Capability Overview · AI Agent & Data Architecture · June 2026

Everyone is bolting AI onto your data.

We build the foundation that makes it

tell the truth.

dataeze.ai puts a team of AI analysts on your own data, answering any business question in plain language, in seconds, with every number traced to a query that actually ran.

The difference is the data architecture underneath, and it is why most AI on your data quietly fails.

Sound familiar?

4 data problems are capping your growth.

We see the same 4 in almost every business we walk into. Count how many are running in yours right now.

01✕

3 versions, same KPI.

Sales quotes one number, finance another, the dashboard a third. Meetings turn into debates about whose number is right. Confusion, not decisions.

✓dataeze: one governed metric layer. Every team quotes the same number, every time.

02✕

Analyst on leave. Decisions wait.

Every question queues behind 1 busy person. When they are out, decisions wait, or run on gut feel.

✓dataeze: an AI analyst that never takes leave. Any question, answered in seconds.

03✕

Looks fine on top.

Totals look healthy while one channel, SKU or region quietly bleeds underneath. The leak is always one cut deeper than the dashboard goes.

✓dataeze: every answer drills 10 cuts deep automatically. Leaks surface themselves.

04✕

Breaks Friday. You find it Tuesday.

A pipeline silently fails, reports keep rendering stale numbers, and by the time anyone notices, it is a fire, not a fix.

✓dataeze: monitored pipelines with alerts. You know the same day, not next week.

We built dataeze.ai to remove all 4. The rest of this deck shows exactly how.

The real reason AI disappoints

You cannot run a Tesla on a broken road.

Most "AI on your data" projects give wrong answers, so people decide AI is not ready for them. The AI is not the problem. The ground 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.

Who we are

20 years turning messy data into decisions.

dataeze.ai is founder-led, built on 20 years of enterprise data leadership at India's biggest Telecom, Media, FMCG and D2C companies. AI-first today: "Easy Data, Fast Decisions." Built by operators, not researchers.

The founder's 20 years · 2004 → today

AirtelTelecom Videocon TelecomTelecom Tata TeleservicesTelecom Siti CableMedia DB Corp · Dainik BhaskarMedia HT MediaMedia SC JohnsonFMCG LenskartD2C · AVP, Global Pricing & Growth OWNDAYSEyewear D2C Cars24Auto Marketplace

The foundation runs deep: he started in 2004, when sales reports were still totalled on calculators, long before the data ever touched a dashboard. 20 years later, that ground-up instinct for numbers is what dataeze is built on.

dataeze · the journey

2021 · Founded

Started as a hands-on data & BI analytics shop for Indian enterprises.

2022–23 · Semantic

Built our specialty: clean, governed semantic layers over raw operational data.

2024 · Scale

Production dashboards across Retail, FMCG, D2C & distribution. SQL-backed, traceable.

2025–26 · AI Agent

Launched the AI Analytics Agent, a team of AI analysts on your own data.

4

specialist AI agents: Analyst, Strategist, Architect & Insights Engine

~2 min

from raw data to a production-grade, decision-ready dashboard

100%

traceable: every answer maps to a verifiable SQL query

dataeze Agent
AI Agent · on your data

Q Which region underperformed last month, and why?

A

North is the only declining region, -14.2% MoM. Logistics-driven: a courier-mix shift added +3.1 days delivery TAT, pushing RTO to 19%.

Depth cuts 10 dimensions

Region	North	-14.2%
Channel	Quick-comm	-22%
Courier	Partner B	-31%
Zone	z_d (RoI)	+3.1d TAT
Payment	COD	19% RTO
SKU	Roll-on 50ml	-9%
Week	WoW	↓ 4 wks
Warehouse	Howrah	on track
TAT bucket	>5 days	+38%
RTO reason	Address	41%

Summary

The drop is logistics, not demand. North's delivery TAT and RTO trace to one courier on z_d pincodes; rebalancing to the backup courier recovers ~9% of lost revenue in ~2 weeks.

