[01] AUTONOMOUS INSIGHTS

The insights must find you. Not the other way around.

Oceans of data. Thousands of metrics. Intricate dependencies between them. Domain knowledge distributed across systems and your teams’ heads.

DataGenie fuses deterministic big-data analytics with AI to deliver Actionable, Autonomous Insights written in your domain language — pointing your business leaders straight to what deserves their attention.

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ISO 27001:2022

HIPAA

GDPR Runs in your VPC

Watch it work ↓

RETAIL · THIS MORNING SWEEPING…

100,000+ metric × dimension combinations, scored on every read against a band learned from your own history.

[02] THE PRODUCT REAL SCREENS · IT RUNS ITSELF

The product itself. At work.

Briefs — written findings that arrive on their own schedule — turn up without being asked. Wisdom answers the follow-up in plain English. The Knowledge Center holds the business knowledge both of them reason from. Click anywhere in the frame to pause.

PICK YOUR INDUSTRY — everything below is tailored to it

Good morning, Alex

Set the questions you care about once. DataGenie keeps watching, and the briefings come to you newest first.

Retail · 4 packs running

Your briefings

↻ RefreshCustomize

← Your briefings Merchandising & Margin · daily

Markdown broke margin on the latest day

2026-06-30 · Merchandising & Margin · daily · marketplace-wide2026-08-24

The single finding

Contribution margin per order fell to 12.68 vs 18.57↓ even as fully loaded margin rate dropped to 4.54 vs 9.33↓, discount depth jumped to 8.21 vs 4.00↑, and order volume still rose to 680 vs 481.55↑.

So what? This was not a demand miss. The business sold more orders at higher basket values, but it gave away too much margin through discounting, higher goods cost, and fee-heavy tender mix, with the sharpest pressure sitting in electronics and long-tenor BNPL.

CM per Order 12.68 vs 18.57↓ −31.7% Primary outcome KPI · latest day vs forecast

CM Pct of GMV 4.54 vs 9.33↓ −51.3% Fully loaded margin rate · critical miss

Discount Rate Pct 8.21 vs 4.00↑ +105.3% Up is bad here · giveaway depth doubled

Cost of Goods 60.84K vs 25.33K↑ +140.2% Cost lines rose much faster than order growth

Margin broke on 2026-06-30

The seven prior days stayed in a healthy band; the break arrived entirely in the latest bucket.

06-2306-30 · latest day

Actual CM per Order Forecast

What changed in the margin formula

Orders and basket value both rose — the cost stack rose much faster.

Outcome and controls

The miss is margin quality, not demand

MetricActExpDev

CM per Order12.6818.57−31.7%

CM Pct of GMV4.549.33−51.3%

Gross Margin Pct11.1716.96−34.1%

Order Count680481+41.2%

Average Order Value279199+40.3%

Additive cost lines

Built by discounts, goods cost and fees

MetricActExpDev

Discount Amount15.6K3.8K+306.7%

Cost of Goods60.8K25.3K+140.2%

Payment Fee7.0K2.8K+148.9%

Discount Rate Pct8.214.00+105.3%

Net Revenue97.7K46.1K+111.8%

Root cause — electronics and BNPL

Categories explain where the economics broke; payment method explains why fee pressure intensified.

BNPL_12_MonthCRITICAL payment method · worst tender-level break −272.3%

Mobile PhonesMAJOR category · CM turned negative −118.4%

LaptopsMAJOR category · deepest large-category miss −113.6%

TV_AudioMAJOR category · near-zero unit economics −96.1%

The worst cells are electronics intersecting long-tenor BNPL — Laptops × BNPL_12_Month at −554.8%, Mobile Phones × BNPL_12_Month at −720.4%.

Where it showed up

Texas, California and Florida carried the clearest state-level discount and margin anomalies, with several supporting states adding cost pressure.

