Built

Your D2C Marketing Agent

True attribution. Campaign intelligence. SEO gaps. Product intelligence. One AI platform

Shaped alongside with Brands  

Meta says 4x ROAS. Your Shopify revenue says otherwise. Who's right?


QuickInsights shows you the truth across every channel — and tells you exactly where to spend next.

How it Works?

Act

AI-prioritised actions

QuickInsights tells you what to pause, scale, or restock — with $$ impact estimates.

Pause

Scale

Restock

See

One dashboard, every channel

Shopify, Meta, Google, — unified in one view, updated daily.

Daily Pulse

Attribution

Performance

Ask Newton Anything

Ask me anything about your business

|

Ask Newton Anything

Ask me anything about your business

|

Ask

Instant answers in plain English

​Ask "Which campaign drove the most revenue last week?" — get an answer, not a spreadsheet.

Summaries

Insights

Many more

Getting Started

Up and running today

No data team. No BI setup. Just connect and go

Step 1

Connect

Shopify, Meta, Google, GSC— seamless integrations

Quickinsights

Your Channels

Step 2

Analyze

We analyse your revenue, ad spend and SKU data — and build your attribution model automatically

QUickinsights Analyzes everything

Attribution

Revenue mapping

Ad deep-dive

SKU level Insights

SEO Insights

Step 3

AI at work

Newton learns your business. AI models trained on your data, not generic benchmarks.

  • class AutomationTrigger:
    def __init__(self, threshold):
    self.threshold = threshold
    self.status = "inactive"

    def check_trigger(self, value):
    if value > self.threshold:
    self.status = "active"
    return "Automation triggered!"
    else:
    return "No action taken."
    def get_status(self):
    return f"Status: {self.status}"

  • class AutomationTrigger:
    def __init__(self, threshold):
    self.threshold = threshold
    self.status = "inactive"

    def check_trigger(self, value):
    if value > self.threshold:
    self.status = "active"
    return "Automation triggered!"
    else:
    return "No action taken."
    def get_status(self):
    return f"Status: {self.status}"

Step 4

Continuous Optimization

Newton gets smarter with every data point. Your insights improve as your business grows.

Newton AI

·Learning from your data

Attribution model

Updating

Demand signals

Up to date

Benefits

Revenue. Ads. Demand. Products. One AI platform

Attribution, campaigns, demand, products — one AI platform, zero spreadsheets

Which ad actually drove the sale?

Multi-touch attribution. True blended ROAS — not what Meta reports

Spot problems before they cost you

Creative fatigue, margin drops, anomalies all caught automatically

Find your next customer first

SEO gaps, branded search lift, demand all surfaced automatically

Know which SKUs to scale, which to cut

Stockout alerts, slow movers, SKU margin take action before it hits your P&L

Built for how India buys

COD ratios, RTO rates, festive seasonality. It is built in, not bolted on

Enterprise grade insights

Built for everyone in the team to access data

FAQs

We’ve Got the Answers You’re Looking For

How is this different from GA4 or Looker?

How long does setup take?

What channels do you support?

We already use Meta attribution. Why do we need this?

Do we need a data team?

Every rupee you spend deserves an answer. We give you one.

Book a Call Today and Start Optimizing