DEV Community: Nitish Kumar

I Built an AI Operating System for E-commerce (Because Dashboards are Dead)

Nitish Kumar — Thu, 18 Jun 2026 11:45:22 +0000

Most e-commerce founders spend 80% of their time managing fragmented software and only 20% actually growing their business.

We’ve all been there: You have Shopify for the store, Klaviyo for emails, Meta Ads for traffic, a spreadsheet for inventory, and 5 other SaaS dashboards. You spend hours context-switching, pulling data, and manually executing strategies.

The software meant to help us is actually slowing us down. Human attention has become the bottleneck.

That’s why I built ClarityCommerce — an Autonomous Commerce Operating System.

Instead of giving you another dashboard to stare at, you simply state your business objective (e.g., "Increase Q3 revenue by 20%" or "Launch a retention campaign for abandoned carts"). Our Multi-Agent AI Workforce then analyzes your live store data, identifies opportunities and risks, and autonomously executes the strategy across your connected platforms.

🛑 The Paradigm Shift: From Dashboards to Outcomes

Historically, businesses purchased software to perform specific functions (Accounting software, CRM software, etc.). Every new tool created a new layer of operational complexity.

ClarityCommerce introduces a fundamentally different paradigm: The platform is not organized around tools. It is organized around outcomes.

State your objective: Talk to the Clarity Executive AI like a co-founder.
Dual-Engine Analysis: The AI runs simultaneous Opportunity and Risk analyses using your Commerce Memory & Knowledge Graph.
Autonomous Execution: Specialist agents (SEO, Pricing, Inventory, Marketing) execute the approved mission directly on your store via the Model Context Protocol (MCP).

🏗️ Under the Hood: The Architecture

Building a production-grade, multi-agent autonomous system is incredibly complex. I didn't just build an LLM wrapper; I architected a polyglot, enterprise-grade operating system.

Here is the tech stack powering ClarityCommerce:

1. Polyglot Backend

Go API Gateway: Handles high-throughput routing, rate limiting, and SSE (Server-Sent Events) streaming for real-time UI updates.
Python/FastAPI AI Service: Handles the heavy AI lifting, LangGraph orchestration, and MCP tool execution.

2. Multi-Agent Orchestration (LangGraph)

Instead of a single LLM prompt, I built a hierarchical state machine using LangGraph.

The Executive AI receives your objective and breaks it down.
Department Agents (Marketing, Finance, Inventory) plan the strategy.
Specialist Agents execute the actual tasks. LangGraph provides durable, stateful, and resumable mission graphs, allowing us to implement complex human-in-the-loop governance gates.

3. Dual-Layer Memory System

AI is useless if it forgets your business context every time you refresh the page.

Commerce Memory (Supabase + pgvector): Stores episodic, semantic, and strategic context. We use RLS (Row-Level Security) to ensure strict multi-tenant data isolation.
Knowledge Graph (Neo4j): Maps the deep relational topology of your business (Missions → Markets → Strategies → Risks). When you ask a question, the AI queries the graph to ground its reasoning in your historical business topology.

4. Cross-Platform Execution (MCP)

To actually do things on your store, we use the Model Context Protocol (MCP). We built a Multi-Platform MCP Server that allows our Python agents to safely interact with Shopify, WooCommerce, Magento, Wix, and Squarespace APIs using a unified interface.

5. The Trust & Governance Layer

Autonomous AI is scary if it can accidentally set your product price to $0.00.

OPA Guardrails: Every tool call is evaluated against business policies (e.g., policy.price.max_drop_50pct) before execution.
The Governance Gate: High-risk actions require explicit human approval via the dashboard or email before the AI is allowed to execute them.
Audit Trails: Every OPA decision is logged to Postgres for full Explainable AI compliance.

🎨 The Frontend: Objective-Driven UI

The frontend is built with Next.js (App Router) and Tailwind CSS, featuring a custom "Cream & Bronze" glassmorphism design system.

Instead of a traditional sidebar with 50 menu items, the UI uses a Progressive Disclosure model. The main interface is the Command Center, where you interact with the Executive AI via a streaming, chat-like interface that visualizes the Dual-Engine thoughts in real-time.

🚀 What's Next?

We are officially live at claritycommerce.cc!

I built this for the solopreneur who wants to scale without hiring a 10-person agency, and for the enterprise operator who wants to unify their fragmented tech stack.

Try it out:

Connect your store (Shopify, WooCommerce, etc.).
Give the AI an objective.
Watch the Dual-Engine analyze your business and the Specialist Agents execute the mission.

I’d love to get feedback from the DEV community, especially on the multi-agent orchestration and the MCP implementation. If you have any questions about the architecture, drop them in the comments! AMA! 🙌

Links:

🌐 Live App: https://claritycommerce.cc/
📖 FAQ: https://claritycommerce.cc/faq

Why I'm building an AI commerce engine that shows its work (and how you can help)

Nitish Kumar — Fri, 29 May 2026 07:07:40 +0000

Hey devs 👋

I'm Nitish — a full-stack developer and AI automation engineer. For the past 7 years, I've built SaaS platforms, AI agents, and e-commerce integrations for startups and agencies.

And I kept seeing the same problem:

Most "AI commerce tools" aren't actually AI.

They're if-then scripts wrapped in a chat UI.

They drain budgets with unpredictable credits.

They hide decisions behind black boxes you can't audit.

So I decided to build the antidote.

Introducing ClarityCommerce — an AI commerce engine built on genuine reasoning, not rules.

The Tech Stack (so far):

Next.js 15 (App Router) + TypeScript
Multi-model AI router: DeepSeek V3.2 (primary), Gemini 2.5 Pro / Claude 4 Sonnet (fallback)
Cost-aware token routing: auto-downgrade to cheaper models when margin <70%
Zod + Server Actions + Resend for waitlist flow
Vercel Edge Functions + Cloudflare for global low-latency
Explainable AI Dashboard: every decision leaves a verifiable trail

Why this matters:

Sellers get transparency: see why the AI recommended a price change
Founders get predictability: flat-rate pricing, no credit anxiety
Devs get control: open architecture, clear API contracts, no vendor lock-in

I'm writing this because:

I want brutal feedback on the architecture before we scale
I'm looking for 5-10 technical co-builders for the MVP phase
I believe the future of AI commerce is explainable, compliant, and affordable — and I'd rather build it in public than in silence

If you're a developer, AI engineer, or technical founder:

What would you change about this stack?
Have you built explainable AI systems before? What worked?
Would you use a commerce AI that showed you its decision trail?

Drop your thoughts below 👇 I read every comment.

Want to follow the build or join the beta?
→ Waitlist + technical roadmap: https://claritycommerce.nitishkumar.pro/

→ My portfolio (past SaaS/AI work): https://www.nitishkumar.pro/

Built with transparency.

— Nitish

BuildInPublic #AI #Ecommerce #NextJS #TypeScript #LangChain #DevCommunity #IndieHackers #SaaS #OpenSource

Hey Dev.to! I'm Nitish, a Full-Stack & AI Engineer. I build scalable web apps and AI agents using React, Node, Python & LangChain. Excited to share code, AI workflows, and dev tips here. Portfolio: https://www.nitishkumar.pro/

Nitish Kumar — Thu, 28 May 2026 07:21:27 +0000

Nitish Kumar | Systems Engineering & Cloud Architect

Explore Nitish Kumar's technical capabilities constellation, systems architecture insights, and high-performance engineering projects. Specialize in robust cloud deployments.

nitishkumar.pro