Built for e-commerce teams past the spreadsheet stage

Shopify, your WMS, and every courier — tracked in one control tower your whole team shares.

I pull your storefront, warehouse management system, and logistics aggregator data into a single platform — so Ops, Warehouse, CX, and Logistics stop chasing five different screens and start working off the same order, the same numbers, the same day. Deployed on AWS or self-hosted, built for teams who've outgrown spreadsheets and need this live before the next funding milestone, not next quarter.

See the control tower Book a Free Session

Uptime target: 99.9%
Tracking refresh: <2 min
Data pulls / day: Multiple
Deploy target: AWS or local

Services

What the control tower gives you

One cloud-hosted system that gives ops, warehouse, logistics, and CX teams a shared source of truth — deployed on AWS, built to scale.

Order Tracking

End-to-end visibility into every order, from storefront to last-mile delivery, so problems surface before customers ever notice.

Warehouse Control Towers

Real-time SLA and TAT monitoring across warehouses and fulfillment centers, with bottleneck detection built in.

Ops Workflow Tools

Assignment, escalation, and resolution tracking so every issue has a clear owner and nothing sits unresolved.

Why teams switch

Everything your ops team needs, in one tool

Built from real order-ops pain points, not a feature checklist — every capability below solves something that used to take a spreadsheet, a Slack thread, and a lot of manual checking.

See everything

One pane of glass

OMS, Shopify, and Shiprocket data unified in a single tool — no tab-switching to piece together what happened to an order.

See everything

TAT analytics

P50 / P90 / P95 turnaround-time percentiles per warehouse, not just averages that hide the long tail.

Move as one team

Role-based access control

Every team sees exactly what's relevant to them — nothing more, nothing less.

Move as one team

Automated mail escalation

Breached orders trigger escalation emails automatically, and replies show up in-tool without leaving the order.

Stay ahead of logistics

Courier partner performance

Delivery success rate, RTO rate, and turnaround time tracked per courier partner — see who's actually performing.

Stay ahead of logistics

Configurable alerting

Set the SLA breach threshold and alert recipients from the tool — tune sensitivity without touching code.

Built to scale, built to fit

API / webhook integrations

Increff, Unicommerce, Shopify, and Shiprocket connect via API or webhook — built to fit the stack you already run.

Built to scale, built to fit

AWS or self-hosted

Deploy fully managed on AWS, or self-hosted on Docker in a server you control — same tool, your call.

Every build above is a starting point, not the whole menu

This page shows one flagship control tower. The actual system gets scoped to your order volume, your OMS/courier stack, your team structure, and your rules — swap in the integrations you need, drop the ones you don't, and deploy it on AWS or your own server.

What actually changes

Where this moves your numbers

Not features for the sake of features — here's exactly what each one fixes for a multi-brand, multi-storefront operation.

One live view across every brand and storefront — nobody exports a thing.

Instead of: Ops manually combines five brand sheets every morning just to know what shipped.

Courier performance tracked per brand and per pincode, so a bad lane gets fixed before it costs you.

Instead of: A courier's NDR/RTO problem shows up in your P&L before anyone notices it in ops.

The right person gets an email, Slack, or WhatsApp ping the instant a breach threshold you define is crossed.

Instead of: SLA breaches get discovered when a customer complains on social media.

Production-to-dispatch visibility flags a stockout risk before it blocks an order.

Instead of: Production and warehouse teams find out about a stockout when the order fails.

Role-based views give every brand manager exactly their slice, live, without a rebuild.

Instead of: Each brand manager wants a different cut of the same data, so someone builds five reports.

Per-order margin tracked as it happens — not discovered a month late.

Instead of: You find out true landed cost (freight + COD fees + returns) at month-end reconciliation.

Under the hood

Deploy it your way — cloud-managed or self-hosted

The same application and data layer run either as a fully-managed AWS deployment or as a self-hosted Docker stack on infrastructure you already control. Neither is a compromise — pick based on your team's constraints, not the tool's.

Cloud-managed

Amazon Web Services

— fully managed, scales on its own

Swipe to see the full diagram

AWS Cloud · Region

VPC

Public subnet

ALB

EC2 (app)

Private subnet

RDS

Ingest

Lambda

EventBridge

Security & ops

IAM

CloudWatch

Alarms

KMS

Secrets Mgr

Request & data flow

1Ops team traffic hits an Application Load Balancer over HTTPS.
2The ALB routes to containerized EC2 instances running the Flask app.
3The app reads and writes order data in RDS (Postgres), encrypted at rest via KMS.
4EventBridge triggers a Lambda on a schedule to pull OMS, Shopify, and Shiprocket data into S3.
5Secrets Manager supplies DB and API credentials to EC2 and Lambda — nothing hardcoded.
6CloudWatch collects logs and metrics from EC2 and Lambda; Alarms fire on SLA breaches. IAM governs least-privilege access across every service.

