We work with all major providers: OpenAI (GPT-4o), Anthropic (Claude 3.5), Google (Gemini Pro), and leading open-source models like Mistral and LLaMA. We select the best model for each specific task based on capability, cost, and data privacy requirements.

Fully model-agnostic. We have no commercial relationship with any AI provider. We choose models based entirely on what delivers the best result for your use case — not what pays us a commission.

We use n8n and Make as our primary automation platforms for most projects. For simpler integrations, Zapier works well. For complex, high-volume workflows, we build custom Python-based pipelines.

Yes, when appropriate. Open-source models (Mistral, LLaMA, Phi) are particularly useful when data privacy is critical and you need models running on your own infrastructure rather than third-party APIs.

Yes. We build around what you already have. We’ve integrated AI with over 50 different tools and platforms. We don’t force a technology change — we enhance what’s working.

LangChain is a framework for building AI applications — particularly for chaining together AI models, tools, and data sources. We use it for complex multi-step AI pipelines, RAG systems, and agent architectures.

A vector database (like Pinecone or Supabase pgvector) stores information in a way that allows AI to search by meaning, not just keywords. You need one when building RAG systems — so AI can retrieve the right internal documents to answer questions accurately.

Primarily GPT-4o and Claude 3.5 for most production deployments — they offer the best balance of capability and reliability. We fine-tune custom models when you have specific domain knowledge that general models lack.

Yes. Fine-tuning trains a model specifically on your data, making it more accurate for your domain. We handle data preparation, training, evaluation, and deployment. It’s particularly valuable for industry-specific terminology or classification tasks.

Yes. We deploy on all major cloud platforms depending on your existing infrastructure and preferences. We also work with on-premise deployments for clients with strict data residency requirements.