AI Powered Business Intelligence

The insight stack

Ask the question.
Get an answer you can audit.

Every insight we deliver carries three parts: the question you asked, what the model found, and the action it recommends. No mystery scores. Click through the deck — this is the exact shape your teams receive.

Insight card

QuestionWhat will demand look like next month?

InsightFourteen SKUs are tracking 22% above their twelve-week trend, and three of them will run short before the 20th at current purchasing speed.

Recommended actionPull the purchase orders for those three SKUs forward one week. The purchasing lead approves the change in one screen.

Insight card

QuestionWhich customers are close to leaving?

InsightTwelve accounts show falling logins alongside rising support tickets — the same pattern that preceded 60% of last year’s cancellations.

Recommended actionSuccess managers call the top five this week. The list re-ranks daily, and every call outcome is logged back into the model.

Insight card

QuestionWhy did margin drop in the west region?

InsightDiscount depth rose four points and freight cost per order rose nine. Product mix barely moved — the leak is pricing, not portfolio.

Recommended actionTighten approval on discounts above 12% in that region and re-price the two freight lanes driving most of the increase.

Insight card

QuestionWhat belongs in Monday’s board pack?

InsightRevenue is on plan, churn risk is up slightly, and two pipeline deals slipped a quarter. Nothing else moved enough to mention.

Recommended actionPublish the generated draft — but only after your analyst checks the three exception notes and adds their commentary.

Insight card

QuestionIs this spike real, or a broken feed?

InsightNot real. The spike traces to three duplicate orders from one integration, in the same window the pipeline’s freshness check flagged.

Recommended actionDuplicates quarantined, yesterday’s report recomputed, and the integration owner notified with the exact timestamps.

Click the deck or use the button to move through the insights.

The human + AI duet

Two lanes,
one decision.

We don’t hand the wheel to a model. The AI lane reads, drafts and watches without tiring. The human lane frames, judges and owns. They meet at three points — and no output reaches a customer, a price or a ledger without one.

Human lane context, judgement, ownership

AI lane reading, drafting, watching

Framing

Human decides which question is worth asking and what a good answer looks like.

AI turns it into a query, a testable definition and a first pass at the evidence.
Evidence

Human reads the reasoning, pushes back on the odd numbers and signs off the finding.

AI reads the data at scale, drafts the insight and cites the rows and windows behind it.
Action & watch

Human makes the call, takes the action and owns what happens next.

AI monitors the result and raises a hand the moment reality diverges from the plan.

Model facts

Read the label
before you trust the model.

Any vendor can say “AI”. We publish the label: what a feature is made of, how the work is split, where people sign off, and what happens when the model is unsure.

Model facts

Serving size: one decision workflow · re-served with every data cycle

Process composition

Data quality & lineage30%
Modelling & evaluation25%
Guardrails & monitoring25%
Human review & sign-off20%

Per serving, you can expect

Accuracy: measured on hold-out data before launch, reported with its error band
Explainability: every answer links back to the rows, windows and definitions behind it
Drift checks: weekly, with automatic alerting when inputs or behaviour change
Human approval: required before any action reaches a customer, a price or a ledger
Uncertainty: low-confidence outputs are flagged “uncertain” instead of guessed
Training data: your data stays in your environment; never used to train public models

Percentages show how MADDS splits effort on a typical AI feature build — they are process shares, not performance claims. Accuracy depends on your data, and we measure it before we promise anything.

MADDS builds AI features as part of your BI stack — inside the warehouse and dashboards you already run, not as a chatbot bolted on the side. A forecasting model sits next to the purchasing screen that uses it. A churn score appears where the success team already works.

The unglamorous parts get the most attention: clean inputs, stable metric definitions, monitored pipelines, tested hand-offs. That is what keeps a model trustworthy in month six, not just on demo day.

Where the humans stand

A named reviewer signs off before any output reaches a customer, a price or a ledger.
Every figure can be traced from the dashboard back to the raw rows behind it.
Accuracy is re-measured on a schedule — if it slips, your team hears it first.
No fully automated actions on live systems, and no claims we haven’t measured.

Scenario theatre

Three stages where
the work actually changes.

Not pilots and slideware — the everyday scenes where a measured AI feature saves hours and catches what people miss. Choose a scenario to compare the same workflow before and after.

Before

Planning runs on yesterday’s picture.

Purchasing works from last month’s spreadsheet and the sales lead’s gut feel. Eight SKUs stock out while forty sit on the shelf too long.

Refresh: Monthly, by hand
Surprise: Frequent

After

The forecast refreshes before decisions do.

The model recalculates nightly from history, promotions and seasonality. Purchasers see SKU-level ranges and approve changes in one screen.

Refresh: Nightly
Human step: Purchaser approves

Before

Churn appears only when the customer leaves.

Save calls start after the relationship is already over, and nobody can say which accounts were drifting or why.

Warning time: None
Save rate: A guess

After

Risk becomes a ranked list with reasons.

Every account is scored weekly on usage, support and billing signals. Managers decide who to call and log the outcome back into the model.

Refresh: Weekly
Human step: Manager chooses

Before

The weekly pack is assembled by hand.

Monday’s report costs an analyst a day of copy-paste. Numbers arrive after the meeting and charts disagree because the source versions drift.

Build time: One day a week
Versions: Three

After

The first draft is ready before the meeting.

The pack drafts itself from governed metrics. The analyst checks exceptions, adds commentary and signs off before anything is shared.

Ready: 06:00 Monday
Human step: Analyst signs off

Prediction vs actual

We show the dots
before we claim the line.

Every forecasting model we ship is graded one way: predicted against actual, on weeks the model has never seen. Then we keep grading it after go-live — and report the score either way.

model prediction actual demand · units shipped per week

Illustrative plot from a demand-planning build. Your accuracy is measured on your data before anything goes live — and published to your team either way.

94%

of weekly demand points landed inside the stated error band on hold-out weeks the model had never seen.

8 weekshold-out window, kept back before go-live
±6%mean absolute error, printed on every forecast
Monthlyre-grading against actuals — accuracy slips get escalated, not buried

A forecast without an error band is a guess with a chart on it. We ship the band, the back-test and the re-grading schedule with the model, so planning teams know exactly how much weight each number can carry.

Who we Are

The MADDS way

Career

CRM

EMRS

HMIS

Web Portals

CMS

Mail Management System

Supply Chain Management

Business intelligencethat reasons —and shows its working.

Ask the question.Get an answer you can audit.

Two lanes,one decision.

Framing

Evidence

Action & watch

Read the labelbefore you trust the model.

Where the humans stand

Three stages wherethe work actually changes.

Planning runs on yesterday’s picture.

The forecast refreshes before decisions do.

We show the dotsbefore we claim the line.

Put a thinking machinebeside your team.

Business intelligence
that reasons —
and shows its working.

Ask the question.
Get an answer you can audit.

Two lanes,
one decision.

Read the label
before you trust the model.

Three stages where
the work actually changes.

We show the dots
before we claim the line.

Put a thinking machine
beside your team.