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AI & Data Practice

Generative AI & Agent Development

Custom RAG pipelines, LLM fine-tuning, and autonomous AI agents grounded in private data.

Service Overview

Transform your proprietary enterprise data into intelligent AI assistants. We build custom Retrieval-Augmented Generation (RAG) agents and automated LLM workflows using Azure OpenAI, LangChain, and vector databases.

Who This Service Is For

Organizations looking to automate internal document research
Customer support teams seeking 24/7 intelligent agent responses
Product managers embedding GenAI features into software products

Practical Challenges We Solve

•Employees spending hours searching through thousands of PDF manuals
•Generic ChatGPT models hallucinating false business answers
•Privacy concerns surrounding company data leaking into public AI models

What We Deliver

Private enterprise RAG pipeline architecture
Document parsing, vector embedding, and indexing
Azure OpenAI model integration with guardrails
Custom web conversational UI or API integration

Scope Boundaries & Clear Commitments

Standard Included Scope
  • •RAG pipeline build
  • •Vector DB indexing
  • •Azure OpenAI model setup
  • •Security guardrails
  • •Web UI / API
Requires Separate Agreement
  • •Azure OpenAI API token consumption costs

Target Use Cases & Workflows

Internal policy & technical manual AI research agent
Customer support ticket automated resolution agent
Contract & legal document parsing workflow

How We Work & Delivery Methodology

Enterprise GenAI engineering with strict zero-data-retention, hallucination guardrails, and deterministic grounding.

01. DiscoveryScope & Wireframes
02. ArchitectureSchema & API Plan
03. Build & TestSprint Demos & QA
04. HandoverCode Transfer & Support

Core Technologies & Toolstack

Azure OpenAILangChainLlamaIndexPineconePythonFastAPI

Cost & Timeline Factors

Project estimates vary depending on technical complexity rather than flat template rates. Key cost drivers include:

  • •Volume and diversity of document formats indexed
  • •Agent autonomy level (read-only search vs multi-step actions)
  • •Fine-tuning vs standard RAG architecture requirement

Handover, IP Ownership & Support

GenAI application repository & vector indexing scripts
Model prompt engineering & guardrail configuration docs
30-day post-launch technical warranty

Client Preparation Checklist

To help us launch your project quickly, having these items ready is beneficial:

  • •Sample enterprise documents (PDF, Docx, TXT)
  • •Azure cloud subscription with OpenAI model quota
  • •Evaluation criteria & sample test question set

Frequently Asked Questions

Yes. We use enterprise Azure OpenAI instances with strict zero-data-retention policies, ensuring your data is never used to train public models.

Discuss Your Project

Share your software, cloud, or data project requirements — our engineering leads will get back to you within 24 hours.

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