Primary state break Strong secondary Secondary cost pressure Smaller supporting anomaly No material anomaly

TX criticalCM per Order −182.1% −15.04

FL criticalCM per Order −167.2% −12.18

CA criticalCM per Order −107.4% −1.33

GA supportingCost of Goods +171.9% +3.71K

OH supportingPayment Fee +139.9% +312

Connected demand context

Demand was strong; the economics were the failure. Same pattern across every category: strong sell-through, extreme markdowns, then bad contribution margin.

CategoryDemand MarkdownEconomicsRead

Mobile Phoneslargest connected demand case 76+275.2% 198.6K+1370.4% −7.89major Demand arrived, but markdowns and payment fees destroyed margin.

Laptopscleanest all-stage surge 41+289.0% 73.3K+1147.7% −7.68major High-ticket demand converted, but unit economics collapsed.

TV_Audiomost extreme markdown surge 43+183.7% 176.9K+1882.9% 1.51−96.1% The category still sold, but price was given away too aggressively.

Marketplace-widewhole business 3,817+40.8% 99.19%+0.2% 17.82%+0.3% Rules out weak demand and stock-out explanations.

Nobody had to ask for it read in place every step recorded

NotebooksMemory

Ask Wisdom, know Everything

What would you like to know about your data?

Select dataset ⌄ Skills, Data & MCP

Summarise this quarter What changed this week? Top movers by revenue

Notebooks in Retail

Margin by category · markdown 3 cells · today

← Notebooks margin_by_category

Books is the outlier weakness at $0.32 CM per Order. In this dataset a negative CM per Order would be a genuine breach; Books is just barely above zero, so it is the first category I’d scrutinise.

CM per Order · by category · this quarter

Furniture $21.40

Kitchenware $18.02

Home_Textile $16.11

Electronics $9.94

Books $0.32

This was a same-day, cross-category markdown shock, not a slow drift. All three owned categories posted a major anomaly on May 20 against their expected CM per Order.

CM per Order breach on May 20

Furniture −$2.75

Home_Textile $3.27

Kitchenware $1.43

Furniture was the worst break, dropping below zero, while Home_Textile and Kitchenware held above it.

It is bleeding. The worst-cost states do not sit at higher conversion — they cluster far lower on fulfilment margin without a compensating uplift in conversion.

Conversion does not offset long-haul fulfilment drag

Fulfilment margin / order 16.2%Conversion rate19.0%

15 states plotted · the three worst sit bottom-left, not top-right.

3 questions every step recorded same steps on every run

Knowledge Center

The business knowledge and reasoning Wisdom draws on so every answer stays accurate, explainable, and grounded in how your business actually works.

Skills 8 Business Events 5 Domain Knowledge 15 Documents Memories 2 MCP

Skills

Captures reusable reasoning for recurring business situations.

+ Add new Cognitive Skill

Showing 4 of 8 · Retail

Availability Break Triage

5 steps · ships with the pack
Inventory & Availability Guardian

Markdown Cannibalisation Check

5 steps · ships with the pack
Margin & Markdown Sentinel

Checkout Drop-off Trace

4 steps · ships with the pack
Conversion & Checkout Funnel Radar

Loyalty Lapse Early Read

4 steps · ships with the pack
Retention & Loyalty Early-Warning

Tour running. Hover or click to pause

Trusted by

GlobalFortune 500Risk analytics MongoDB

THE THREE POWERS Finds it. Finds all of it. And the same way twice.

[03] AUTONOMOUS POWER 1 OF 3

Findings arrive. Nobody goes looking.

Other tools wait for somebody to ask the right question. DataGenie catches what broke, works out why, and puts it in front of whoever owns the number — before anyone thought to look.

How it gets found today arrived today · 9

Four Slack threads. Nine emails. Three days of somebody's week — and fd break rate was never the question anyone asked.

With DataGenie

DataGenie brief · daily · autonomous 06:12 · before anyone asked

Suspicious FD breaks · Branch 07

Affluent-uninsured · behaviour → money → book

This reads as suspicious money movement, not pricing or service. Premature-broken FD jumped to $306.8K against $31.4K expected, with five ticketless breaks where none were expected. CASA net flow fell to −$170.6K as outflow hit $476.7K — and $176.0K of that went to first-time payees.

