How Reficera Was Built: AI Orchestration from Spec to Production
A behind-the-scenes look at building a full personal finance app by orchestrating AI agents — from planning and specification through design, validation, and delivery.
The problem with “just prompting” AI
Most people who use AI for development open a chat, describe what they want, and hope for the best. Sometimes it works. Often it doesn’t — and when it fails, it fails in ways that are hard to debug because there was never a spec to check against.
Reficera wasn’t built that way.
What Reficera is
Reficera is a personal finance application — live in production with real users since March 2026. It includes monthly and annual dashboards, budget management with custom subcategories, full transaction history, authentication, internationalization (English and Spanish), and profile management.
The entire application was built through AI orchestration — not casual prompting, but structured direction of AI agents through a defined methodology.
The methodology: Plan before you build
Every feature in Reficera went through the same sequence:
- Planning and Specification — Define exactly what needs to be built. Requirements, constraints, edge cases, acceptance criteria. Nothing is ambiguous.
- Design — Architecture decisions, component structure, data models. This is where stack choices happen — for Reficera, that meant Next.js, React, TypeScript, PostgreSQL, and Docker.
- Validation — Does the plan hold up? Are there contradictions? Missing pieces? This gate exists specifically to catch problems before they become code.
- Execution — Only now do AI agents write code. They work within the validated spec, not freestyle. The orchestrator directs, the agents execute.
- Verification — Does the output match the spec? Quality checks, security review, anti-hallucination validation.
What AI orchestration actually looks like
The term “AI orchestration” sounds abstract until you see it in practice. Here’s what it means concretely:
- Multiple AI models working in coordination — not just one chat window
- Specialized agents for different phases: one for specs, one for implementation, one for verification
- Persistent context management — agents don’t lose track of what was decided three steps ago
- Anti-hallucination systems — active checks that prevent agents from inventing APIs, fabricating data structures, or producing code that contradicts the spec
- Skills systems and MCPs — loaded on demand, not all at once, keeping each agent focused on its specific task
What went right
The methodology caught real problems before they became code. During the validation phase for Reficera’s budget system, the spec revealed a conflict between how subcategories should inherit limits from parent categories and how monthly rollovers would work. That conflict was resolved in the spec — not discovered after implementation.
The verification phase caught hallucinated API endpoints twice. An agent generated code calling endpoints that didn’t exist in the backend spec. The anti-hallucination system flagged these before they were committed.
What’s honest about the scope
Reficera is an MVP. It has 3 early users, all US-based. It’s still in active development. The transaction import flow needs work. The mobile experience is functional but not polished.
This honesty matters because the point isn’t “look at this perfect app.” The point is: a structured methodology produced a real, working, deployed product — and the same methodology scales to larger projects with larger teams.
The takeaway
AI orchestration isn’t about using AI harder. It’s about using it with the same discipline you’d apply to any serious engineering project: plan first, validate before building, verify after delivery.
Reficera is the proof that this works. Not because it’s perfect — because it’s real.
Want to know more about the methodology? See the Technical Focus page for the full breakdown, or get in touch to discuss how this approach could work for your project.