Build Custom Generative AI Applications

RAG (Retrieval-Augmented Generation) gives models access to your documents in real-time—better for knowledge-heavy tasks like Q&A and document analysis. Fine-tuning customizes model behavior—better for style, tone, and specialized reasoning. Most projects need both: RAG for knowledge + fine-tuning for behavior. We recommend RAG first (faster, cheaper), fine-tuning only if RAG doesn't meet accuracy.

Costs vary by usage: Simple copilot (1M tokens/month): $500-$2K. High-volume system (100M tokens/month): $10K-$50K. We optimize through prompt engineering, caching, batch processing, and model selection. Most clients see 30-50% cost reduction in the first 3-6 months through optimization. We provide cost tracking dashboards and monthly optimization recommendations.

We implement multiple layers: on-premise deployment options, data encryption in transit/at rest, zero data retention with cloud APIs, SOC 2 compliance, HIPAA/PCI certification if needed. All development includes security audit and penetration testing. We also implement guardrails to prevent sensitive data leakage in AI responses.

Yes. We design agentic workflows where AI agents autonomously execute tasks, make decisions, and take actions within defined boundaries. Examples: autonomous document processing, intelligent triage, supply chain optimization. Agents use tool calling, RAG, memory management, and decision trees. We always include human-in-the-loop approval for high-stakes decisions.

We implement continuous improvement: collect user feedback on outputs, use human corrections to fine-tune or refine prompts, monitor performance metrics, and A/B test approaches. Most systems improve 10-20% per quarter through iterative optimization. We also implement fallback strategies and escalation workflows for uncertain outputs.