Also pairs well with

Route 53 (DNS)CloudFront (CDN)ECS / Fargate (alt. to EC2)SNS (alerts)Terraform (IaC)

Self-hosted

Docker · Any Server

— on infrastructure you already own

Swipe to see the full diagram

Local / on-prem server

Docker Compose network

Nginx

Webapp

PgBouncer

Postgres

Airflow

Celery

Redis

Flower

Vendor APIs

Request & data flow

1Nginx terminates traffic and reverse-proxies to the Gunicorn-served Flask app.
2The app talks to Postgres through PgBouncer for connection pooling.
3Airflow's scheduler runs the ingestion DAGs multiple times a day, pulling from Increff, Shopify, and Shiprocket.
4Background jobs are queued through Redis and picked up by Celery workers; Flower gives visibility into queue health.
5The entire stack ships as one Docker Compose file — one command brings it online on any server you control.

Also pairs well with

Grafana + Prometheus (observability)MinIO (S3-compatible storage)GitHub Actions (CI/CD)Portainer (container UI)Watchtower (auto-updates)

Same modular design, different runtime

Every box above is a swappable module, not a hard dependency. Move to GCP (Cloud Run, Cloud SQL, Pub/Sub, Cloud Scheduler), Azure (App Service, Azure Database for PostgreSQL, Event Grid, Functions), or a Databricks-based pipeline — the application and data layer don't change, only where they run.

Inside the build

What the control tower looks like

This is the actual interface layout and information design of the tool — sidebar, navigation, tables, and status logic all match what ops teams use day to day. Every figure below is synthetic sample data, not a client's live numbers.

control-tower.internal

Sample data · read-only

Dashboard

Completed

Cancelled

Ready to Dispatch

Packed

Picking

Fulfillable

Unfulfillable

Warehouse Command Center

4 warehouses3 aging 3+ days6 open SLA breachesActive breach rate: 14.3%

Warehouse	Total	COMPLETED			CANCELLED		OPEN / ACTIVE			AGING				Health
Warehouse	Total	Count	Rate%	Breach	Count	Breach	Total	Pick	Pack	0d	1d	2d	3d+	Health
WH-Bengaluru	17	5	29.4%	0	1	1	9	2	1	5	1	1	2	Critical
WH-Bhiwandi	23	8	34.8%	2	3	3	10	3	5	8	1	1	0	Normal
WH-Gurugram	20	7	35%	0	1	0	8	3	2	5	2	1	0	Normal
WH-Hyderabad	30	9	30%	2	1	1	15	7	4	11	3	0	1	At Risk
ALL WAREHOUSES	90	29		4	6	5	42	15	12	29	7	3	3

OTF Rate

71%

Follow-ups Today

Orders with Errors

Escalations (7d)

Priority Summary

Critical

Normal

Order Age (Open)

3d+

Order Status Funnel

Picking

Packed

Pickup Pending

Completed OTF

Completed Breach

How the engagement works

Week by week, start to handover

~6-7 weeks, typical

Week 1
Discovery & scoping
Map order-to-fulfillment flow across every brand/storefront, audit data sources, agree success metrics.
Weeks 2-3
Data & pipeline
Connect OMS, WMS, and courier feeds; build the reconciliation and sync logic underneath.
Weeks 3-5
Build
Dashboards, order-detail views, and automated alerts wired to real data, not mockups.
Week 6
UAT & iteration
Your ops team runs real scenarios through it; edge cases get fixed before go-live.
Week 7
Handover
Training, documentation, and a clean handoff — plus an optional support retainer.

Proven in production

One flagship build, fully deployed

This is the real control tower this page is modeled on — designed and deployed for an actual multi-brand D2C operation. Client name withheld by request; everything below reflects what actually shipped.

Multi-brand D2C group · Shopify + third-party OMS

Warehouse & Order-Ops Control Tower

A cloud-hosted command center that replaced manual CSV firefighting with real-time SLA tracking, automated error routing, and full order visibility across every brand and warehouse.

SLA monitoring

P50 / P90 / P95

Courier refresh

< 2 min

Deploy target

AWS or Docker

A multi-brand D2C group running several Shopify storefronts, fulfilled through a third-party OMS, had outgrown its ops process. Every SLA breach was caught by hand — someone scanning CSV exports, hoping to catch a late order before a customer complained. When something went wrong, there was no record of who owned the fix, no way to escalate it, and no reliable way to tell if the storefront and the OMS even agreed on how many orders existed.

I designed and deployed a fully cloud-hosted control tower on AWS to fix this at the root. A containerized data pipeline pulls order data from the OMS multiple times a day, with API, email, and file-based fallbacks so a single integration hiccup never stalls the whole operation. A rules engine classifies every order error the moment it appears and routes it to the right team automatically. Ops, logistics, warehouse, and CX each get a role-scoped view of exactly what's theirs to fix, with built-in assignment, escalation, and remarks so nothing sits untouched.

Python / FlaskPostgreSQLApache AirflowRedis + CeleryDockerNginxAWS (EC2/ECS)

Get in touch

Still tracking orders in a spreadsheet?

Let's talk about what a control tower would look like for your operation — built around how your team actually works.

Book a free 15-minute session

No pitch — just 15 minutes to understand what you're trying to solve and whether a build like this actually fits. Share two times that work and I'll confirm one.

rohithsakkaravarthi@gmail.com +91 73389 13707 Download the catalogue (PDF)