01 · FORECASTING

A forecast nobody acts on is just an expensive opinion.

We wire the number into the order, the roster, the maintenance window — not into another dashboard.

"We measure the baseline before we build the model — and tell you if the model doesn't beat it."

Book a Discovery Sprint → The first call is free · 45 minutes · no obligation

02 · WHAT WE BUILD INTO A FORECASTING SYSTEM

Every capability here exists to change one decision you make every week.

Signal ingestion

Orders, seasonality, promotions, external drivers — one pipeline, not a spreadsheet per source.

Baseline + model

Naive baseline scored first. Model only ships if it beats it, on out-of-sample data.

Uncertainty band

A range, not a single number — so the downstream action knows how much to trust it.

Decision integration

Wired into the reorder point, the roster, the maintenance window. The action, not a dashboard.

Drift monitoring

Exception routing to a human

Scheduled retraining

Full audit trail

Ownership handoff — no lock-in

Capacity booked against the forecast

03 · METHOD

Baseline first. Model second. That order is not negotiable.

Before we tune anything, we score what a naive guess would have gotten you — same-period-last-year, or a flat trend line. If our model can't beat that number out of sample, we say so. That's the gate every forecast clears before it reaches you.

01
Establish the naive baseline

We compute what a simple, no-model forecast would have scored on your own history — the number your last vendor never showed you.
02
Backtest out-of-sample

The model is scored only on periods it never saw during training. No in-sample numbers are shown to you — ever.
03
Report uncertainty, not a point number

Every forecast ships with a confidence band. A single number with no range is a guess wearing a decimal point.
04
Fail the gate, hear about it

If the model doesn't beat baseline on your data, that's the deliverable — not a buried footnote in an appendix.

Bring your baseline — book a Discovery Sprint

04 · WHY MOST PREDICTIVE-ANALYTICS PROJECTS FAIL

The forecast changed nothing — and everyone on the team knew why.

Most predictive-analytics projects don't fail on math. They fail on the five decisions below — made once, at the start, and never revisited.

— No naive baseline was ever scored against the model.
— The output fed a dashboard, not an order, a roster, or a maintenance window.
— Confidence intervals were dropped from the final report.
— Accuracy was validated on data the model had already seen.
— The pipeline stayed the vendor's, not yours, after go-live.

05 · From a Number to a Decision

Getting forecasting into production — not into a slide.

A model that scores well in a notebook and a model that survives a live rollout are different engineering problems. We build for the second one: the number has to reach the order system, hold up when demand drifts, and keep working after we leave.

Wired into the system of record

The forecast lands inside the ERP, WMS, or scheduling tool your team already opens — not a dashboard nobody logs into.

Drift is monitored, not assumed

Live accuracy is tracked against the baseline every cycle. When the model degrades, it's flagged before it costs a decision.

Retrained on a schedule, not a crisis

A retraining cadence is set at handoff — so the model improves on its own timeline, not after it's already wrong.

You own the pipeline

Code, weights, and infra transfer to your team. No lock-in, no dependency on us to keep it running.

06 · RECEIPTS

Only out-of-sample results shown.

No backtest is graded on data the model already saw. Every number below is what the forecast scored on weeks it had never touched.

Vikaas — outbound pipeline forecast

31%below naive baseline

6wkheld-out test window

Delivery capacity gets booked against this forecast every week — it is graded on our own schedule, not a slide.

Retail client — SKU-level demand

18%error vs. baseline's 41%

92%of SKUs beat naive

Reported per-SKU, not blended — an average would have hidden where the model actually lost.

Manufacturing client — maintenance windows

44%fewer false alarms

120hrsunplanned downtime avoided

Baseline was the existing threshold-alert system, not a strawman naive guess.

07 · DOGFOODING

We forecast our own business before we forecast yours.

Vikaas forecasts Banao's own pipeline every week — delivery capacity gets booked against it. Same discipline, your data.

THE MECHANISM

Every week, Vikaas produces a pipeline forecast. Every week, Banao's own delivery capacity is booked against that number — not a dashboard nobody opens.

2016 building since — this discipline predates the pitch

300 people, run on our own AI stack

Same baseline-first, decision-integrated discipline — applied to your data.

Bring us your data

08 · WHERE WE BUILD AND DEPLOY

Not remote. Regional.

5 offices — Bengaluru, Chandigarh, Dubai, Cambridge, California

3 regions covered directly — India, UAE, US/UK

30+ clients served across those regions since 2016

0 time-zone handoffs between build and deploy teams

Model build sits in Bengaluru and Chandigarh; deployment sits with the client's own operations team, in the client's own time zone.

09 · NOT A FIT

We'd rather lose the deal here than lose your trust in month three.

Every engagement clears a baseline gate before it clears a contract. If your situation can't clear it, we'll say so — on this call, not after a quarter of retainer.

“We measure the naive guess before we build anything. If the model can't beat it, you don't pay for the model.”

Internal standard Applied to every forecasting engagement, including our own — Vikaas forecasts Banao's own pipeline the same way.

Bring your data — we'll tell you the truth in 45 minutes

10 · HOW WE START

Bring us your baseline. We'll tell you if a model beats it.

Three steps, one sprint, no obligation. Only out-of-sample results shown.

01

Baseline first

We score a naive forecast on your own historical data before any model exists — the number every claim gets measured against.
02

Backtest, out-of-sample

The model is tested only on periods it never saw — the same discipline we hold our own forecasts to.
03

Verdict, not a demo

If the model doesn't beat the baseline, we say so. If it does, you see the margin — in your own units.

Book a Discovery Sprint → The first call is free · 45 minutes · no obligation

11 · FREQUENTLY ASKED QUESTIONS

Before you book the sprint, the questions we hear every time.

What counts as a "baseline" and why does it come before the model?

A naive forecast — last period's value, or a simple seasonal average — computed on the same data before any model is built. If the model can't beat it out-of-sample, we say so before you pay for the model.

What data do you need from us to start?

Historical transaction or order-level data — typically 12–24 months — from whatever system holds it today. No pre-cleaning required before the first sprint.

Do you work with our ERP, or do we export data manually?

We connect to the ERP or warehouse directly where access allows; where it doesn't, a scheduled export is set up once and left running.

Which regions have you delivered in?

US, UAE, and India — offices in Bengaluru, Chandigarh, Dubai, Cambridge, and California, with clients including Swiggy, PhonePe, Indian Oil, and RAK Ceramics.

How is accuracy actually reported?

Out-of-sample only — scored against data the model never trained on — alongside the baseline it had to beat. No in-sample numbers are shown.

What happens after the forecast is generated?

It's wired into the decision it's meant to change — a reorder point, a delivery-capacity booking, a staffing roster — not left as a standalone dashboard.

Do we own the model, or are we locked into you?

You own the system. No proprietary lock-in on the model, the pipeline, or the data it runs on.

How long until we see a working forecast?

Weeks, not quarters — the discovery sprint itself is scoped to 45 minutes on your own data before any build commitment.

12 · RELATED