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AI Services / 02

Generative AI & LLM Engineering

Deploy enterprise-grade Generative AI — RAG systems over your private data, custom copilots, LLM fine-tuning, and AI agents that automate complex knowledge work. Built with security, governance, and cost efficiency at the core.

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Models We Work With
GPT-4o Claude 3.5 Llama 3 Mistral Gemini Phi-3 Falcon
Enterprise GenAI Architecture
LLM / Foundation ModelGPT-4o, Claude, Llama — Azure OpenAI, Bedrock, Vertex AI
RAG PipelineVector DB, embedding model, retrieval, reranking, prompt
Knowledge BaseYour private data — SharePoint, SAP, databases, PDFs
Agent FrameworkLangChain, LlamaIndex, AutoGen, custom agents
Security & GovernanceData privacy, PII masking, content filters, audit logging
GenAI Services

What We Build

RAG Systems & Enterprise Search

Retrieval-Augmented Generation over your private knowledge base — SharePoint, SAP documents, technical manuals, policies — with source citation and hallucination mitigation.

Custom AI Copilots

Domain-specific AI assistants for sales, HR, legal, finance, and engineering — embedded in Teams, Slack, or your applications. Role-aware, data-secured, and auditable.

LLM Fine-Tuning

Supervised fine-tuning of open-source LLMs (Llama, Mistral, Phi) on your domain data — for specialized tasks like contract review, medical coding, or customer communications.

AI Agent Development

Autonomous AI agents using LangChain, AutoGen, or CrewAI — for multi-step reasoning, tool use, API orchestration, and complex workflow automation with human-in-the-loop controls.

Document Intelligence

AI-powered document processing — contract analysis, invoice extraction, compliance review, and report generation using GPT-4o Vision, Azure Document Intelligence, and custom pipelines.

Conversational AI & Chatbots

LLM-powered conversational experiences — customer service bots, internal helpdesks, and voice assistants — far beyond the limitations of traditional rule-based chatbots.

How We Build RAG

The RAG Engineering Process

1
Data Ingestion & Chunking

Ingest documents, split into optimal chunks with metadata preservation and hierarchy awareness.

2
Embedding & Vector Indexing

Generate embeddings with ada-002, BGE, or text-embedding-3 and index in pgvector, Pinecone, or Azure AI Search.

3
Hybrid Retrieval

Combine semantic vector search with BM25 keyword search and reranking for precision retrieval.

4
Prompt Engineering & Generation

Structured prompts, few-shot examples, and output format control for consistent, accurate responses.

5
Evaluation & Continuous Improvement

RAG evaluation frameworks (RAGAS) to measure faithfulness, relevance, and answer correctness over time.

Technology Stack

GenAI Engineering Tools

LLM Platforms Azure OpenAIAWS BedrockGoogle Vertex AIHugging Face
Frameworks LangChainLlamaIndexAutoGenSemantic KernelHaystack
Vector Databases pgvectorPineconeWeaviateAzure AI SearchQdrant
Evaluation & Safety RAGASPromptflowAzure Content SafetyGuardrails AI
Build Smarter

Bring Generative AI to Your Enterprise — Securely.

Our GenAI engineers have delivered production RAG systems, copilots, and AI agents for enterprises across finance, retail, and healthcare. Start with a 4-week PoC.

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