For Abhishek Shah · Anchor Group & GreatWhite Electricals
You asked what
we have been building.
Here it is.
Not concepts. Systems that run in production tonight, on four real businesses, on their own infrastructure. Then what the same engine does for a new consumer brand, and for a dealer network the size of GreatWhite's.
650,000+ orders reconciled to source910,000+ shipments modelled to margin4 businesses live in productionEvery answer on your own infrastructure
4 short chapters, about 3 minutes. What we built → what it does → why it works → what it does for Anchor.
The direct answer
6 systems, all live, all on the client's own cloud.
None of these are pilots or prototypes. Each one refreshes overnight and feeds decisions people actually make the next morning.
D2C · Personal careLive
Multi-channel commerce warehouse
Shopify, Amazon, quick-commerce and B2B pulled into one warehouse, with revenue reconciled to source every night.
650,000+orders, tied to the rupee
D2C · ApparelLive
Logistics and margin at item grain
Every shipment modelled down to the line item, so gross margin is visible by courier, lane and SKU.
910,000+items modelled
FMCG · StationeryLive
Self-serve executive dashboard
An 8-page board dashboard running on their own semantic model. No analyst sits in the loop.
8 pagesno analyst needed
Services · Multi-siteLive
Three booking systems, one truth
Three unconnected systems unified, with row-level access so each trainer sees only their own classes.
3 sourcesunified, row-level secure
ProductLive
The AI analyst itself
Multi-tenant agent: ask by chat or voice, export to PDF, schedule to email, get alerted when something moves.
13 languageschat, voice and mobile
FoundationsLive
The unglamorous part underneath
Nightly pipelines, stored procedures, reconciliation guards and health monitoring. This is what makes the rest survive.
60 min → 3 minnightly refresh, rebuilt
See it for yourself
This is what it looks like in a real business.
56 seconds: a question at 9am, the answer, the reason, and the action, all on the company's own data.
Muted · tap for sound
Example 01 · Consumer brand, end to end
A D2C brand: 5 channels, one number.
A personal care brand that went viral after Shark Tank India. Orders arriving from 5 places, spend across 3 ad platforms, a warehouse, a courier aggregator and 2 call centres. None of it agreed with any of the others.
What we connected
•Shopify, Amazon, quick-commerce, marketplace and B2B orders
•Meta Ads, Google Ads and GA4, for real attribution
•The warehouse system, for dispatch, inventory and cancellations
•The courier aggregator, for shipping cost, SLA, RTO and returns
•Two voice platforms, for inbound and outbound customer calls
What it changed
✓Revenue reconciled to source, order by order, every night
✓A duplicate-revenue leak found and fixed in the first month
✓One channel was under-reported by nearly half, all of it silently credited to Organic. Fixed at the source
✓Units, orders and margin now agree across every report and every deck
650,000+ orders reconciled60 min → under 3 nightly refresh7am brief in the founder's inbox
The revenue number never moved. What moved was whether anyone believed it.
Example 02 · Distribution and margin
Top line looks fine. Margin leaks one lane at a time.
No standard report shows you profit per SKU per lane per partner. So the leak sits inside a healthy-looking revenue line for quarters.
D2C apparel · logistics
We moved the whole model from order grain to item grain.
Duplicate tracking numbers were quietly inflating every shipment metric they had. We rebuilt it at line-item grain, added a courier dimension and calculated true gross margin per item. Profit by courier, lane and SKU became visible for the first time.
910,000+items modelled
Per lanemargin visibility
Stationery FMCG · distribution
A distributor network, made self-serve for the leadership team.
An 8-page executive dashboard built on their own governed model: demand through to shelf, distributor-level movement, category and SKU performance. Leadership pulls it themselves, no analyst in the loop.
8 pagesself-serve
Demand → shelfend to end
A dealer network is the same shape. Sell-in versus sell-through, and margin per SKU per zone after freight and schemes.
Example 03 · The engine
Built like a product. Not like a demo.