Last 5 actions

Parsed intent, entities & time frame

Resolved metrics on the governed semantic layer

Ran 3 validated queries on live data

Sliced 10 dimensions; verified totals & grain

Generated root cause + recommended action

What the AI agent actually does

Ask in plain English.
Get the number, the why, the next move.

1

It understands the business question

No SQL, no dashboard hunting. Anyone, founder to field ops, just asks.

2

It reasons, queries and verifies

Plans the question, reads the semantic layer, runs real SQL, checks the result before answering.

3

It explains and recommends

Not just the metric, the root cause and the next action, with every figure traceable to a query.

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.

Always on, 24/7 with you

We spot it, before you miss it.

Most analytics waits for you to ask. 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.

Under the hood

The anatomy of the agent.

Every question runs through the same disciplined loop, the reason it answers like your best analyst, not a chatbot guessing.

Because step 3 always resolves against the governed semantic layer, the SQL is correct by construction: the same metric means the same thing every time it is asked.

6 steps

plan → verify

0

guessed numbers

Self-check

on every answer

1

Understand

Parse intent, entities & time frame from the question.

✓

2

Plan

Decide which metrics, dimensions & filters are needed.

✓

3

Resolve via semantic layer

Map to governed definitions, the accuracy guarantee.

✓

4

Generate & run SQL

Compose validated SQL and execute on live data.

✓

5

Verify

Sanity-check totals, grain & nulls before responding.

✓

6

Explain & recommend

Answer + root cause + next action, fully traceable.

✓

The data architecture · what makes it reliable

Raw, scattered systems → one governed brain.

An AI agent is only as good as the layer beneath it. This is the pipeline we build before a single question is ever asked.

Orders & Revenue

ERP / WMS & Inventory

Supply chain & Logistics

CRM, CX & Voice

Ads, GA4 & Sheets

Consolidate & clean

Every source via API into one warehouse, de-duplicated, reconciled, standardized.

Our specialty

Semantic layer

One governed definition of every metric, the single source of truth.

AI agent + dashboards

Conversational agent & function-wise dashboards on top, trusted by every level.

Any source

API, DB, file, no rip-and-replace of existing systems

Near-real-time

intraday refresh with pipeline health monitoring

Governed

row/column security & full audit at the semantic layer

Why ours is accurate when others hallucinate

We don't drop AI on messy data.
We earn the accuracy first.

Most "AI on your data" tools hallucinate because the data underneath is dirty and the metrics are undefined. The semantic layer is the hard part everyone skips, and the reason our agent is trusted every morning.

One definition, everywhere

Revenue, margin, fill-rate, TAT, RTO, ROAS, defined once, identical from boardroom to last mile.

Every answer is traceable

Each number maps to a verifiable SQL query, nothing is made up, nothing is a black box.

It is your business, modelled

Your hierarchies, your channels, your edge cases, not a generic template.

AI dropped on raw data

✗ Confident, wrong numbers

✗ Each answer defines "revenue" differently

✗ No way to check where a number came from

dataeze · semantic-layer first

✓100% accurate, consistent answers

✓One trusted definition of every metric

✓Every figure traces back to real SQL

One architecture · every industry

The same engine. A different question on every desk.

The architecture is industry-agnostic; the semantic layer makes it industry-specific. A few of the questions our agent answers, by sector:

FMCG & Distribution

Which 40 outlets should each salesman visit today, and which scheme to pitch?

Primary/secondary sales · beat productivity · distributor stock

BFSI

Which branches are leaking deposits, and what is the early-warning on NPAs?

Portfolio · collections · cross-sell · risk

Retail

Which stores will stock-out this weekend on the top-20 SKUs?

Store funnel · sell-through · basket · replenishment

Healthcare & Pharma

Which territories under-prescribe versus their real potential?

MR productivity · Rx audit · stockist · demand

Supply Chain

Where is OTIF slipping this week, and what is the root cause?

OTIF · lead time · freight · procurement

Manufacturing

Which lines are dragging OEE, and where is yield being lost?

