Applied AI.

Generative AI has moved rapidly from demo to deployment — and in that transition most organizations are discovering the gap between a prototype that works and a production system that does. We build applied AI features that ship with the guardrails, evaluation, and cost discipline real systems require.

• Retrieval-augmented generation (RAG) systems
• Agentic and tool-using LLM applications
• LLM integration and orchestration
• Evaluation frameworks and quality monitoring
• Prompt engineering at scale
• AI-assisted internal tooling

Production, not prototype. Demo-level prompts do not survive production traffic. We build systems with evaluation, monitoring, fallback, and cost controls.

Retrieval over hallucination. For most knowledge-work applications, RAG outperforms larger models at a fraction of the cost. We architect the retrieval layer as carefully as the generation layer.

Model-agnostic design. Model choice is a variable, not a constant. Systems are designed to swap OpenAI, Anthropic, open-source, or fine-tuned variants as the market shifts.

Evaluation before deployment. Without measurement, LLM quality drifts invisibly. We build evaluation datasets and continuous measurement into every production system.

Security and privacy scoped. Data exposure to third-party model providers is a design-phase decision; PII, IP, and regulated data paths are architected deliberately.