A chat box over a database demos beautifully and dies on contact with a real business. This is the part that decides whether it survives, and all of it is already built and running.
Multi-tenant, on your infrastructure
Each business gets its own sealed environment inside its own cloud. Your data never leaves it.
Access control down to the row
A regional head sees their region, a partner sees their account. Enforced at the data layer, not hidden in the UI.
A second agent checks the first
Every headline number is independently re-verified by a second pass before it reaches you.
Traceable to the exact query
Every figure links back to the query that produced it. You can audit any answer. No black box.
Swap the AI model without a rebuild
The underlying model is a setting, not an architecture. Costs fall, you benefit, nothing gets rewritten.
It lives on your phone
Installable app, push alerts and voice input. The answer finds you, you do not have to go looking.
Capabilities
Not just answers. The whole workflow.
Everything the agent does around the question, all on your own live data.
Reports that come to you
Schedule any question to re-run on live data and land in your inbox and on Slack.
Just ask out loud
Tap the mic and speak your question. No typing, and it works on your phone.
Answers in your language
Ask in English, Hindi, Japanese and 10 more. The reply comes back the same way.
Bring your own files
Drop in an Excel, CSV or PDF, or paste a screenshot, and ask questions grounded in it.
Board-ready in one click
Turn any answer into a clean, branded PDF, ready for the meeting.
Role-based access
You decide exactly which data, and how much, each user or group sees. Right down to the row.
Every dashboard, live
Your existing Power BI reports, embedded in one place and always current.
Business context Coming soon
Connect Slack, email, WhatsApp and meeting notes so the analyst knows your world.
See the depth in action
Ask it anything. Get the depth, not just the number.
One agent, every function. The questions below are illustrative, shaped the way a manufacturer and a consumer brand actually ask them. Each one gets the number, the why and the exact action, in seconds.
Growth · Revenue
2.1s
QWhy did we miss the revenue plan this month?
Revenue
₹42.6Cr
▼ 9% vs plan
Volume
▼ 12%
▼ below plan
Realization
▲ 3%
▲ price held
WhyNot price. 3 of 12 regions drove 80% of the miss on volume, while realization actually improved.
DoRegional heads: rebuild the demand plan for those 3 regions this week. Recoverable this quarter: ₹3.8Cr.
Always on, 24/7 with you
We spot it, before you miss it.
dataeze works alongside you, around the clock. It watches every metric and flags the moment something moves, an opportunity to grab or a loss to stop, so you act while it still counts and keep the business growing.
Opportunity
Instagram ROAS jumped to 5.1x this morning. Push more budget while it lasts.
Live
Risk
Returns on one product are up 3x in 48 hours. Pull the batch before it scales.
Live
Anomaly
North region orders down 22% vs trend. A courier delay is the cause.
Live
Stock-out risk
Your top 3 products hit zero in 4 days at this rate. Raise the order today.
Live
Always on, working with you to grow the business, not just report on it.
The real reason AI disappoints
You cannot run a Tesla on a broken road.
Give a brilliant AI messy, ungoverned data and it guesses, then hands you a wrong answer. One wrong answer and no one trusts it again. The AI is not the problem. The road underneath it is.
≠ AI on messy data≠ Messy data✓ The road dataeze builds✓ dataeze road
Everyone is racing to run the Tesla. We build the road first, then let it fly.
Why this is hard to copy
Anyone can add AI. This is what they cannot copy.
Any capable team can wire up an AI. The moat is what builds up underneath it, and gets harder to copy every month you run.
It runs on your own infrastructure
Every pipeline, the semantic layer and the AI sit inside your environment. Your data never leaves. Most tools cannot offer this at all.
It compounds, and it locks in
The semantic layer gets richer every month. Once every team runs on one trusted definition, it becomes the source of truth, not a tool you can swap out.
We arrive with the models built
Metric models already proven across FMCG, D2C and logistics. Your build is faster because we are not starting from a blank page.
Traceable, and operator-built
Every number traces to the exact query that produced it, modelled by a 20-year operator who knows which metrics move a P&L.
