Conversational AI, RAG & Semantic Search
AI-powered Q&A for PDPA compliance — zero-hallucination answers from your document corpus, every claim verifiable, every source traceable
Retrieval-Augmented Generation (RAG) combines the power of large language models with precise information retrieval. Instead of relying on what the model 'remembers' from training, RAG retrieves relevant documents in real-time and uses them to generate accurate, cited responses.
- Grounds every AI response in your actual document corpus
- Eliminates hallucinations through source verification
- Works with any document format: PDF, Word, HTML, scanned images
- Maintains full audit trail for regulatory compliance
Retrieval-Augmented Generation (RAG)
RAG eliminates AI hallucinations by grounding every response in your actual documents. When a user asks a question, the system retrieves relevant passages from your Vector Database, then generates an answer citing specific sources. Every claim is verifiable, every source is traceable.
Traditional generative AI can fabricate plausible-sounding but incorrect information. RAG solves this by constraining the AI to only use information from your verified document corpus. The system retrieves the most semantically relevant passages before generating a response, ensuring factual accuracy.
Our RAG implementation uses a multi-stage retrieval pipeline: initial semantic search narrows candidates, re-ranking refines relevance, and the generation model produces a coherent answer with inline citations pointing to exact document locations.
- Multi-stage retrieval: semantic search → re-ranking → generation
- Inline source citations with page and paragraph references
- Confidence scoring flags uncertain answers before delivery
- Supports both Thai and English queries against bilingual corpora
Vector Database Technology
Our Vector Database converts documents into high-dimensional embeddings that capture semantic meaning. Unlike keyword search, semantic search understands intent — finding relevant information even when exact terms differ. This powers cross-language retrieval across Thai and English documents.
- High-dimensional vector embeddings capture semantic meaning
- Cross-language retrieval: query in Thai, find English documents (and vice versa)
- Sub-10ms search latency across 10M+ document vectors
- Automatic re-indexing when documents are updated or added
Zero Hallucinations. Verifiable Truth.
Enterprise AI cannot afford fabricated answers. Our confidence scoring system flags low-certainty responses before they reach users. Every generated answer includes page-level source citations, enabling instant human verification.
- Confidence threshold gating — low-certainty answers flagged for review
- Page-level and paragraph-level source citations
- Audit trail of every query, retrieval, and generated response
- Human-in-the-loop verification workflows for critical decisions
PDPA Conversational AI Assistant
An AI-powered Q&A assistant that staff can query in natural Thai or English about PDPA compliance. The conversational AI draws from your organization's policy corpus and PDPA regulations to provide instant, verified answers — serving as both a compliance support tool and a training resource for privacy awareness.
- Natural Thai and English Q&A for PDPA compliance questions
- Draws from PDPA legislation, organizational policies, and regulatory guidance
- Staff training tool — employees learn PDPA requirements through interactive Q&A
- Pre-built PDPA knowledge base covering all sections of the Act
- Conversation history and analytics for compliance monitoring
System Architecture
How It Works
Ingest Documents
Upload documents in any format. The pipeline extracts text, handles OCR for scanned documents, and prepares content for embedding.
Generate Embeddings
Each document chunk is converted into a high-dimensional vector that captures its semantic meaning, then stored in the Vector Database.
Query & Retrieve
When a user asks a question, the system finds the most semantically relevant passages from across your entire document corpus.
Generate & Cite
The AI generates a natural-language answer grounded in retrieved sources, with inline citations pointing to exact document locations.
Ingest Documents
Upload documents in any format. The pipeline extracts text, handles OCR for scanned documents, and prepares content for embedding.
Generate Embeddings
Each document chunk is converted into a high-dimensional vector that captures its semantic meaning, then stored in the Vector Database.
Query & Retrieve
When a user asks a question, the system finds the most semantically relevant passages from across your entire document corpus.
Generate & Cite
The AI generates a natural-language answer grounded in retrieved sources, with inline citations pointing to exact document locations.
Use Cases
Regulatory Research
Instantly find relevant PDPA sections, BOI conditions, or SEC requirements by asking natural-language questions.
Contract Analysis
Query across thousands of contracts to find specific clauses, obligations, or risk patterns.
Policy Q&A Portal
Provide employees with an AI-powered internal knowledge base that answers policy questions with cited sources.
Due Diligence
Accelerate M&A due diligence by semantically searching across target company documentation.
Compliance Monitoring
Track regulatory changes and automatically identify which internal documents need updating.
Multilingual Support
Support teams working across Thai and English documents with cross-language search capabilities.
Staff PDPA Training
Use the conversational AI assistant as an interactive training tool — staff ask PDPA compliance questions in natural language and receive verified, cited answers that build privacy awareness.