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.
SOC 2 Type 2ISO 27001:2022HIPAAGDPRRuns in your VPC
100,000+ metric × dimension combinations, scored on every read against a band learned from your own history.
[02] THE PRODUCTREAL 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
AL
Good morning, Alex
Set the questions you care about once. DataGenie keeps watching, and the briefings come to you newest first.
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 Order12.68 vs 18.57↓−31.7%Primary outcome KPI · latest day vs forecast
CM Pct of GMV4.54 vs 9.33↓−51.3%Fully loaded margin rate · critical miss
Discount Rate Pct8.21 vs 4.00↑+105.3%Up is bad here · giveaway depth doubled
Cost of Goods60.84K 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 OrderForecast
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.
TV_AudioMAJORcategory · 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 breakStrong secondarySecondary cost pressureSmaller supporting anomalyNo material anomaly
TXcriticalCM per Order−182.1% −15.04
FLcriticalCM per Order−167.2% −12.18
CAcriticalCM per Order−107.4% −1.33
GAsupportingCost of Goods+171.9% +3.71K
OHsupportingPayment 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.
CategoryDemandMarkdownEconomicsRead
Mobile Phoneslargest connected demand case76+275.2%198.6K+1370.4%−7.89majorDemand arrived, but markdowns and payment fees destroyed margin.
Laptopscleanest all-stage surge41+289.0%73.3K+1147.7%−7.68majorHigh-ticket demand converted, but unit economics collapsed.
TV_Audiomost extreme markdown surge43+183.7%176.9K+1882.9%1.51−96.1%The category still sold, but price was given away too aggressively.
Marketplace-widewhole business3,817+40.8%99.19%+0.2%17.82%+0.3%Rules out weak demand and stock-out explanations.
Nobody had to ask for itread in placeevery step recorded
NotebooksMemory
Ask Wisdom, know Everything
What would you like to know about your data?
Select dataset ⌄Skills, Data & MCP
Summarise this quarterWhat changed this week?Top movers by revenue
Notebooks in Retail
Margin by category · markdown3 cells · today
← Notebooksmargin_by_category
AL
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
AL
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.
AL
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
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
inventory-availability-guardian.mdships with the pack · never edited here
Inventory & Availability Guardian
Catches stockouts before they lose the sale.
What it watches, and what normal is
Cut by
SKUstoreregionchannelDC
every combination of them, continuously
What makes a move a finding
a move that repeats last year’s shape at the same time of year is not one
It reads, in place
Tour running. Hover or click to pause
Trusted by
GlobalFortune 500Risk analytics
THE THREE POWERS Finds it. Finds all of it. And the same way twice.
[03] AUTONOMOUSPOWER 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
Writing the brief
DataGenie brief · daily · autonomous06:12 · 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 Break RateNet CASA FlowBalance-weighted OutflowOutflow-to-Yield ShareFirst-Payee RatioOff-Hours Activity
5 dimensions
branchcohortraildestinationpayee-status
Six KPIs by five dimensions, swept on every read. DataGenie came with all of it.
[05] DETERMINISTICPOWER 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?
waitingRun01
—
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 Brokena metric it tracks · defined once, at onboarding
branchone of the 5 dimensions it watches
last week7 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 metricsAsk01
$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.
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.
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.
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 $1.4M exposed — 29% of the total on 26% of the stores.
% of lost demand from loyalty membersWeekly lost sales ($K)
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.
ForecastingScenario planning · what-ifPrescriptive next best actionsContribution analysisCorrelation & attributionConversational dashboardsReal-time, hourlyNext-period projectionAlerts to Slack, Teams, Jira
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.
Deterministic insights — no LLM in the compute pathSuper-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] ENTERPRISE-GRADEFROM DAY ONE
It runs inside your cloud, on the systems you already own.
ISO 27001:2022SOC 2 Type 2GDPRHIPAA
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
[11] CUSTOMER PROOFNAMES 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.
[12] GETTING STARTEDFIRST BRIEFS INSIDE A DAY
Point it at your data. First briefs inside a day.
[ 01 ]
Point it at your data
Enter your database credentials. It reads in place — no pipeline to build, no ETL job to maintain, no migration first.
You · minutes
[ 02 ]
Brief it like an analyst
Describe the problem in plain English — it proposes the KPIs, dimensions and time grain. Or switch on a Value Pack that already has them written for your industry.
You · one conversation
[ 03 ]
The briefs start arriving
One-time setup, then autonomous. No dashboards to build, no alerts to tune, no questions to think of first.
DataGenie · from then on
[13] BEFORE YOU ASK4 QUESTIONS
Q1What is a Value Pack?
The KPIs, the dimensions and the transformation rules for one recurring problem, written once and shipped ready. Not a template you fill in — the rules that separate a real cause from a coincidence are already in it.
Q2What is a Brief?
A written finding, on a schedule, sent to whoever owns the number. You never have to ask for one. Every figure traces back to the row it came from.
Q3Is this instead of my dashboards, or alongside them?
Alongside. It reads the same warehouse and answers the questions nobody charted. Nothing is migrated and nothing is switched off.
Q4What if my problem is not one of the sixteen?
A pack is assembled from the same parts — KPIs, dimensions, rules. We author a new one with your team.
[14] 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.
SOC 2 Type 2
ISO 27001:2022
HIPAA
GDPR