The AI is the easy part. What compounds underneath it is the moat.
Under the hood
All your data, in one place you can trust.
The reason every answer can be trusted: it all runs on one clean, agreed version of your data, built once, up front, before a single question is asked.
ERP, sales & finance
Supply chain & WMS
CRM, marketing & web
Files, sheets & more
Bring it together
Every system pulled into one place, cleaned and matched up.
One source of truth
One source of truth
One agreed definition of every number, that everyone works from.
Ask anything
Ask in plain English; live dashboards for the board on top.
What this looks like for you
Two problems. One foundation.
A new consumer business being built, and an established electricals business at scale. They need opposite things, and the same foundation carries both.
Track A · Greenfield
The new consumer brand
Build it clean on day one, instead of retrofitting it at 100 crore.
✓Storefront, marketplaces, quick-commerce, ads and 3PL wired into one truth from launch day
✓Contribution margin per order, not just revenue: after ads, shipping, RTO and returns
✓Cohorts, repeat rate and true CAC by channel, with attribution that does not dump everything into Organic
✓Every number the board will ask for, available the week you launch
Track B · Scale
The electricals business
7 categories, thousands of SKUs, a dealer network in the thousands, 2 plants, exports.
✓Primary versus secondary: what you sold in, against what actually moved off the shelf
✓Dealer and territory sell-through, so a slowing dealer surfaces in days, not quarters
✓Margin per SKU per zone, after freight and scheme spend, not just list price
✓Dispatch SLA and fill rate by plant, domestic and export, on the same screen
4 to 6 weeks
to live on your own data
Value by week 3
first workstream shipped
About 1/3rd
of an in-house team's cost
Us vs the field
Everyone does a slice. We do the whole job.
AI copilots, AI-BI platforms and dashboards each cover a piece. Only dataeze does it end to end, on your own governed data.
dataeze
AI copilotsJulius, Vanna
AI-BI platformsThoughtSpot, Hex
BI dashboardsPower BI, Tableau
Runs on your own infrastructure
✓
✗
~
~
Fixes and governs the data first
✓
✗
✗
✗
Answers in plain English, in seconds
✓
✓
~
✗
Every answer traceable to a query
✓
✗
~
✓
Watches metrics 24/7, flags risks
✓
✗
✗
✗
Live in weeks, we run it with you
✓
~
✗
✗
Swipe the table →
Same features, or close, as the specialists. Only one does the whole job, on your own data.
Why you can trust this
20 years turning messy data into decisions.
This is not a lab experiment. It was built by an operator, not a researcher, on two decades of running data inside some of India's largest Telecom, Media, FMCG and consumer businesses, and it is already live in production today.
The foundation runs deep: he started in 2004, when sales reports were still totalled on calculators, long before the data ever touched a dashboard. That ground-up instinct for numbers, proven across telecom, media, FMCG and consumer, is what dataeze is built on.
Proof, on real businesses
Live in production. Already paying off.
Reconciled to the rupee
Numbers the board finally trusts
A D2C brand: every revenue number tied out to source across 650,000+ orders, with a costly double-count caught in the first month.
Days to seconds
Board questions, answered live
Questions that took an analyst days to pull are now answered in plain English, in seconds, by anyone who asks.
60+ min to under 3
Fresh data every morning
A nightly refresh rebuilt from over an hour to under three minutes, so every decision runs on today's numbers.
900,000+ shipments
Margin you could not see before
A logistics business, modelled end to end, with profit visible by courier, lane and SKU for the first time.
Shark Tank India
D2C · Personal Care
D2C · Apparel
Fitness · Studios
Kokuyo Camlin · Stationery FMCG
Live today across D2C, FMCG, retail and logistics. The same engine, ready for Anchor.
The invitation
Let us show you this on Anchor's own data.
Pick one question you cannot answer today, for the new brand or for GreatWhite. Give us one export to work from. In a short working session we will show you the number, the reason behind it and the action, live, on your own data.
A new brand only gets one chance to be built on clean data. After that, every fix is a migration.
