Why your AI analyst lies when you point it at raw data
Point a chatbot at 14 disconnected systems and it will confidently give you 3 different revenue numbers, none of them right. The problem is not the model, it is the foundation underneath it.
An AI data team for growing brands
Growth runs on numbers.We make yours right.
Every growth call, from the next ₹10L of ad spend to the next PO, made on one number the whole business trusts. Built under your Shopify, Amazon, Meta and 3PL, with an AI analyst on every desk.
Live in 2 to 4 weeksReconciled to within 0.3% of sourceAnswers in seconds, SQL to verify
MARGIN LEAK · SKU-200 · LAST 7 DAYS
Finance5,84,000
Amazon5,12,000
Dashboard6,41,000
One number, on every desk
₹5,78,400±0.4% reconciled
This SKU looks like a hero on revenue, but .
DOCap COD on SKU-200 in tier-3 pincodes; push the 100 ml pack there instead.
View the SQL that ran
Contribution = net revenue minus COGS, fees, shipping and RTO cost, per SKU.
SELECT sku,
SUM(net_rev - cogs - fees - ship_cost - rto_cost) AS contribution
FROM sku_pnl
WHERE sku = 'SKU-200'
GROUP BY 1;Illustrative demo data. Your numbers, live, in 2 to 4 weeks.
Live in production for
Built by operators from
●The rounding error
Lose 5 orders today and the dashboard calls it 0.1%. To those 5 customers, it was 100%.
Averages are where growth quietly dies. Your reports round the misses away; we build the foundation that catches every single one.
▷See it work
Ask your data anything. Get one number, and the move.
Every answer is one governed number, the plain-English why, the exact action to take, and the SQL that produced it. This is the analyst on every desk.
Questions our clients ask every morning
Which channel makes the most margin after marketplace fees?Which pincodes should switch to prepaid-only?Where is RTO eating my north-zone COD?Is our best-selling SKU actually our most profitable?Which SKU stocks out before the next PO lands?Which ad set scales on ROAS but loses on contribution?How much cash is stuck in undelivered COD?Which courier is breaching SLA this week?Which channel makes the most margin after marketplace fees?Which pincodes should switch to prepaid-only?Where is RTO eating my north-zone COD?Is our best-selling SKU actually our most profitable?Which SKU stocks out before the next PO lands?Which ad set scales on ROAS but loses on contribution?How much cash is stuck in undelivered COD?Which courier is breaching SLA this week?
Why does Finance's revenue differ from the dashboard?Which store fell behind target this week, and why?Which 20% of SKUs carry 80% of margin?How many first orders repeated within 60 days?What did returns and RTO cost us last month?Which campaign sends sessions that never convert?Which warehouse should ship this order?What do I fix first to grow margin next month?Why does Finance's revenue differ from the dashboard?Which store fell behind target this week, and why?Which 20% of SKUs carry 80% of margin?How many first orders repeated within 60 days?What did returns and RTO cost us last month?Which campaign sends sessions that never convert?Which warehouse should ship this order?What do I fix first to grow margin next month?
Ask in plain English
Ask anything about your business
01The problem, and the fix
We are not another tool.
We are not another tool.
We are the end-to-end fix for the number nobody agrees on.the analyst bottleneck.the leak nobody sees.finding out too late.
Four walls every scaling brand hits. Your data is scattered across Shopify, Amazon, Meta and your 3PL, apps you do not own, and every one reports its own number. We take the whole chain end to end: unify the data, govern every metric, and put an AI analyst on every desk. You will recognise all four.
Wall 1 / 4Three numbers, one metric
3 dashboards, 3 different numbers
Shopify, Amazon and Meta each report a different figure for the same metric, and you own none of them. Bigger teams get the same split across sales, finance and the dashboard. The meeting ends in a debate, not a decision.
→dataeze: every source pulled into a warehouse you own, with one governed definition, so every screen shows the same number.
Wall 2 / 4One analyst, one queue
Your reporting has a bus factor of one
Every question routes through the one analyst who knows where the data is buried. They take leave, and the report never comes. The call either waits, or gets made on gut feel.