FD PREMATURE BROKEN $306.8K ~~vs ~$31K normal~~

FD BREAK RATE 75.9% ~~vs 26.0% exp~~

NET CASA FLOW −$170.6K ~~vs +$20.6K exp~~

Joined

FD lifecycle (term deposits) Demand deposits (CASA) Customer & channel engagement Payment rails & destinations

One card, routed to the desk that owns the number. Nobody had to ask for it.

[04] AT SCALE POWER 2 OF 3

Ditch the binoculars. Employ the satellite.

Binoculars see where you point them. DataGenie asks nothing — it sweeps every KPI against every dimension and speaks only about the ones that broke.

Slicing it by hand

Deposits · Explore combination 1 of 100,000+

Measures

Dimensions

Rows

Filters

elapsed 00:00 nothing out of band yet

Think it up. Type it. Run it. Read that nothing moved. Then do it again. A morning goes this way, and the one that broke is the one nobody got to.

What DataGenie does with the same morning

0 combinations swept before anyone sat down 0 spoke. The rest stayed quiet.

Reading every combination against its band…

It swept these. Nobody had to choose them

6 KPIs

FD Break RateNet CASA Flow Balance-weighted Outflow Outflow-to-Yield Share First-Payee Ratio Off-Hours Activity

5 dimensions

branchcohort raildestination payee-status

Six KPIs by five dimensions, swept on every read. DataGenie came with all of it.

[05] DETERMINISTIC POWER 3 OF 3

Ask it twice. The number doesn't move.

Point a model at raw tables and it writes a new query every time — different join, different answer. DataGenie recognises the question and runs the one query it already has, over numbers it added up last night. Same query, same number.

A model writing its own SQL

?Of last week’s deposit outflow, how much went to first-time payees — by branch, against the same week last quarter?

waiting Run01

—

Same question, a new query every time. Which one do you show the board?

The same question, to DataGenie

?What moved FD Premature Broken last week, and which branch is behind it?

It already knows every part of that

FD Premature Broken a metric it tracks · defined once, at onboarding

branch one of the 5 dimensions it watches

last week 7 daily points · added up last night

So it runs the query it haswritten once

fd_premature_broken BY branch × cohort · daily · vs band

read from pre-aggregated metrics Ask01

$306.8K

Recognised, not written. Read, not computed. The number can’t move.

Ask it twice on your own data. Thirty minutes, the same number both times, and the working attached. There’s a person on the other side of this button.

Book a demo

[06] VALUE PACKS 18 PACKS · 5 INDUSTRIES

It already knows your business, switch it on!

A Value Pack already understands one problem the way someone who has worked it for years does — what usually goes wrong, which numbers give it away, and what separates a real cause from a coincidence. The KPIs, the dimensions and the rules come with it. Switch one on and it watches 100,000+ metric × dimension combinations, continuously.

Every pack, in detail →

[07] BRIEFS

What changed, why, and what it rules out.

A written finding, on a schedule, sent to whoever owns the number. Every figure traces to its source, and the narrative leads with what the data eliminates — not just what moved. These are the briefs the pack you picked above actually writes.

Briefs — Watching

Written on schedule · — · nobody had to ask

Every figure traces to its source How Briefs work →

[08] WISDOM SAME QUESTION · SAME ANSWER

The packs watch your data. Wisdom talks to you.

Ask it anything about the business, in plain English. It works out which analysis answers that, runs it across your own data, and hands back the finding with every step it took.

Northeast Availability & Loyalty Exposure

ALWhich of our stockouts are hitting loyalty members hardest — not just the ones losing the most?

Done — joined each store's stockout lost-sales with the share of that lost demand coming from high-value loyalty members, and ranked the relationship exposure, not the dollar loss.

In a nutshellTwo Northeast-metro stores carry far more loyalty exposure than raw lost sales suggests. NE-114 loses $86K this week but 41% of its basket comes from members, against a 23% chain average. Across the region, 9 of 34 stockout stores sit above 35% member share, and those nine hold $412K of the $1.4M exposed — 29% of the total on 26% of the stores.

