Growth co-pilot for your D2C brand.
Plugs into your store, ads, and marketplaces. Reads the numbers every day — and tells you what's working, where you're leaking money, and what to do next.
The work that compounds your brand.
Growing a D2C brand means making decisions across many specialist areas every week — each one needing daily attention.
Daily ops is multi-disciplinary.
Running a growing D2C brand means making calls across many areas every week. Each area has its own data, its own cadence, its own specialists.
A full team for all of it is expensive.
Specialists across ads, SEO, conversion, pricing, retention, RTO, and marketplaces typically run ₹50k+/month each. For most growth-stage brands that's more than the annual salary budget allows.
What gets watched daily, gets fixed.
A losing campaign, a churning cohort, a lost buy-box — most of these are recoverable if caught in days, not weeks. The signal exists; the watcher often doesn't.
Experiments are where the upside lives.
Landing page tests, pricing changes, win-back flows — the compounding gains. Easy to deprioritise when daily operations need attention.
Plugs into your stack. Reads it every day.
Connected to Shopify, Meta, Google, marketplaces, and your chat tools. Day one — no new dashboards for your team to learn.
Always on.
Working while you sleep. Reports land at 8am IST.
Remembers your brand.
Every decision, experiment, and outcome stays. Gets sharper week over week.
Talks where your team talks.
Slack, WhatsApp, email. No new tool to learn.
Eight specialist areas. Every day.
Each area has its own daily analysis, alerts, and experiment proposals. Connected to your stack from day one.
Daily watch on Meta & Google. Flags fatigue, frequency drift, falling ROAS.
Tracks rankings, audits PDPs, writes the copy gaps it finds.
Watches store behaviour. Proposes landing tests, upsell flows, and quiz funnels.
Tracks margin, discounts, and competitor prices. Suggests floors and tests.
Watches cohorts after first purchase. Drafts win-back and lifecycle campaigns.
Flags risky pincodes, COD-skew buyers, and return-prone SKUs every week.
Monitors buy-box, prices, and listings across Flipkart, Myntra, Blinkit, Zepto.
Tracks competitor pricing, ad creative, and launches. Suggests counter-moves.
Every area analyses, reports, and proposes experiments. You decide what to run.
One real conversation.
It analyses, reports a finding, offers a hypothesis, and suggests an experiment.
Quick one — Meta CPC looks high this week. What's going on?
You're right — it's up 18%week-on-week (₹12.04 → ₹14.20).
It's almost entirely one campaign: “Festive Drop v3”. Its frequency just hit 4.7× (your threshold is 3×) and ROAS on it has dropped 22% in the same week. Other ad sets are holding steady.
Looks like classic creative fatigue (same ad seen too many times). My suggestion: pause v3 and swap in 2 new creatives for a 14-day test. If you want, I can outline what the two creatives should each test for.
Before that — what worked best last month? Group by hook type.
Last 30 days, sorted by ROAS:
Fit-led hooks — 2.4× avg
- “True to size, for every fit” — 3.1×
- “Model wears her usual size” — 2.6×
Drop-led hooks — 1.6× avg
- “New drop every Friday” — 2.0×
- “Festive capsule, 12 pieces” — 1.4×
Fit-led is doing about 50% better. Worth leading the 2 new tests with the “true to size” angle.
Makes sense. I'll set them up in Ads Manager myself — keep an eye once they're live.
Will do. Just name them fit-led-v1 and fit-led-v2so I can track them separately. I'll send a 7-day update once they're running.
Week 1 on the new ads:
- fit-led-v1 — 2.7× ROAS(we modelled 2.4×), CPC ₹11.60
- fit-led-v2 — 2.2× ROAS, CPC ₹13.10
- “Festive Drop v3” (paused) — spend redirected cleanly, no spillover
Overall paid ROAS up 18% this week. v1 is the clear winner.
A couple of things from beyond the ad numbers:
Buyers from v1 are repeating at 24% within 21 days(your store average is 16%). The “true to size” angle seems to be working.
Their RTO is running cleaner too — 11% on v1 vs 19% on the paused v3 last month.
Net, the unit economics on v1 are stronger than the headline ROAS suggests.
Suggestion for next week: scale v1's budget by 50%, hold v2 flat for one more week of data before you decide.
Nice. What's the margin per order looking like? Are v1 buyers pulling fewer discount codes too?
Yes — clean pattern. v1 buyers used a discount code on 23% of orders, vs 48% on v3 buyers last month. Margin per order is about ₹47 higher on average.
Between the discount restraint and the lower RTO, true contribution margin on v1 is closer to 2.2× (vs ~1.5× on v3 last month). There's more headroom for the 50% scale-up than the ROAS number alone suggests.
You see what the data shows, what it means, and what we propose to test.
Other options worth considering.
- Knows your storeConnected day 1
- Remembers decisionsFull memory of brand
- ScopeAds, SEO, CRO, pricing, retention, RTO, marketplace
Four steps. About two weeks.
We plug in, set things up, then run. The co-pilot gets sharper about your brand every week.
Connect your stack
Shopify, Meta, Google, marketplaces, Klaviyo, WATI. No new tools for your team.
We set things up
~2 weeks. We build your dashboards, alert thresholds, and analysis schedule.
It goes live
Reports and recommendations start landing in Slack, WhatsApp, or your inbox.
Ask, test, repeat
Ask for any analysis. Approve or reject experiments. It sharpens every week.
Three plans. Same team.
Higher tiers buy more analysis depth and more flexibility to ask.
All your data, in one place.
- Setup and integration with your stack
- Consolidated daily and weekly reports
- Smart alerts when key metrics breach thresholds
- Delivered on Slack, WhatsApp, or email
InsightsReports + recommendations.
- Everything in Reports
- Scheduled analysis across every area
- Hypothesis and experiment suggestions
- Weekly reviews — pricing, ads, retention, RTO, marketplace
Ask the co-pilot anything.
- Everything in Insights
- Ask for any analysis across any area, any time
- Priority turnaround on requests
- Custom dashboards and alerts on request
Questions D2C founders ask.
The things people bring up before they sign.
An AI co-pilot that we built, set up, and supervise. The two-week setup is hands-on by our team. After that, the co-pilot runs the daily analysis. Our team reviews anything that requires judgement before it reaches you.
See it on your store.
Thirty minutes. We'll connect your data and walk you through what the co-pilot would catch in week one.
Pick a time that works for you. We'll send a calendar invite with the meeting link.
Pick a time