→dataeze: a team of AI analysts, always on. Ask in plain English, get the answer in seconds, with the SQL to verify.
Wall 3 / 4The leak under the topline
The real leak hides below the surface
The topline looks fine, and the detail is scattered across 6 vendor exports, so nobody slices deep enough. The leak, or the upside, is always one cut deeper.
→dataeze: the deep dive runs itself. Agents slice every cut and surface the leak or the opening you would have missed.
Wall 4 / 4Found out on Tuesday
It breaks Friday, you find out Tuesday
The number lives in a vendor dashboard nobody opens between reviews. You are always a week behind the problem.
→dataeze: a live alert the instant a metric breaks, on any device, so you act while it is still cheap to fix.
↗Why this compounds
Clean data does not just run the business. It raises the round.
Investors diligence the numbers long before they diligence the deck. A brand that can trace every metric back to the query that produced it answers diligence in days, and defends its valuation with evidence instead of narrative.
You own the warehouseYour data leaves the vendor dashboards and lands somewhere you control.
No analyst in the critical pathAnyone can ask a question and get a governed answer in seconds.
Every number is traceableDiligence ready by default, down to the query that produced it.
02The belief to flip
You bought the Tesla.
You bought the Tesla.
Nobody built the road.
Every AI pilot you have run put a brilliant driver on broken roads: 14 disconnected systems, 3 definitions of revenue, no map. It answered anyway, confidently. The model was never the problem. The road is.
dataeze builds the roads. The governed semantic layer under your stack is the reason the AI tells the truth.
<20%
accuracy of AI-generated SQL on raw, ungoverned schemas in industry benchmarks. Confident answers, wrong numbers.
vs
90%+
when every query compiles through a governed semantic layer. We build that layer first, on every engagement.
Sources: AtScale natural-language query benchmark; enterprise text-to-SQL accuracy studies, 2025-26.
Your stack, unchanged
ShopifyAmazonMeta AdsGoogle AdsGA43PL & couriersERP / TallyWhatsApp CX
We go underneath, not instead
dataeze · the governed data foundationwarehouse you own + semantic layer + AI analysts
Not another tool on the shelf. The foundation underneath all of them, built inside your infrastructure, live in 2 to 4 weeks.
◆Our vision
Built for years. Changed in days.
A world where no growing brand ever outgrows its own numbers. One set of definitions your whole team and your AI both trust, held by a model that knows your business better every week and reshapes in days when the business turns.
The longer we run your numbers, the more the model knows your business, and the sharper every decision you make.
01One definition, lockedEvery metric gets one owner-approved meaning, reconciled to source within 0.3% and traceable to the exact SQL that ran.
02Knows your businessWe learn your P&L, your SKUs and your December, so the model speaks your business, not a template's.
03Productized method, bespoke modelThe engine and the AI analyst are the same for everyone. The model built on top is yours alone.
04Moves at your speedA new metric or channel ships in days, a whole new source in weeks, never a scoping cycle in months.
05Deeper every weekEvery question teaches the model more about your business, so the answers sharpen and the value keeps compounding, all of it yours.
Why it's an easy yes
You keep growing. We keep every number right.
Live in 2 to 4 weeks, built on your own infrastructure so your data never leaves, and you own the entire system. Already in production for Phitku (Shark Tank India), The Pant Project, REDMAT and Kokuyo Camlin.
See it on your own data
03The approach
Raw, scattered systems in. One governed brain out.
This is how the road gets laid. Anyone can demo a chatbot on clean slides; almost no one can make it trustworthy on your live data. Here is why ours holds up.
STAGE 01
Consolidate & clean
Every source, via API, DB or file, into one warehouse: de-duplicated, reconciled, standardized. No rip-and-replace of what you already run.
Where it lives or dies
STAGE 02
Semantic layer
One governed definition of every metric. The single source of truth, and the hard part everyone skips. This is our specialty.
STAGE 03
AI agent + dashboards
A conversational agent and function-wise dashboards on top, trusted from the boardroom to the last mile, every answer traceable.
