Production-grade AI systems for teams past the prototype.

We build, refactor, and scale the AI applications that move from demo to revenue. Engineering rigor where vibe-coded systems fall apart.

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The full picture

What a production AI system actually requires.

Most AI demos are 20% of the system. We build and operate the other 80%.

The anatomy of a production AI system A layered system showing the user surface, orchestration, reasoning components, foundation, and cross-cutting governance concerns. 001 · USER SURFACE Chat, API, agent, embedded UI 002 · ORCHESTRATION Router, agent loop, tool calls, prompt templates decides what runs, in what order, with what context 003 Models Claude · GPT · Gemini routing, fallback, cost 004 Retrieval pgvector, hybrid search reranking, grounding 005 Tools CRM · DB · third-party typed schemas, sandboxing 006 · FOUNDATION Data pipelines, embeddings, feature store, vector index the substrate everything reasons over CROSS-CUTTING Eval pipelines Observability Drift detection Audit trails Cost tracking Access control Safety filters Versioning Incident response Demo systems usually have only the top two layers. Production systems need every layer in this picture. The anatomy of a production AI system A vertical view of the layers in a production AI system: user surface, orchestration, three reasoning components, foundation, plus cross-cutting concerns at the bottom. 001 · USER SURFACE Chat, API, agent, embedded UI 002 · ORCHESTRATION Router, agent loop, tool calls decides what runs, when, with what context 003 — 005 · REASONING Models Claude · GPT · Gemini routing, fallback, cost Retrieval pgvector, hybrid search reranking, grounding Tools CRM, DB, third-party APIs typed schemas, sandboxing 006 · FOUNDATION Data pipelines, embeddings, vectors the substrate everything reasons over CROSS-CUTTING · ALL LAYERS Eval pipelines Cost tracking Observability Access control Drift detection Safety filters Audit trails Versioning Incident response Demo systems usually have only the top two layers. Production systems need every layer.

Capabilities

Where we work across the lifecycle.

01

AI application engineering

RAG systems, agent workflows, multi-LLM orchestration designed for accuracy under load.

02

Vibe-coded rescue

Stabilize, harden, and scale AI systems that broke down moving from demo to production.

03

Scaling infrastructure

Streaming, persistence, evals, and observability built for real production traffic.

04

AI transformation advisory

Architecture review, model selection, and roadmaps that survive contact with reality.

The wedge

The gap between a demo and a system that ships.

Vibe-coded prototypes pass demo day. Then they meet production traffic, real edge cases, and audit requirements. We rebuild them into systems that survive.

Vibe-coded prototype versus production-grade AI system Side by side comparison: a vibe-coded prototype has only a hardcoded prompt feeding a single LLM call returning a string, while a production system adds versioning, retrieval, multi-model routing with fallback, schema validation, evals, and observability. BEFORE · VIBE-CODED works in the demo, breaks in production [1] Hard-coded prompt strings glued together at runtime [2] Single LLM call one model, no fallback, no retry [3] String response free-form text, hope for the best no retrieval · no schema · no evals no fallback · no observability no audit trail · no cost tracking REBUILD our work AFTER · PRODUCTION survives real traffic and audit Versioned prompt tracked, A/B tested Retrieval grounded context Multi-model router with fallback routes by complexity, retries on failure Schema validator typed JSON outputs Eval harness catch drift early Typed structured output + observability · audit log · drift detection + cost tracking · version control · access policies Vibe-coded prototype versus production-grade AI system Vertical comparison: top half shows a vibe-coded prototype with hardcoded prompt, single LLM call, string response, and missing infrastructure. Bottom half shows a production-grade system with versioned prompts, retrieval, multi-model routing with fallback, schema validation, evals, and observability. BEFORE · VIBE-CODED works in the demo, breaks in production Hard-coded prompt strings glued together at runtime Single LLM call one model, no fallback String response free-form text, hope for the best no retrieval · no schema · no evals · no fallback no observability · no audit · no cost tracking REBUILD ↓ AFTER · PRODUCTION survives real traffic and audit Versioned prompt tracked, A/B tested Retrieval grounded context Multi-model router with fallback routes by complexity, retries on failure Schema validator typed JSON outputs Eval harness catch drift early Typed structured output + observability · audit log · drift detection + cost tracking · version control · access policies

Selected work

Real systems, in production, under real load.

