When should a business choose RAG over Fine-Tuning?

RAG (Retrieval-Augmented Generation) is ideal for enterprise applications requiring real-time updates, strict source attribution, and zero data leakage without high retraining costs. Fine-tuning is preferred when altering model style, domain jargon, specialized syntax, or reducing latency for small localized models.

Is private corporate data safe when using Generative AI?

Yes. We architect private RAG pipelines using enterprise cloud endpoints (AWS Bedrock, Azure OpenAI, GCP Vertex AI) or deploy open-weight models (Llama, Mistral) on dedicated private infrastructure with zero external data retention.

Enterprise LLMs & RAG Architecture

Generative AI & LLM Application Services

Build secure LLM-powered tools, enterprise retrieval-augmented generation (RAG) architectures, internal domain knowledge bases, and custom generative applications.

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Generative AI Deliverables

From proof of concept to secure multi-tenant production setups, we deliver robust LLM applications.

Enterprise RAG Systems

Connect LLMs to internal PDFs, Notion, Confluence, SharePoint, and SQL databases with hybrid semantic search and strict document access filters.

Fine-Tuning & Model Adaptation

Fine-tune open-weight models (Llama 3, Qwen, Mistral) on proprietary domain datasets for specialized task accuracy and cost reduction.

Private LLM Infrastructure

Depending on project requirements, solutions can be designed for managed cloud, private-cloud, or customer-controlled infrastructure, configured to prevent model training on your private business data.

Security, Verification & Guardrails

Generative AI systems require defensive engineering against hallucinations, prompt injection attacks, and data leakage. Our applications include active guardrails at every layer.

Prompt Injection Protection

Input sanitization and adversarial detection to prevent prompt override attempts.

Citation & Source Attribution

Direct link generation to underlying source documents for factual verification.

Cost & Token Optimization

Semantic caching, model routing, and prompt compression to control API usage costs.

Frequently Asked Questions

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