Keyword search with no context
Users can't find what they need even when the content exists. The problem: no semantic search.
Conversational Web
Conversational Web with RAG architecture:
crawling, embeddings, vector database and MCP endpoint for AI-powered websites.
Does your content fail to answer questions?
Keyword search with no context
Users can't find what they need even when the content exists. The problem: no semantic search.
Generic chatbots that hallucinate
Answers are not grounded in site content and generate incorrect information. The problem: chatbots without RAG.
High bounce rate due to friction
Users leave the site when they cannot navigate extensive content with ease. The problem: navigation UX.
Content invisible to AI agents
The site cannot be discovered or queried by assistants like Copilot or ChatGPT. The problem: site without MCP.
The real impact on the business
Abandonment due to friction
Users who cannot find immediate answers leave the site and do not convert.
Loss of AI discoverability
Site content is not accessible to AI agents, losing traffic and authority in the agentic ecosystem.
Answers without verifiable source
Generic chatbots generate information without grounding in the site's official content.
Conversational Web connects RAG to your real content for accurate, hallucination-free answers.
83%
of customers expect to interact with someone immediately when they contact a company (Salesforce)
80%
By 2029, agentic AI will autonomously resolve 80% of common customer service problems without human intervention. (Gartner)
81%
of customers try to resolve the issue on their own before contacting a representative (HBR)
Why choose Conversational Web
Hallucination-free answers
Generates responses based solely on indexed site content through RAG architecture with a verifiable source.
True semantic search
Understands user intent and finds relevant content even when the exact words do not match in the index.
Compatible with any website
Indexes pages, products, blog and documentation from any CMS or platform without modifying the existing architecture.
AI agent-ready website
Exposes content via MCP protocol so external agents like Copilot or ChatGPT can discover and query the site.
Technical capabilities of the service
Automatic crawling and indexing
Crawls the entire website, extracts content and segments it into chunks optimized for semantic retrieval.
Vectorization with embeddings
Converts each chunk into a numerical vector capturing its semantic meaning using OpenAI embedding models.
Reranker for Top-K precision
Applies a re-ranking stage on retrieved results to maximize relevance before sending them to the LLM.
Integrated MCP endpoint
Exposes the indexed site as an MCP server so AI agents can access content in a structured and secure way.
What we build with you
RAG pipeline design
We design the complete crawling, vectorization, vector database and generation architecture for your site.
Vector database implementation
We deploy and configure Qdrant or Pinecone with optimized indexes for your site's content volume.
Integration with your CMS or site
We connect the RAG pipeline to your existing platform without migration or changes to the current architecture.
Activación del endpoint MCP MCP endpoint activation
We enable the MCP endpoint so your site is discoverable and queryable by AI agents in the agentic ecosystem.
Frequently Asked Questions
Tech stack
