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GenAI Implementation

Strategy, fine-tuning, RAG pipelines, and production deployment of generative AI models tailored to your enterprise.

Enterprise GenAI: Beyond the Demo

Most GenAI demos are impressive. Most GenAI production systems are fragile. We specialize in the hard part: making generative AI reliable, measurable, and safe at enterprise scale.

What We Build

RAG Systems

Connect any LLM to your internal documents, databases, and knowledge bases. Enable natural language Q&A over your proprietary data with full source citation.

Fine-Tuned Domain Models

When a general-purpose LLM isn't accurate enough for your domain (legal, medical, financial), we fine-tune open-source models on your proprietary data.

LLM Evaluation Frameworks

You can't improve what you don't measure. We build automated evaluation pipelines that score your LLM outputs for accuracy, hallucination rate, and business relevance.

GenAI Content Pipelines

Scale content operations with AI-assisted writing, SEO optimization, and multi-channel distribution — with human review gates built in.

Our GenAI Stack

  • Models: OpenAI, Anthropic Claude, Google Gemini, Llama, Mistral
  • RAG: LangChain, LlamaIndex, custom vector stores
  • Vector DBs: Pinecone, Weaviate, pgvector, Chroma
  • Evaluation: RAGAS, LangSmith, custom eval harnesses
  • Deployment: AWS Bedrock, Azure OpenAI, GCP Vertex AI, self-hosted

Frequently Asked Questions

Should we build or buy our LLM?

For most enterprises, starting with a hosted model (GPT-4, Claude, Gemini) via RAG is faster and more cost-effective than fine-tuning. We help you make the right trade-off for your data sensitivity, latency, and cost requirements.

How do you handle data security with LLMs?

We architect your system so sensitive data never leaves your environment. Options include on-premises open-source models, private cloud deployments, and data masking layers for hosted models.

What is RAG and when should we use it?

Retrieval-Augmented Generation connects an LLM to your internal knowledge base. Use it when you need the model to answer questions using your proprietary data without fine-tuning.

Ready to get started?

Book a free call to discuss how GenAI Implementation can work for your business.

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