24/7 Sales & Operational AI Agents
Agents operating across WhatsApp Business and web portals to qualify prospects, check live stock, calculate custom pricing, deliver PDF quotes, and schedule executive meetings.
🤖 Enterprise AI Engineering · Autonomous Agents
Autonomous AI Agents, Enterprise LLMs & Langflow Workflows
We architect and deploy autonomous Artificial Intelligence Agents securely connected to your operational systems: from 24/7 conversational sales reps on WhatsApp and Zenit CRM to operational agents that inspect live inventory in Zenit WMS, process unstructured invoices, and execute workflows via MCP and private RAG.
Visual Orchestration with Langflow
Model Context Protocol (MCP) Tools
Private RAG over Enterprise Docs
OpenAI · Claude · Gemini · Private Local LLMs
Omnichannel Input (WhatsApp / Web / ERP)
“Do you have 120 units of SKU #ZR-400 in stock and can you send me a formal quotation?”
LLM Reasoning + Tool Calling (MCP)
1. Check warehouse inventory in Zenit WMS API (340 units available) · 2. Generate PDF quote in Zenit CRM.
Audited Execution in 4 Seconds
Quotation #COT-8912 delivered to client with deal stage updated in CRM funnel.
APPLIED ARTIFICIAL INTELLIGENCE CAPABILITIES
Connecting the reasoning power of Large Language Models (LLMs) with your enterprise databases and business rules
Visual Orchestration with Langflow
Maintainable, visual workflows linking prompt templates, conversational memory, routing logic, and enterprise tools with complete architectural auditability.
Private Knowledge Bases with RAG
Retrieval-Augmented Generation (RAG) over internal technical documentation, contracts, catalogs, and operational SOPs to provide zero-hallucination answers with citations.
Secure MCP Protocol & Tool Calling
We deploy Model Context Protocol (MCP) servers with granular permission scopes so agents interact directly with your ERP, WMS, CRM, and SQL tables safely.
Intelligent Document Processing (IDP)
Automated OCR and structured data extraction from purchase orders, invoices, tax documents, and supplier agreements, feeding clean JSON into your systems.
Data Governance, Privacy & Guardrails
Your enterprise data is never used to train public LLM models. We implement semantic guardrails, full audit logging, and seamless escalation to human supervisors.
TRADITIONAL CHATBOT VS. ZENIT ENTERPRISE AI AGENT
Why forward-thinking enterprises are replacing rigid button trees with autonomous system-connected agents
| Operational Dimension | Traditional Rule-Based Chatbot | Zenit Enterprise AI Agent |
|---|---|---|
| User Comprehension | Fails if the user deviates from preset numbered menus ("Press 1 for Sales"). | Understands natural human language, voice memos, photos, and PDF attachments with context. |
| Knowledge Source | Static hardcoded scripts that quickly become obsolete and difficult to update. | Live RAG architecture connected directly to active catalogs, SOPs, and policies. |
| System Execution | Limited to capturing basic contact info or sending a generic notification email. | Queries real-time stock in Zenit WMS, creates PDF quotes in Zenit CRM, and books calendar calls. |
| Orchestration & Control | Opaque proprietary black boxes with zero visibility into reasoning steps. | Transparent, auditable flows in Langflow and governed tools via MCP. |
| Human Escalation | Customer is dumped into a queue and forced to repeat their entire inquiry from scratch. | Provides the human executive with a comprehensive summary and detected intent. |
ENTERPRISE AI AGENTS FAQS
Security, architectures, and integration workflows
What is the difference between a traditional button chatbot and an enterprise AI agent by Zenit?
A traditional chatbot follows rigid decision trees. A Zenit AI Agent understands natural language (text, voice notes, PDFs), retrieves proprietary knowledge via RAG without hallucinating, and executes verified actions across enterprise systems (checking WMS stock, generating CRM quotes, or opening ERP tickets) through controlled tool calls.
Is our proprietary enterprise data used to train public AI models?
Never. We enforce strict enterprise data governance using private API endpoints with zero-data-retention agreements (OpenAI Enterprise, Anthropic Claude, Google Vertex AI), or private self-hosted open-weights models (Llama, DeepSeek) deployed on isolated servers with pgvector.
What is the Model Context Protocol (MCP) and why is it critical?
MCP is the open industry standard for securely exposing data sources and business tools to LLMs. Instead of brittle custom glue code, MCP enforces granular role-based permissions, allowing AI agents to query databases and trigger authorized workflows safely.