% of lost demand from loyalty members Weekly lost sales ($K)

Two more cells in this thread View full notebook →

And that was one cell

Wisdom is not only the conversation. It forecasts, plans and prescribes.

The same coordinator that answered that question projects the metric forward, simulates the change you are considering against every dependent KPI, and says what to do next — off the same governed numbers, with the working attached.

Forecasting Scenario planning · what-if Prescriptive next best actions Contribution analysis Correlation & attribution Conversational dashboards Real-time, hourly Next-period projection Alerts to Slack, Teams, Jira

See how Wisdom works

Get a brief from your own data. Thirty minutes, a real brief at the end of it, and the follow-up answered. There’s a person on the other side of this button.

Book a demo

[09] ENTERPRISE ARCHITECTURE FIT READS · COMPUTES · DELIVERS

Where DataGenie fits in what you already own

Your cloud · your private subnet

Your data

Read where it lives

25+ native connectors

Your context

The part your BI tools never read

DocuFeeder

Your SOPs, rulebooks, process docs, playbooks

Context MCP

Live operational context

Business Events

Plant fire

Strike

Promotion

Competitor launch

Your central data intelligence platform

What it does

Learns

Detects

Connects

Narrates

Deterministic insights — no LLM in the compute path Super-efficient token & DWH usage

And it becomes

Central Metric & Insight Store

Every metric in the organisation, one definition.

Central Context Store

Every business context, a single source of truth.

What it delivers

Autonomous insights

Conversational analytics

Skills

Conversational reports

Conversational dashboards

What-if analysis

How it delivers

For your people

Web app

Mobile appLater 2026

Where your teams already work

Insights pushed out and questions answered.

For your AI — DataGenie MCP

Enterprise assistants

Point them at DataGenie rather than your data warehouse.

Agentic workflows

Build the wider workflow anywhere; call DataGenie for anything analytical.

For your builders — Secure API

DataGenie works headless behind the scenes. Build your own applications.

[10] WHY THE ARCHITECTURE MATTERS

Don’t point AI straight at your data.

The common mistakescanning…

AI / LLM

×0

Your data

questions ×0 → full scans ×0

✕AI token & DWH costs blow upa full scan, every question

✕Non-deterministic answersnew SQL each time, new number

✕Limited contextonly what fits the window

The DataGenie wayasking…

AI / LLM

MCP DataGenie metrics store

scheduled

×1

Your data

questions ×0 → store reads ×0 compute ×1

✓Super-efficient AI & DWH usagecomputed once, read many

✓Deterministicsame question, same number

✓Wider contextyears of your seasonality

[11] ENTERPRISE-GRADE FROM DAY ONE

It runs inside your cloud, on the systems you already own.

ISO 27001:2022

SOC 2 Type 2

GDPR

HIPAA

Security & compliance

Zero raw rows stored, and none sent to the model — it only ever receives aggregates. AES-256 at rest, TLS in transit.

Access & authentication

Role-based access control throughout, single sign-on against Azure AD, Google or Okta, and no hardcoded credentials — secrets stay in your own key vault.

Deployment

Kubernetes in your VPC's private subnet on AWS, Azure or GCP, stood up with Terraform or Pulumi. Reuse the Databricks workspace and the model you already pay for. Or let us manage it.

Reads in place

No pipelines, no data movement

Learns from the dashboards you already have

Delivers where you work

[12] CUSTOMER PROOF NAMES ANONYMISED

Nobody went looking for any of this money.

$691K

of margin, found

“We would have missed this for another quarter without DataGenie.”

Chief Information OfficerLeading meat manufacturer, US

$4M

of leakage, prevented

“DataGenie prevented revenue leakage we never would have found ourselves.”

VP of TechnologyGlobal Fortune 500 risk analytics firm

$1M

of analyst cost, saved

“The questions we used to staff a team to ask now answer themselves.”

Analytics leadershipGlobal retailer

Customer names anonymised for external sharing. References available on request.

[13] PROOF OF CONCEPT 8 WEEKS · YOUR CLOUD

Value in a 2-month PoC.

