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 Skills / Project Memory
A controlled AI coding workflow using AGENTS.md, ARCHITECTURE.md, project diary memory and focused skills to keep repeated work reliable.
Problem
Long AI coding sessions lose project context, repeat discoveries and mix durable project knowledge with transient chat noise.
Solution
Project memory is split by responsibility: AGENTS.md for rules, ARCHITECTURE.md for durable structure, diary for current state and focused skills for repeatable workflows.
Result
Future AI sessions can resume with less rediscovery while keeping permanent instructions compact and evidence-based.
Stack
Codex, AGENTS.md, ARCHITECTURE.md, project diary, Markdown articles, skill routing.
Business value
More repeatable engineering work for AI-assisted development, audits, maintenance and documentation-heavy systems.
What this proves
The studio does not only use AI tools; it builds operating methods for controlled AI engineering.