The story behind Memoriq
Most knowledge is scattered: web pages, PDFs, spreadsheets, support tickets, and internal notes each living in their own silo. Memoriq dissolves the boundaries. Point it at a list of sites or a domain, and its crawler autonomously discovers, indexes, and keeps content fresh—no manual ingestion pipeline required.
Beyond the crawl, Memoriq ingests documents of every format—PDFs, DOCX, images, slides, spreadsheets, code, and more—and unifies everything into a single vector database. Advanced retrieval-augmented generation (RAG) finds the right chunks fast, while GraphRAG goes further: a sophisticated knowledge graph links entities and concepts across sources, so answers connect ideas rather than just matching keywords.
Because Memoriq exposes a clean retrieval API and developer SDK, any AI application can plug into a rich, always-current knowledgebase and answer with grounded, source-backed evidence—without re-architecting its own stack.
Outcomes that compound
The measurable difference Memoriq makes to your operations.
One knowledge base, every source
Web, documents, and internal notes converge into a single retrievable corpus.
Answers that connect ideas
GraphRAG surfaces relationships that pure vector search would miss.
Power any AI app
A retrieval API and SDK drop rich, grounded knowledge into your stack in hours, not weeks.
From input to insight
Connect
Point Memoriq at your sites or upload your documents. The crawler and ingestion pipeline begin indexing immediately.
Compose
Memoriq embeds everything into the vector database and builds the knowledge graph, linking entities and concepts across sources.
Act
Query via API or SDK—or let your AI application retrieve grounded, source-backed answers in milliseconds.
Backed by the Nexelligence engine
The infrastructure Memoriq runs on, at scale.
Active Agents
Data Volume
Decision Speed
System Uptime
Core Capabilities
Everything Memoriq brings to your workflow.
Autonomous Web Crawling
Continuously discovers and crawls the sites and domains you care about, refreshing content on a schedule so your knowledge base never goes stale.
Multi-Format Document Ingestion
Upload PDFs, DOCX, images, slides, spreadsheets, and code—each is parsed, chunked, and embedded automatically.
Unified Vector Retrieval
Every source lands in one vector database, so a single query reaches across the web and your documents in one result set.
Advanced RAG Pipelines
Hybrid search, re-ranking, and retrieval-tuned chunking ensure the most relevant evidence surfaces first.
GraphRAG with Knowledge Graphs
Entities and concepts are linked across sources, letting queries answer relational questions that pure vector search cannot.
Source-Backed Answers
Every retrieved passage carries provenance, so AI applications can cite exactly where an answer came from.
API & SDK for AI Apps
A clean retrieval API and developer SDK let any AI application query the knowledge base with full control over retrieval strategy.
Freshness & Scheduling
Configurable crawl schedules and change detection keep index and graph in sync with the sources that matter.
AI Technologies
The models and methods powering Memoriq.
Where Memoriq shines
Customer Support Knowledge
Answer support queries with grounded, current answers drawn from docs, wikis, and the web.
AI Application Backend
Give your AI apps a ready-made retrieval layer over your private and public knowledge.
Enterprise Research & Search
Unify scattered internal documents into one searchable, graph-aware knowledge base.
Built for your world
Where Memoriq fits across teams and industries.
Trust, by default
Enterprise-grade protections built into every Memoriq deployment.
Powered by our Services
The expertise behind Memoriq, available as engagements.
AI Knowledge Base
Build a unified knowledge foundation that powers all your AI applications. We architect vector databases, knowledge graphs, and hybrid retrieval systems that connect concepts across your enterprise data—making knowledge accessible to both humans and agents.
Retrieval Augmented Generation (RAG)
Ground your AI responses in your own data. We architect RAG pipelines that combine the fluency of large language models with the accuracy of your documents, databases, and knowledge sources—eliminating hallucinations and ensuring every answer is sourced and verifiable.
Sovereign Data LakeHouse
Architect unified, secure data foundations that keep your sensitive information strictly on-premise or within sovereign clouds. We build LakeHouse architectures optimized for agentic retrieval.
Questions, answered
How is this different from a search engine?
Search engines rank pages; Memoriq understands your corpus. It embeds everything into a vector database, links entities in a knowledge graph, and returns grounded passages your AI applications can cite.
Does it crawl anything on the web?
No. You define scope—specific sites or domains—and Memoriq honors robots.txt and your configured crawl policies.
Can it retrieve across web and uploaded documents?
Yes. Web pages and uploaded documents live in the same vector database, so a single query retrieves across both with unified ranking.
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