Private Document RAG Systems
Connect company manuals, technical guidelines, and legal contracts to LLMs with strict source citations and zero hallucination.
AI Consulting: Autonomous Agents, RAG Systems & Operational Automation
We integrate frontier language models directly with your proprietary documents, SQL databases, and operational workflows. Practical, secure, zero-hallucination architectures for enterprises and professionals.
ENGINEERING CAPABILITIES
Specialized AI Advisory & Development Services
Autonomous AI Agents
Develop tool-calling agents capable of executing multi-step goals: SQL querying, analytical report generation, and external API execution.
1-on-1 Technical Mentoring
Direct hands-on advisory to diagnose model selection, review AI pipeline code, and unblock architectural bottlenecks in production.
AI Automation with Excel & ERPs
Intelligent bridges that extract unorganized emails or invoice data using AI and load them cleanly into Excel tables and SAP systems.
Prompt Engineering & Model Evals
System prompt optimization, strict structured JSON output enforcement, and automated eval suites for OpenAI, Claude, and Gemini.
WhatsApp & Web Conversational Agents
Intelligent 24/7 conversational agents hooked directly to WhatsApp Business and web applications for technical client qualification.
Private VPS AI Deployment
Hardening and self-hosting of models and vector stores on dedicated Linux servers, enforcing zero third-party data retention.
Document Intelligence (Vision + LLM)
Transformation of multi-page scanned PDF contracts, financial statements, and complex tables into audit-ready structured data.
End-to-End Turnkey Development
Complete prototyping to production delivery: infrastructure configuration, documented source code, and team onboarding.
TECHNICAL BLUEPRINTS
How We Architect Production-Ready AI Systems
Corporate RAG (Retrieval-Augmented Generation) Architecture
Your confidential documents are chunked, vectorized, and stored in a private semantic index. When queries arrive, only relevant context snippets are provided to the model with page-level citations.
[PDFs / Internal Manuals / Contracts]
│ (Document ingestion & semantic chunking)
▼
[Vector Database // Chroma / Qdrant on your VPS]
│ (Cosine similarity retrieval)
▼
[LLM // Gemini / Claude / OpenAI] ──► Exact cited answer with 0% hallucination
│ (Document ingestion & semantic chunking)
▼
[Vector Database // Chroma / Qdrant on your VPS]
│ (Cosine similarity retrieval)
▼
[LLM // Gemini / Claude / OpenAI] ──► Exact cited answer with 0% hallucination