STEP 1
Understand
Parse intent, entities and time frame.
STEP 2
Plan
Decide the metrics, dimensions and filters.
STEP 3
Resolve
Map to the governed semantic layer.
STEP 4
Run SQL
Compose validated SQL, execute on live data.
STEP 5
Verify
Sanity-check totals, grain and nulls.
STEP 6
Explain
Answer, root cause, next action, traceable.
Your data stays on your infrastructure.
We build the entire system, the data layer, the semantic layer, the dashboards and the agent, inside your own server or cloud project. dataeze stores none of your data. Role-based access at the semantic layer, every query logged and traceable.
Nothing crosses this boundary
- Built & hosted in your environment
- Row & column security, full audit trail
- No data to dataeze cloud
- No third-party storage
04Built for consumer brands
Made for how D2C actually runs. A different question on every desk.
The foundation is the same everywhere; the semantic layer makes it yours. The questions the agent answers, and the kind of answer it gives back.
D2C & E-commerce
"Why did contribution margin drop last week, across every channel?"
e.g. "Amazon is 40% of revenue but 5% of margin after fees. Shift spend to own-site."
RTO · dispatch SLA · ROAS · CX · cohorts
FMCG & Distribution
"Which 40 outlets should each salesman visit today, and which scheme to pitch?"
e.g. "A scheme gave away ₹15L in margin without lifting volume. Kill it next cycle."
Primary/secondary sales · beat productivity · distributor stock
Retail
"Which stores will stock out this weekend on the top-20 SKUs?"
e.g. "₹4L of slow-moving stock sitting at 2 stores. Move it to the high-velocity branch."
Store funnel · sell-through · basket · replenishment
Supply Chain
"Where is OTIF slipping this week, and what is the root cause?"
e.g. "OTIF fell to 82% on one lane, driven by a single carrier's lead-time slip."
OTIF · lead time · freight · procurement
The same foundation also runs for BFSI, Healthcare & Pharma, Manufacturing and Tech / GCC teams. If that is you, let's talk.
05Proof, not promises
Real companies, live pipelines, numbers that hold up.
A sample of what we run in production today, across very different businesses.
D2C · Shark Tank IndiaLive in production
Phitku
Personal care · Shopify, Amazon, Blinkit, B2B · we are the analytics team
One foundation. Every desk gets the same truth, turned into their next move.
±0.3%
revenue vs Shopify, reconciled nightly, so the board number is never in doubt
Every source, Shopify · Amazon · Blinkit · B2B, self-healed nightly and reconciled into one number then handed to each desk as an action:
Founder
The whole business as one story
Where growth and margin really come from, e.g. own-site out-earns Amazon after fees, so move the spend, with the number that proves it.
Ops
What to action first, right now
The exact orders and lanes at risk, e.g. 340 north-zone orders breaching SLA, re-route these before the cut-off.
Manufacturing
What to produce this week
Demand turned into a build plan, e.g. hold 3 weeks of cover on the fast movers, no stockout, no dead stock.
CX
Which pains to fix first
The few issues that drive most tickets, e.g. fix RTO comms first, it recovers the most revenue per hour spent.
Board
Board-grade numbers, and the road to 3x
Revenue reconciled to the rupee, and the levers that get there: repeat rate, own-site mix and RTO recovery, tracked every month.
Wellness · Multi-studioLive in production
Pilates studios · Punchpass, Fresha, Zoho
Trainers act on at-risk clients, live.
- Punchpass, Fresha and Zoho unified into one warehouse
- Power BI dashboards plus a Buddy trainer panel for daily ops
- Self-healing daily pipeline with a watchdog, fresh on its own
3 into 1three disconnected tools unified into one live warehouse
FMCG · StationeryIn delivery
Kokuyo Camlin · demand-to-shelf forecasting
Demand-to-Shelf clarity from raw sales data.
