Phlox AI
Agentic search and multi-step reasoning for technical knowledge synthesis.
Alvar (formerly known as Sajari) represents the 2026 benchmark for intelligent site search, utilizing a hybrid architecture that blends traditional keyword matching with sophisticated neural vector search. Its core technical differentiator is its built-in Reinforcement Learning (RL) layer, which autonomously optimizes search rankings based on real-time user interaction data such as click-through rates and conversion events. The platform is designed for high-scale e-commerce and content-heavy enterprises that require sub-100ms latency across global indices. Alvar's 'Pipelines' feature allows developers to programmatically control the search experience using a visual or code-based logic editor, enabling complex custom ranking factors, synonym handling, and dynamic faceting. As of 2026, Alvar has positioned itself as a primary competitor to Algolia and Elastic, specifically targeting organizations that want the power of vector search without the overhead of managing specialized infrastructure. It integrates deep telemetry to provide actionable insights into 'no-result' queries and trend forecasting, making it a critical component of the modern headless commerce stack.
Uses feedback loops from user clicks and purchases to automatically re-rank search results for better relevance.
Agentic search and multi-step reasoning for technical knowledge synthesis.
The world’s first conversational AI-powered answer engine for real-time, sourced information.
Accelerate scientific discovery with high-fidelity RAG-powered literature synthesis and verifiable citation mapping.
The next-generation AI search engine that delivers direct answers and clean content without ads or tracking.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Combines BM25 keyword matching with dense vector embeddings for semantic understanding.
A workflow engine that processes queries and results through customizable steps (e.g., filtering, boosting, external data enrichment).
Automatically generates filters based on the attributes present in the current result set.
Updates the search index within seconds of a data change via API or webhook.
Allows a single query to search across multiple disparate data collections simultaneously.
Uses NLP to identify the intent and category of a search query to redirect users or boost relevant departments.
High bounce rates due to irrelevant search results on mobile devices.
Registry Updated:2/7/2026
Inaccurate keyword matching across 15 different languages.
Users unable to find specific technical answers using simple keyword search.