Context and retrieval
Context engineering: define the source material, permissions, retrieval behavior, memory, citations where useful, and the boundaries of what the application should answer.
What an LLM application needs to earn user trust
Reliable behavior comes from the full system: what the user is trying to accomplish, which information the application can use, when an answer needs structure or a tool call, what happens when confidence is low, and how the team can inspect and improve the result after launch.
Product interactions and integrations
Build the user flow and application services around structured outputs, external systems, and human review where the workflow needs judgment.
Evaluation and operations
Specify useful behavior as evals, test representative cases, inspect failures, and monitor quality, latency, and cost as the product evolves.
Built for lean teams
We help founders and small engineering teams make the right product and technical choices before complexity grows: where a model creates real leverage, what should remain deterministic, which integrations matter, and what the team needs to own after delivery.
Related Frontz work
AI agents and agentic workflows extend an LLM application when the product needs tool use or multi-step execution. AI product development covers the broader path from product discovery to production delivery. nextAI-dev is a public example of Frontz's approach to AI-assisted engineering workflows.