- Self-serve dashboard suite over one governed model
- AI BI agent answers plain-English questions, SQL-traceable
- Demand forecasting and allocation from sales and distributor data
Demand to shelfAI forecasting and allocation, live over one governed model
D2C · ApparelLive foundation
Made-to-measure · Fynd + Shopify
True store-level attribution, with no shared key.
- Data warehouse rebuilt on a clean BigQuery star schema
- Fynd and Shopify bridged despite no shared identifier
- A silent shipment-feed outage caught and root-caused in days, not quarters
Store-leveltrue attribution across Fynd and Shopify, no shared key needed
See it on your data, not our slides.
We prep a live teardown of your stack before the call. Name, work email, and a time, that is all.
06The pedigree
Operating experience. Quantified.
dataeze is 22 years inside the engine rooms of Indian business, from the sales floor to the boardroom, now productized.
22 yrs
operating, 2004 to today, from the sales floor to the boardroom
₹180 Cr/yr
pricing-led revenue impact delivered at Lenskart
1 IPO
Lenskart 2019 to 2025, inside the building on the way up
6
industries, B2B and B2C: Telecom, Media, FMCG, Retail, Auto, D2C
3
regions run hands-on: India, Japan, Southeast Asia
2,500+
stores put on automated replenishment, ₹52 Cr in unlocked upsell
07Who you work with
You get the operator, not an account manager.
dataeze is founder-led, with a senior network activated per engagement. The people who scope your problem are the people who build it. No pyramid of juniors learning on your budget.
22 years inside the engine rooms of Indian business.
Airtel's regional sales floors. Strategy desks at Dainik Bhaskar and HT Media. Trade and distributor data at SC Johnson. Then 6 years at Lenskart, Head of Analytics to AVP Global Pricing & Growth, on the road to its IPO, running Owndays analytics across Japan and Southeast Asia along the way, and AI-first analytics at Cars24 after that.
dataeze is that experience, productized.
Connect on LinkedIn2004Bharti Airtel, then Videocon and Tata Tele: the telecom years, where reporting first met real scale.
2013Dainik Bhaskar & HT Media: business planning and corporate strategy inside India's biggest media houses.
2017SC Johnson: FMCG analytics, distributor and trade data.
2019Lenskart: Head of Analytics to AVP Global Pricing & Growth, through the IPO, with Owndays analytics across Japan and SEA.
2025Cars24: AI-first analytics infrastructure.
2026dataeze: everything above, productized for everyone else.
08Insights
Field notes from the engine room.
How to make your data tell the truth, then put it to work on every desk. Written from real production, not theory.
The semantic layer is the real AI investment for D2C brands
Your reports disagree because revenue, orders and CAC live in a dozen tools that never met. A governed semantic layer writes every definition down once, so an AI analyst and your whole team reason from the same truth.
One governed number the whole team actually trusts
When finance, growth and ops each pull their own version of last month's revenue, every meeting starts by arguing about whose number is right. One governed number ends that, backed by the exact SQL that produced it.
An AI analyst beats another dashboard, and here is why
A dashboard answers the 20 questions someone anticipated 6 months ago and stays silent on the one you have right now. An AI analyst lets anyone ask in plain English and returns a traceable answer with the exact action.
The 4 walls that keep consumer brands from acting on their data
Most brands do not lack data, they lack the decision. Four walls stand in the way, and naming them is the first step to tearing them down.
Live alerts are the point, reports are just the receipt
By the time a monthly report tells you a SKU went out of stock or CAC spiked, the damage is already booked. Live alerts close the loop the moment something moves, while you can still act on it.
Want these applied to your data? Book a 20-min working session →The full argument, in our capability deck →
◆Ways to start
Start where it hurts. Grow into the rest.
Three ways to work with us. Every one starts from the same foundation: a warehouse you own and one governed definition of every number.
Path 01
Foundation
Your numbers, reconciled.
Every source wired into a warehouse you own, a semantic layer that defines each metric once, and dashboards on the surface your team already uses. Live in 2 to 4 weeks.
- Warehouse and nightly pipelines
- Semantic layer, reconciled to source
- Power BI or web dashboards
How most clients run
Path 02
Foundation + AI analysts
One number on every desk, and the move.