BEVERAGE ALCOHOL · COMMERCE INFRASTRUCTURE

From two prototypes to one regulated commerce platform

A creator-driven storefront for premium spirits and a compliance-first commerce backend for alcohol brands. Both started as vibe-coded MVPs that worked in a demo but couldn't survive 50-state alcohol regulation, real payment volume, or supplier onboarding.

We rebuilt them into one production platform: state-by-state compliant order routing, Stripe-backed payouts to licensed retailers, an AI insights layer over brand performance data, and a field-sales CRM iOS app for on-premise reps. Live in market in 2026.

50 states
compliant routing surface
2 → 1
unified consumer + B2B platform
iOS
field-sales CRM shipped
SYSTEM ARCHITECTURE
Regulated commerce platform architecture Three consumer surfaces flow into a compliance engine and order router, which distributes to a retailer network, Stripe payouts, and logistics. AI insights operate as a cross-cutting layer. CONSUMER SURFACES Creator storefront creators · curation collections, follows Brand checkout embedded on websites supplier-direct sales Field-sales CRM (iOS) route, visit log, POs on-premise reps PLATFORM CORE Compliance engine, order router state-by-state regulatory rules · age + ID gate · licensed-retailer matching the regulated middle that turns clicks into compliant orders FULFILLMENT & PAYMENTS Retailer network order routing, status licensed in each state Stripe payouts splits, commissions automatic creator payouts Logistics + ID adult-signature delivery carrier integration AI INSIGHTS Brand performance SKU comparisons Campaign attribution Compliance review Lead generation Klaviyo · Meta · GA4 Audit log Drift detection enriches every layer PRODUCTION FUNDAMENTALS Versioned prompts · eval pipeline · cost tracking · access control · 99% uptime SLO Regulated commerce platform architecture Vertical view: three consumer surfaces feed into a compliance engine and order router, which distributes to a retailer network, Stripe payouts, and logistics. AI insights and production fundamentals operate as cross-cutting blocks. CONSUMER SURFACES Creator storefront creators, curation, collections Brand checkout embedded on supplier websites Field-sales CRM (iOS) routes, visits, POs for on-premise reps PLATFORM CORE Compliance engine, order router state-by-state rules · age + ID gate licensed-retailer matching FULFILLMENT & PAYMENTS Retailer network order routing, status, licensed in each state Stripe payouts splits, automatic creator commissions Logistics + ID adult-signature delivery, carrier integration AI INSIGHTS · ALL LAYERS Brand performance Lead generation SKU comparisons Klaviyo · Meta · GA4 Campaign attribution Audit log Compliance review Drift detection PRODUCTION FUNDAMENTALS Versioned prompts · eval pipeline cost tracking · access control · 99% SLO
WHAT WE SHIPPED
  • Compliance routing engine. State-by-state regulatory rules, age and ID gating, automatic licensed-retailer matching for every order.
  • Payments & payouts. Stripe-backed flow with split payouts to retailers and creator commissions, full audit trail.
  • Two consumer surfaces. A creator-curated storefront and embeddable checkout tools that drop into any supplier website.
  • Field-sales CRM. Native iOS app for on-premise reps: route planning, geo-tagged visit logs, purchase-order management.
  • AI insights layer. Performance, SKU, and campaign attribution unified across Klaviyo, Meta Ads, and GA4.

Client names available under NDA on request. More case studies coming as we ship.

Technical depth

The stack we ship on.

Models & orchestration

  • Claude · GPT · Gemini
  • LangChain4J · LlamaIndex
  • Multi-LLM routing
  • Structured outputs

Backend

  • Kotlin · Spring Boot
  • Python · FastAPI
  • SSE streaming
  • JWT + OAuth

Data & retrieval

  • Postgres · pgvector
  • Pinecone · Weaviate
  • Hybrid search
  • Embedding pipelines

Infra & ops

  • AWS · GCP · Azure
  • Docker · Kubernetes
  • Eval pipelines
  • Observability stack

Approach

How we work.

Most AI consultancies sell strategy decks. We ship code. Every engagement starts with a working system in week two and ends with your team owning what we built.

WEEK 1

Architecture review & failure-mode analysis

We read your code, talk to your team, identify what's actually breaking.

WEEK 2

First production-shape prototype

Working system with eval harness. Not slides.

WEEK 3-6

Iterate against production traffic

Streaming infrastructure, observability, evals tied to business metrics.

HANDOFF

Your team owns the system

Documentation, runbooks, and pairing until they don't need us.

Have an AI system that needs to actually work?

We respond within 24 hours. First call is a technical conversation, not a sales pitch.

hello@fydoro.com