SELECTED WORK / PROOF
Case studies from real AI engineering and product systems.
Production CRM, messaging runtime, AI-assisted development methodology and automation patterns.
Each case separates business problem, solution, result, stack and proof value.
No inflated metrics. Only work that can be explained through system shape and operational value.
Proof layer for aicoding.am
These are not generic blog posts. They are the commercial proof layer for how Sevada Yenokyan and aicoding.am approach AI automation, LLM systems, RAG workflows, CRM/ERP integrations and AI-assisted product development.
Narciss CRM
A production operating platform for flower retail: CRM, inventory, orders, delivery, POS, messaging and integrations in one business loop.
Problem
Flower retail operations were spread across customer communication, order intake, stock availability, bouquet assembly, delivery and external systems.
Solution
A Django-based operating platform connected customers, orders, inventory, recipes, delivery, POS surfaces, messaging channels and integration control planes.
Result
The business received one production system for daily operations instead of disconnected CRM, warehouse, messenger and order workflows.
Stack
Django, PostgreSQL, Redis, Celery, Docker, Nginx, integrations, messaging channels.
Business value
Operational control across sales, fulfillment, stock, delivery and customer communication.
What this proves
AI-assisted product development can produce a domain-specific production system when paired with engineering review and operational modeling.
AmoBit Inbox
A B2B inbox for operator workflows with workspace isolation, multi-channel conversations, protected media and a Django backend as source of truth.
Problem
Operators needed one controlled workspace for customer conversations across channels without exposing media or mixing workspace state.
Solution
A React workspace client and standalone Django API model workspaces, channels, contacts, threads, messages, attachments and protected media access.
Result
Messaging became a browser-first operational surface instead of a desktop wrapper or channel-specific tool.
Stack
React, Django, REST APIs, protected media endpoints, provider/profile channel model.
Business value
Cleaner operator workflows, safer media access and a stronger base for AI-assisted support and routing.
What this proves
AI-coded internal tools can be shaped into maintainable B2B systems when runtime boundaries are explicit.
Codex Session Lifecycle
A restart-safe session lifecycle for long-running Codex projects: 16 published skills, a live interactive atlas and an evidence-first handoff between dialogs.
Problem
A dialog can end while the underlying task remains active. A fresh session may lose decisions, repeat investigation, resume an obsolete plan or treat unverified work as complete.
Solution
The paired new-session and end-session orchestrators connect verification, plans, architecture, project diary, durable guidance and one compact Summarizations.md handoff through explicit gates.
Result
The system is publicly inspectable through a GitHub repository and a deployed dependency atlas that explains every phase, artifact contract and safety boundary.
Stack
Codex skills, Markdown contracts, Vite, React, Node.js tests, Nginx, systemd.
Business value
Faster restart of long-running work, less repeated discovery, cleaner durable memory and a clearer boundary between verified results and unfinished work.
What this proves
aicoding.am builds and publishes inspectable operating systems for controlled AI-assisted engineering, not only isolated prompts or demos.