Everything in Path 01, plus the plain-English analyst, an alert the moment a metric breaks, and a senior team running and reconciling the pipeline every night.
- AI analyst on every desk, SQL to verify
- Alerts on breaks, on any device
- Run and reconciled nightly by us
Path 03
Build on the foundation
The tool your business is missing.
Custom panels, agents and workflows on top of the governed model: a live floor panel for retail, an inventory planner for the warehouse, a WhatsApp analyst for the founder.
- Live intelligence panels
- Inventory and replenishment planners
- WhatsApp and voice analysts
Priced per engagement and quoted on the call. Before you pay anything, we name the first 3 things we would fix on your own data.
09Questions
Straight answers, before you ask.
What is dataeze?
dataeze (dataeze.ai) is an AI-first data and analytics firm for growing consumer brands. We rebuild your data foundation so AI can tell the truth about your business, then put a team of AI analysts on every desk, so anyone can ask a question in plain English and get a traceable answer with the exact action to take.
Who is dataeze for?
Founders and operators at scaling direct-to-consumer and SME consumer brands across D2C, FMCG and wellness, who are drowning in disconnected data across Shopify, Amazon, ads and spreadsheets.
How is dataeze different from a dashboard or BI tool?
A dashboard shows charts and waits. dataeze governs one trusted number, answers questions in plain English in seconds, and traces every answer back to the exact SQL query that ran. No black box, no analyst bottleneck.
Is my data safe, and where does it run?
Everything runs on your own infrastructure. dataeze builds the data layer, semantic layer, dashboards and the AI agent inside your own server or cloud, with role-based access and every query logged. dataeze stores none of your data.
How fast can we go live?
Most brands are live in 2 to 4 weeks, not the 6 to 12 months a data-team build takes, because the foundation and the AI analysts are productized.
Who built dataeze?
Operators, not researchers, with 20-plus years of enterprise data experience across companies like Lenskart, Cars24, Owndays, SC Johnson and Airtel, now productized for growing consumer brands.
Why not just point ChatGPT or Claude at our data?
You can, and the answer will be confident and often wrong. On raw, ungoverned schemas, text-to-SQL accuracy sits below 20% in industry benchmarks, because nobody has told the model what revenue, margin or an order actually means in your business. The work is the layer underneath: pull every source, clean it, reconcile it, define every metric once. dataeze builds that layer and runs it every night, so the AI answers from one governed number and every answer carries the SQL to verify it.
We already have Power BI and an analyst. Do we still need dataeze?
Keep both. We build the warehouse and the semantic layer under them, so your dashboards and the AI read the same definitions instead of each report recomputing its own. Power BI is one of the surfaces we ship on today. Your analyst stops reconciling exports every Monday and starts answering the questions that move the business.
Will connecting our Shopify, Meta or Google Ads accounts put them at risk?
No. We read through each platform's official API with read-only reporting scopes. We never create campaigns, change budgets, edit listings or touch orders. Access is limited to the metrics we report on, credentials live in the environment the system runs in, which you own, and you can revoke any source at any time.
10Let's talk
Go AI-first in 2 to 4 weeks.
dataeze is not another tool. It's an end-to-end solution that puts an AI-first analyst on every desk, budget-friendly, with ROI in multiples. Pick a 20-minute slot and we will show you the first 3 things we would fix to get you to one number your whole team trusts.
Week 1Connect & auditEvery source wired into a warehouse you own. The first reconciliation gaps surface here.
Weeks 2-3The semantic layerEvery metric defined once, governed, reconciled against your source of truth.
Week 4Live on every deskThe AI analyst, dashboards and alerts, in production on your infrastructure.
Already in production for Phitku, the Shark Tank India personal-care brand, plus REDMAT Pilates, Kokuyo Camlin and The Pant Project.
Book your 20-minute teardown
Just your name, work email, and a time. We prep a live teardown of your business before the call.
Prefer to reach out directly? Email hello@dataeze.ai · WhatsApp +91 99103 55559