OEE · yield · downtime · quality · cost/unit

Technology & GCC

What is eroding margin across delivery accounts this quarter?

Utilization · project margin · attrition · bench

D2C & E-commerce

Why did contribution margin drop last week, across every channel?

RTO · dispatch SLA · ROAS · CX · cohort

Why dataeze · the honest comparison

AI did not fail. Bad data failed it.

Every "AI did not work for us" story has the same root cause: the AI was dropped on messy, ungoverned data. 3 paths teams take today; only 1 makes AI 100% right.

1 · In-house team + AI copilots

✗Crores a year for engineer + analyst + BI dev.

✗Copilots speed up code, never build a semantic layer.

✗6–12 months in, AI on messy data still guesses wrong.

✗The ceiling: a tool that guesses, not a trusted system.

2 · Off-the-shelf AI-BI tools

✗Land straight on top of messy, ungoverned data.

✗Same KPI, 3 numbers: the tool guesses which is right.

✗Great in the demo, never trusted in production.

✗The verdict: “AI failed.” Truth: the data failed.

dataeze

3 · Foundation first, then AI

✓We fix the data first: one governed semantic layer.

✓It compounds and locks in: your source of truth, not a tool to swap out.

✓We arrive with models built across FMCG, D2C and logistics.

✓100% traceable, on your own infra, at ~1/5th the cost.

~1/5th

the cost of an in-house data team

2–4 weeks

to go AI-first, not 6–12 months

100%

accuracy: every answer traced to a query that ran

The competitive picture

Same features. A fifth of the price.
The only one at 100% accuracy.

Our governed semantic layer and defined metrics make every answer 100% accurate and traceable, where AI on raw data guesses. We match the specialists on features, at a fifth of the cost, end-to-end on your own data.

Security & governance

Your data stays
on your infrastructure.

We build the entire system (data layer, semantic layer, dashboards and the AI agent) inside your own server / cloud project. dataeze stores none of your data.

Built & hosted in your environment

Your cloud project, your server. We deploy inside; we never copy data out.

Role-based access + full audit

Row- & column-level security via the semantic layer; every query logged and traceable.

Your own infrastructure

Your raw data: orders, inventory, supply, CX, marketing

Semantic layer: governed metrics

Dashboards: top floor to last mile

AI agent: runs locally, per function

Nothing crosses this boundary

✗ No data to dataeze cloud ✗ No third-party storage

Trusted by teams that ship

Already at work across industries.

Shark Tank India Phitku

D2C · Personal Care

D2C · Apparel

Fitness · Studios

Kokuyo Camlin · Stationery FMCG

and counting…

New brands onboarding across D2C, FMCG, retail and fitness, on the same architecture you have just seen.

And the enterprises where the founder led data & analytics transformations

AirtelVideocon Telecom

Tata Teleservices

Siti Cable

DB Corp · Dainik Bhaskar

HT Media

SC Johnson

Lenskart

OWNDAYS

Cars24

A clear, de-risked path

Live in 2 to 4 weeks. Value from week 3.

We build the foundation function by function, and hand you live dashboards for each one as we go, not one big-bang at the end.

Swipe the plan →

Workstream

Week 1

Week 2

Week 3

Week 4

Discovery & metricsYour data, questions & definitions

Understand

Sales & MarketingRevenue, channels, spend

Foundation

Go live

Delivery, Ops & FinanceLogistics, P&L, cash flow

Foundation

Go live

Understand Build the foundation Go live, on your own data

Where this goes next

An AI analyst on every desk.

Every quarter on messy data, AI keeps guessing and decisions run on gut. Going AI-first now is the moat.

The architecture is proven and the agent is live. The opportunity now is reach, putting an AI analyst on every desk across FMCG, BFSI, Retail, Healthcare, Supply Chain, Manufacturing and Technology. We would love to show you what it can do on your own data.

Book a 20-minute demo →

Pick a slot on dataeze.ai · 20 minutes, on your own data · go AI-first in 2–4 weeks.

Built & hosted on your infrastructure · Easy Data, Fast Decisions.