AgenticRAGVisionBot
Agentic RAG assistant combining document retrieval with computer vision — answers questions, reasons over knowledge bases, and processes images in one private AI system.
Client
Private Client
Timeline
Research to production
Type
Multimodal AI Assistant
Delivery
Private
RAG + CV
Multimodal
Agentic
Tool Use
Private
Knowledge Base
01Overview
A private agentic RAG system that ingests documents and images, retrieves relevant context, and uses tool-calling agents to answer complex queries — merging traditional RAG with computer vision and image processing capabilities.
02The Problem & The Fix
The Challenge
The client needed more than a chatbot: a system that could search internal knowledge, interpret uploaded images, chain reasoning steps, and deliver accurate answers — all running privately without exposing proprietary data to public AI services.
Our Solution
We architected an agentic RAG stack with vector retrieval, multimodal image analysis, orchestrated tool use, and guardrails — enabling document Q&A, visual inspection workflows, and multi-step reasoning in one cohesive private assistant.
03Our Approach
Knowledge Architecture
Designed chunking, embedding, and retrieval strategies for documents and visual assets.
Agent Orchestration
Built tool-calling agents that route between search, vision analysis, and synthesis steps.
Vision Integration
Connected image processing pipelines so the agent can reason over visual inputs alongside text.
04What We Delivered
Key Deliverables
- Agentic RAG orchestration layer
- Vector knowledge base & ingestion pipeline
- Multimodal image analysis module
- Private API for internal queries
- Evaluation & safety guardrails
Features Built
- Retrieval-augmented generation over private document corpora
- Computer vision and image processing for visual Q&A
- Multi-step agentic reasoning with tool calling
- Configurable knowledge sources and access scopes
- Fully private deployment — no public demo URL
05The Impact
Unified private AI assistant for text and image-based queries
Reduced time spent searching internal documentation manually
Agentic workflows that chain retrieval, vision, and synthesis
Secure architecture keeping proprietary data in-house
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