The problem

Modern application development with large language models is constrained two ways at once. Single-model approaches force one general-purpose model to handle architecture, UI, business logic, data, security, and testing equally well, which no model does well. Naive multi-model approaches lose context and consistency at the boundaries between calls. Most LLM development tooling picks one failure mode without acknowledging the other exists.

The cost dimension matters too. Running everything through Claude Sonnet or GPT-4 for a build that includes hundreds of routine boilerplate generations burns dollars and minutes that should be going to the parts of the build that actually need that level of reasoning capability.

The approach

AIOrchBuilder treats LLM application building as an orchestration problem, not a prompting problem. Seven specialist agents with defined input and output contracts handle distinct phases of the build: UI, logic, data, API, auth, test, and review. An orchestrator coordinates handoffs between agents, enforces the contracts, and logs every action to an auditable Retrospective Audit Path so failures can be traced and replayed.

A separate LLM router evaluates per-agent task complexity and dynamically picks the model. Architectural reasoning routes to high-context reasoning models. Routine boilerplate routes to fast cheap ones. Data schema design routes to models with strong structured-output capability. The provider abstraction layer supports Ollama for self-hosted local models, which matters for cost control at scale and for any deployment where data residency or privacy rules out commercial API providers.

Security is wired in from Day 1, not bolted on. Every operation is associated with a user and a role. Every database table includes RBAC fields. Every API endpoint requires authentication. Supabase row-level security policies enforce access control at the database layer, so a compromise of the application layer does not automatically expose the data.

Status

Active prototype, not a one-command demo. It has been used to scaffold real applications and the methodology has been validated end-to-end on production-grade workloads. Deployment requires a Supabase project, an LLM provider configuration (cloud or Ollama), and the resources outlined in the deployment guide.

The orchestration patterns informed the codebase structure of FieldForce Tool Tracker, a real-time GPS asset management platform for utility operations, which proved the methodology works at production scale across a monorepo of three apps with enterprise auth and time-series data tracking.