RAG AI Agent
RAG AI Agent
Retrieval-Augmented Generation solution that answers queries using approved documents, manuals, and knowledge bases with source citations.
Quick Service Summary
- Parent Domain: Custom AI Agent Development Services
- Execution Model: Custom Engineering Scope
- Verification: Bench & Functional Testing
- Delivery Deliverables: Schematics, Code & Documentation
The Engineering Challenge
Engineers, technicians, and customer teams waste hours searching through hundreds of PDFs, technical manuals, and SOPs to find specific operational answers.
The Desired Outcome
A private, document-grounded AI assistant that retrieves precise technical answers from verified corporate PDFs, citing the exact page and source reference.
Core Technical Capabilities
Application-specific engineering focused on reliability, performance, and maintainability.
Document ingestion pipeline: PDFs, Word documents, Markdown, policy manuals
Semantic chunking and embedding generation stored in secure vector databases
Strict citation enforcement: answers include document title, chapter, and page number
Hallucination prevention: answers only from provided documents; states if unknown
Role-based document access: ensures users only query authorized internal files
Private deployment options: on-premise LLMs or private cloud API integrations
Technical Approach
Documents are parsed, cleaned, and embedded into a vector database (e.g. Qdrant / pgvector). Hybrid semantic and keyword search retrieves top relevant passages, which are synthesized by a guarded LLM into a cited response.
Required Customer Inputs
Set of approved source documents (PDFs, manuals, SOPs), user permission tiers, and hosting preference (cloud vs private server).
Project Deliverables
- Deployed RAG AI Agent web interface / chat widget
- Vector database setup and automated document ingestion pipeline
- Configured citation and answer verification guardrails
- Knowledge base management handbook and security audit documentation
Scope Boundaries & Safety Safeguards
The agent’s knowledge is bounded by the documents provided. It does not replace engineering judgement or authoritative safety decisions. TC Smart Technology adheres to transparent engineering standards: solutions do not bypass machine safety interlocks, and all AI features retain human-in-the-loop governance.
From Requirement to Deployment
Standardized stages ensuring every milestone is verified against agreed criteria.
Requirement Analysis
Define functionality, inputs, operational boundaries & success criteria.
Solution Architecture
Engineer schematic, firmware, protocol mapping or AI workflow design.
Design & Development
Build approved deliverables in iterative, reviewable milestones.
Integration & Testing
Verify edge cases, signal fidelity, fault handling & compliance checks.
Deployment & Handover
Deliver code, documentation, commissioning assistance & post-release support.
Technical & Integration FAQs
Answers to common feasibility, architecture, and deployment questions.
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Read Solution SpecsDiscuss Your RAG AI Agent Requirement
Share your product idea, equipment parameters, or operational workflow with our engineering team. We will help you identify a practical, robust technical architecture.