CAPABILITY 02 ยท AI & INTELLIGENT AUTOMATION

Custom RAG Architecture & Knowledge Systems

Custom RAG architecture, grounded assistants, enterprise document intelligence, vector search & citations.

Turn complex policies, technical manuals, contracts, LMS content, and support tickets into verifiable knowledge systems. Using Custom RAG Architecture, hybrid keyword + vector retrieval, and reranking, our knowledge assistants deliver precise answers with exact page and document citations while respecting user permissions.

KNOWLEDGE SEARCH SPEED
< 2 Sec
Enables staff and customers to instantly query thousands of pages of internal documentation.
[LIVE_CAPABILITY_DEMO_WINDOW] ACTIVE SIMULATION
Enterprise Knowledge & RAG Engine
LIVE LOG PIPELINE ยท THREAD #01RETRIEVAL_READY
>๐Ÿ“„ Ingesting SecurityPolicy_2026.pdf (142 pages)
>โšก Generating 1,536-dim Vector Embeddings
>๐Ÿ” Hybrid Search (Dense + Sparse Reranking)
>๐ŸŽฏ Output Generated with 3 Verifiable Document Citations
BENCHMARK:100% Citation Grounding
[ARCHITECTURE_PIPELINE]

How This Solution Works in Operations

A four-stage deterministic engineering process designed for reliability and zero security risk.

01

Document Parsing & Chunking

Extract text, tables, and images from PDFs, Word docs, and web pages.

02

Vector Embedding & Hybrid Indexing

Index content using high-dimensional vector databases and full-text search.

03

Dense Retrieval & Reranking

Fetch top relevant passages using semantic similarity and cross-encoder rerankers.

04

Grounded Synthesis & Citations

Synthesize answers with direct links to source documents and page numbers.

[ENGINEERING_DELIVERABLES]

What We Deliver to Your Enterprise

Every engagement includes clean maintainable code, architecture documentation, automated tests, and operational handover.

โœ“ Document Parsing & ETL Pipeline (PDF, DOCX, HTML, SQL)
โœ“ Vector Database Setup (Qdrant, pgvector, Pinecone)
โœ“ Hybrid Retrieval & Cross-Encoder Reranking Engine
โœ“ Role-Based Access Control (RBAC) & Document Security
โœ“ Grounded Citation UI Component with Document Viewer
โœ“ RAG Evaluation Benchmark & Hallucination Test Suite
[VERIFIED_CASE_STUDY]

Internal SOP & Policy Knowledge Assistant

[THE_CHALLENGE]

Support agents spent 15+ minutes hunting through 500+ compliance PDFs to answer client freight queries.

[THE_SOLUTION]

Engineered a private grounded RAG assistant with role-based document access and instant page-level citations.

[VERIFIED_RESULT]

Reduced policy search time by 92%, boosting support ticket resolution speed from hours to seconds.

CLIENT: Global Logistics & Freight Group Discuss Similar Project →
[EXECUTIVE_FAQS]

Frequently Asked Technical Questions

Will our proprietary corporate data be used to train public AI models?

Never. We deploy RAG systems using isolated private vector stores and enterprise LLM endpoints with zero data retention policies.

How accurate are answers generated by your RAG engine?

Our hybrid dense + sparse retrieval architecture guarantees 100% verifiable source citations, eliminating hallucination risks.

Can the RAG system handle complex tables and PDFs?

Yes, our document ingestion pipeline uses vision-assisted OCR and table extraction to preserve structured tabular data.

[READY_TO_BUILD]

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