Introducing 
Express — Go from prompt to pipeline in minutes.
A pre-built, production-ready Agentic RAG framework with a conversational AI interface, inline citations back to the original records, and full agent reasoning transparency. Try it now at genai.nexla.com. Compatible with NVIDIA NIMs for GPU acceleration.
In response to a user question, an AI agent dynamically decides which nexsets to query, what search terms to use, and how to combine results, searching, reasoning, and responding with full transparency at every step.
Every claim links back to the specific source records, including nexset, document, page numbers, and relevance score, so users can verify any answer with one click.
A ready-to-use chat experience at genai.nexla.com with real-time streaming responses, multi-turn conversations, and a Canvas panel for source and reasoning drill-down.
Service-key authentication plus per-nexset Access Rules, Access Scope, and Filter schemas enforce strict user-level access controls at retrieval time.
Route across OpenAI, Anthropic, Google, Azure, and Mistral with tunable temperature and custom embedding models. Compare quality, latency, and cost in real time.
Composable, API-first design (api-genai.nexla.io) with Python extensibility lets you adopt the latest models, rerankers, and retrieval techniques without lock-in.
AI-driven exploration of connectors that runs passively in the background, continuously surfacing tables, endpoints, and files that could become valuable Nexsets or MCP tools, evaluating business relevance without creating any pipelines. Complements Agentic RAG: Probe discovers data proactively; RAG retrieves it reactively in response to a question.
Probes run continuously in the background, re-scanning connectors as schemas, endpoints, and files evolve, so newly relevant data is always surfaced as a Nexset or MCP-tool candidate without manual rediscovery.
A web-based chat experience at genai.nexla.com with real-time streaming, suggested prompt chips, message actions such as copy, regenerate, and feedback, plus keyboard shortcuts. No code required to query your data.
Every answer includes inline citation badges that link to the exact source, with nexset name, document ID, page numbers, and a color-coded relevance score, so users can verify any claim.
A live Agent Timeline shows Thinking, Researching, and Generating phases, including tool call cards with search queries and result counts. A Canvas panel surfaces the full reasoning trace, sources, and raw tool I/O.
Session context persists across turns so users can refine, follow up, and correct course (for example, “be more specific about Q2”) without restating context. History is preserved across sessions.
Per-nexset filter schemas with three layers (Access Rules, Access Scope, and Filters) map user context to metadata filters at query time. Supports 12 operators including EQ, IN, BETWEEN, EXISTS, and CONTAINS.
A precision-to-creativity temperature slider (0 to 1) lets you choose between deterministic factual lookups, balanced answers, and exploratory generation. Configurable per session.
Native support for OpenAI, Anthropic, Google, Azure, and Mistral with auto-ranked model selection and optional custom embedding configuration (for example, text-embedding-3-small at 1536 dimensions).
Programmatic access at api-genai.nexla.io/v2/agentic-rag with streaming, citations, multi-turn conversations, cache management, and JWT or service-key authentication. Embed Agentic RAG directly into any app or workflow.
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