Custom AI Development Services: LLM, RAG & AI Agents

Not necessarily. While traditional ML requires large datasets, modern generative AI integration allows us to leverage pre-trained foundation models (like GPT-4 or Claude). We can fine-tune these models or use Retrieval-Augmented Generation (RAG) with your specific, smaller datasets to achieve highly accurate results.

The cost varies based on complexity. A custom AI integration or chatbot project typically starts around $15,000–$30,000, while complex enterprise-grade AI agent workflows or predictive analytics systems can range from $30,000–$60,000+. We prioritize identifying high-ROI use cases first to ensure your investment pays off quickly.

We prioritize security by deploying open-source models (like Llama 3) inside your secure cloud VPC (AWS/GCP), using private API connections, and ensuring your proprietary data is never leaked or used to train public foundational models. For European clients, we support GDPR-compliant deployments on EU-based servers. For US healthcare, we follow HIPAA-compliant pipeline architecture.

We focus on rapid deployment. A functional proof-of-concept can often be delivered in days or weeks. Full generative AI integration into production environments typically takes 1 to 3 months, depending on your legacy architecture and security requirements.

RAG (Retrieval-Augmented Generation) connects your LLM to a live knowledge base using vector databases like Pinecone or pgvector — ideal when your data changes frequently. Fine-tuning permanently trains the model on your data for specialized tasks. We typically recommend starting with RAG for faster deployment and lower cost, then fine-tuning only when accuracy requirements demand it.

Yes. We build production-grade AI agents using frameworks like LangGraph that can execute complex, multi-step business workflows — from lead qualification and document processing to automated invoice handling. Unlike simple chatbot demos, our agents include deterministic guardrails, fallback logic, and human-in-the-loop approval steps for critical decisions.

We use a multi-layer approach: vector search chunk verification to validate retrieved data before generation, deterministic routing that sends critical operations through traditional code APIs instead of the LLM, and post-processing guardrails that scan outputs for accuracy before rendering. For regulated industries like FinTech and Healthcare, we implement additional compliance filters.