Generative AI Development Company

Generative AI projects at iCoderz range from $10,000 for a focused RAG knowledge assistant to $100,000+ for a full-featured AI product with fine-tuning, multi-agent orchestration, and enterprise integrations. A basic LLM-powered chatbot or document Q&A system starts from $10K–$20K. We provide a detailed, milestone-based estimate after a free discovery call.

The right LLM depends on your use case, data privacy requirements, latency needs, and budget. GPT-4o and Claude 3.5 Sonnet excel at complex reasoning and instruction following. Gemini 1.5 Pro is strong for multimodal and very long context windows. Llama 3 is ideal for on-premise deployment where data cannot leave your infrastructure. We evaluate and recommend the best fit based on your specific requirements — not on vendor preference.

RAG (Retrieval-Augmented Generation) grounds LLM responses in your specific data — product documentation, internal knowledge bases, or customer records — instead of relying on the model’s general training knowledge. This dramatically reduces hallucinations, keeps responses accurate and up to date, and ensures the AI answers from your business context rather than generic information. For enterprise GenAI, RAG is almost always the correct architecture.

Yes. Fine-tuning on your domain-specific data improves model accuracy, tone consistency, and domain knowledge for tasks like customer support, legal document analysis, or technical content generation. We advise on dataset requirements (typically 500–5,000 high-quality examples), run fine-tuning experiments, and evaluate results rigorously before committing to production.

We combine multiple layers: RAG for factual grounding, output validators and guardrails, citation requirements, chain-of-thought prompting, human-in-the-loop workflows for high-stakes decisions, and evaluation frameworks measuring faithfulness and hallucination rate before and after deployment using Ragas and TruLens.

A focused RAG system or LLM chatbot typically takes 6–10 weeks. A full AI product with fine-tuning, multi-agent orchestration, and enterprise integrations takes 14–24 weeks. We begin with a 2-week discovery sprint to define scope and success metrics — then deliver in iterative 2-week sprints with testable milestones throughout.

Data privacy is built into our architecture from day one. We work under NDA (signed before any discussion), support on-premise deployment using Llama or private OpenAI tiers for sensitive data, and build GDPR and HIPAA-compliant pipelines where applicable. Your proprietary data is never used to train third-party models without explicit written consent.