to ingest PDFs, logs, and knowledge base articles.
How do I integrate it with my existing identity platform or app?▾
Integration is usually done via APIs or SDKs that accept queries and return answers, and by synchronizing indexed content from your identity platform into the RAG ingestion pipeline.
What security and privacy controls should I expect or implement?▾
Expect to enforce access control, encryption at rest and in transit, audit logging, and role-based permissions; additionally, configure data filtering and retention policies to limit exposure of sensitive identity data.
How are personally identifiable information (PII) and deletion handled?▾
Best practice is to redact or tokenize PII during ingestion, maintain deletion workflows to remove indexed items on request, and document retention policies to meet compliance needs.
Which language models are compatible?▾
RAG systems typically work with any generative model accessible via API or self-hosted endpoints, including mainstream cloud-hosted LLMs and on-premise models that accept prompt-based input.
What should I expect about performance and scalability?▾
Latency depends on vector search speed and model inference time; scalable deployments use optimized vector stores, caching, and model autoscaling to handle higher query volumes.
How do I get started and where can I find support?▾
Begin by indexing a small representative dataset, testing queries to tune retrieval and prompts, and consult the project's documentation and support channels for onboarding guides and troubleshooting steps.
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