dataeze.aidataeze.aiAI Analytics Agent
Easy Data · Fast Decisions
Capability Overview · AI Agent & Data Architecture · June 2026
Everyone is bolting AI onto their data.
We build the foundation that makes it
tell the truth.

dataeze.ai puts a team of AI analysts on a company's 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: that is what this deck is about.

Who we are

Four years turning messy data into decisions.

dataeze.ai began as a data-analytics company. Today we are AI-first: "Easy Data, Fast Decisions." Built by operators, not researchers. Founder-led, with 20 years across Telecom, Banking, Media, FMCG and D2C.

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
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
RegionNorth-14.2%
ChannelQuick-comm-22%
CourierPartner B-31%
Zonez_d (RoI)+3.1d TAT
PaymentCOD19% RTO
SKURoll-on 50ml-9%
WeekWoW↓ 4 wks
WarehouseHowrahon track
TAT bucket>5 days+38%
RTO reasonAddress41%
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.

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
✓Near-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

Build a team, buy a tool, or own a foundation?

Hire an in-house data team
✗ Crores per year for engineer + analyst + BI dev.
✗ 6–12 months to the first reliable dashboard.
✗ Still no semantic layer, the hard part skipped.
Off-the-shelf BI / AI tools
✗ Connectors ≠ consolidation; reconciliation on you.
✗ AI on raw data hallucinates confident, wrong numbers.
✗ Generic views, not your functions or your metrics.
dataeze
Consolidate → Semantic → AI
✓Live in 6–8 weeks, proven on a production stack.
✓Semantic layer = trusted, traceable answers.
✓Runs on your own infrastructure: we store nothing.

You are not buying dashboards. You are buying a data foundation that scales with you and answers the moment you ask.

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.

and counting…

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

Where this goes next

An AI analyst on every desk.

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.

Email
hello@dataeze.ai
Call / WhatsApp
+91 99103 55559
Website
dataeze.ai

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

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