1 Weeks 1–2

Data access & sign-off

You grant read-only access to the sources already in your warehouse, inside your own VPC. Together we write down what success means — which briefs, which numbers.

You · one access grant

2 Weeks 3–4

Connect, deploy & validate

We deploy in your VPC and read your sources in place — nothing moves. We onboard the Value Pack’s KPIs and dimensions and verify them against numbers you already publish.

DataGenie · deploys & configures

3 Weeks 5–7

You validate, we support

Briefs arrive unprompted across every unit, region and line of business, on your real data. You hold them against the criteria, with us alongside you the whole way.

DataGenie · runs itself

4 Week 8

Review & scale

A clear met / not-met readout and the path to production — the next teams, the next use cases.

Together · one readout

Entirely in your own cloud, on your real data — nothing leaves your systems. You grant access once; we do the heavy lifting.

[14] BEFORE YOU ASK 9 QUESTIONS

Q1We already have Tableau/Power BI or a similar BI tool. What does DataGenie do that they don’t?

They stay your reporting layer. What changes is who does the looking — a dashboard shows what you asked it to show, so someone has to suspect a problem first. DataGenie runs that search itself, continuously, across every combination of your metrics and dimensions.

Q2We already have a data stack and an AI assistant. Where does DataGenie sit in my enterprise architecture?

Inside your own cloud, above your data and below everything that consumes it. It reads your warehouse in place, leaves BI as the reporting layer, and exposes metrics, context and insights over MCP and a secure API — so your AI assistant and your own applications draw from it. You also gain one definition per metric, in one place.

See where DataGenie fits →

Q3Our teams already point Claude or Gemini at our data and that’s working. Why do I need DataGenie?

Point a model straight at your data and three things happen: it rescans your tables for every question, so cost climbs; it writes a different query each time, so the same question can return two answers; and it only sees the slice it can scan. With DataGenie in between, it reads an answer already computed — once, identical every run, and against years of how your business behaves.

See the difference, drawn →

Q4How does DataGenie know anything about our business?

From four places: Value Packs carry your domain’s KPIs, dimensions and reading rules; DocuFeeder reads your SOPs and process docs; Context MCP connects your systems of record; and Business Events is a calendar for what’s in no system — an outage, a promotion. So when a number moves, DataGenie can read the open ticket that explains it and give you the number and the reason together.

Q5What if our problem isn’t one of the Value Packs you list?

Then we write one with you. Every pack is made from the same parts — KPIs, dimensions, transformation rules, and the domain rules for reading them.

Q6What are the top two differentiators DataGenie boasts of?

Autonomous insights at scale — it tracks every combination of your KPIs and dimensions, not a shortlist someone had to pick, and connects them across sources without ETL. Nothing waits for someone to ask.

Deterministic insights — detection and contribution analysis are mathematics, not generation, and no raw data reaches a language model. Ask the same question twice and the number doesn’t move.

Q7Data security is very important for us. Where does DataGenie run, and what leaves our environment?

In your own environment — your cloud on AWS, Azure or GCP, or your own data centre — on Kubernetes in a private subnet. Nothing leaves: no raw rows are stored, and only aggregated KPIs and dimension values reach a language model. Access is role-based, with SSO and secrets in your own key vault.

Q8We can’t spend much time on a PoC. How long does it take, and what do you need from us?

Our PoC process is consciously designed light on your side. The product does the heavy lifting.

Two months, four phases. Weeks 1–2 you provision read-only access, name one business owner to read the output, and we agree the success criteria together. Weeks 3–4 we deploy into your environment, read your sources in place and onboard the Value Pack. Weeks 5–7 briefs arrive unprompted on your real data and you assess them against the criteria. Week 8 is a clear met / not-met readout and the path to production.

Q9We’re on-prem. Will DataGenie work for us?

Yes. DataGenie runs on Kubernetes, and that’s the same shape in your own data centre as in a cloud VPC — the platform, its services and its storage all sit inside the cluster.

[15] THE POINT

The insights must find you.

Thirty minutes, your data, a real brief at the end of it. There's a person on the other side of this button.

Talk to us See a real brief

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