RAG development means connecting a model to your existing knowledge base (documents, databases, internal data) so it can retrieve and reason over real information at query time. Fine-tuning means retraining a model on your data to change its behavior. For most enterprise use cases, RAG implementation is faster, cheaper, and easier to maintain. Fine-tuning makes sense when you need the model to adopt a very specific style or domain vocabulary. We help you make that call during the LLM Integration Assessment.

We have production experience with Anthropic Claude Sonnet, OpenAI API, and Databricks LLM pipelines. We also work with Azure OpenAI, Vertex AI, and open-source models depending on your infrastructure and compliance requirements. We don't lock you into a single provider – we always recommend what's right for your use case, budget, and architecture.

LLM API costs are one of the most common surprises in AI feature integration. We architect for cost from day one – this includes model selection, prompt efficiency, caching strategies, and fallback logic all affect your bill. For teams already in production, our LLM Platform Retainer includes continuous monitoring and tuning to reduce API spend without degrading output quality.

A document intelligence pipeline is an LLM-powered system that extracts structured data from unstructured documents like invoices, contracts, PDFs, forms. It's the right solution when your team is manually processing documents at scale, or when template-based OCR keeps breaking on edge cases. We've built multilingual OCR AI pipelines handling Arabic-English PDF extraction with structured output – processing time reduced from 30 minutes to 5 minutes per document.

The assessment runs over 3 days at a fixed price. You get three prioritised use cases with ROI estimates, an architecture review of your existing platform, integration complexity scoring, and a recommended tech stack based on your constraints. It's designed to give you a clear go/no-go recommendation per use case, before you commit to a full build.

Yes. AI assistant development and AI chatbot development are part of our LLM Standard package. We build custom assistants connected to your knowledge base via RAG, integrated with your existing platform via API. Our open-source NestJS library (boldare/openai-assistant on GitHub) accelerates time-to-MVP for AI assistants and is the foundation we build on.

An AI assistant responds to user queries using your data. An agentic workflow acts autonomously by breaking down complex tasks, coordinating multiple steps, and executing actions across your systems without constant human input. Agentic workflow development and agent development make sense when you want AI to handle multi-step processes end-to-end, not just answer questions. We cover both – AI assistants in LLM Standard, agentic workflows in LLM Pro.

RAG makes sense when your AI needs to answer questions using your own documents, policies, or fast-changing internal data, think support copilots, internal knowledge search, or compliance-heavy document workflows. It's the wrong tool when your data is small and static enough to fine-tune once, when you need creative output rather than factual retrieval, or when your source documents are too messy and inconsistent to index reliably yet. We'll tell you honestly if RAG isn't the right fit for your use case before we build anything.