You do not have a dashboard problem

You have dashboards. What you do not have is a decision that gets made the same way twice, by someone who owns the result. Iksula builds that decision on your own warehouse, runs it, and stays accountable for the outcome.

Why analytics stalls after the dashboard ships

Nobody owns the decision

The report is delivered, the meeting happens, and the call still gets made on instinct. Analytics teams are measured on shipping the answer, not on whether anything changed.

The numbers do not agree

Two teams pull the same metric and get different figures, so the meeting becomes an argument about the data instead of a decision about the business.

The model is right and ignored

A planner overrides the recommendation daily, nobody records why, so the model never improves and trust never builds. This is the most common failure we see, and it is an instrumentation problem, not a modelling one.

Three parts, and none of them is a dashboard

The Decision System

A governed semantic layer and signed-off metric definitions running on your warehouse — Databricks, Snowflake, BigQuery or Fabric. Ask in plain language, get an answer with every number traceable. Your data never moves to us.

The AI Pod

A small Iksula team that runs the decision for you. They own the exceptions, the adoption and the outcome. Technology tells you what happened; people are what make it get acted on.

The Domain Packs

Pre-built commerce decisions, built once and configured per business model, so an engagement starts from a working template rather than a blank page.

Where we sit

Warehouse-native

We run on the engine you already bought. Every warehouse now ships its own natural-language layer, and we are not trying to replace one — we sit on top of it and supply what it does not have.

Engine-agnostic

We work with your existing BI and AI tools rather than asking you to standardise on ours. There is no Iksula platform for you to migrate onto.

Outcome-owned

Each sprint is scoped against a named business decision, not a dashboard delivery. If the decision does not change, the sprint did not work, and we would rather find that out in six weeks than six quarters.

Fourteen decisions, built once

Each pack is the same decision tuned to your business model — retail, consumer goods, B2B manufacturing, or marketplace and direct.

Demand Forecasting

Inventory & Replenishment

Supply and Production Planning

Pricing & Markdown

Promotion and Trade Promo

Assortment and Portfolio

Customer CLV & Churn

Sales & Channel

Marketing Mix

Margin Leakage & Recovery

Returns and RTO

Working Capital and Order-to-Cash

Logistics and Network

Anomaly and Risk

Six weeks to a measured answer

Week 1

Baseline and service level

We agree the decision, measure how it is made today, and write down what good looks like. This is the week most programmes skip and later wish they had not.

Weeks 2 to 4

Build the pack

The pack goes onto the semantic spine, with definitions signed off as we go rather than at the end.

Week 5

Shadow run

It runs alongside your current process without replacing it. You see what it would have recommended and what that would have been worth.

Week 6

Readout

What it did, what it is worth, and a proposal for running it. If the answer is that it is not worth running, we say so.

Where this has already run

ABFRL

Marketplace reconciliation across seven selling platforms, recovering 5–10% of net ecommerce margin — around ₹15–16 lakh a month.

SiteOne

70% lower data-management cost across the foundation Iksula runs.

ARASCO

18% efficiency improvement across a manufacturing estate in Saudi Arabia.

eCity

60% less time spent on reporting across a UAE retail group.

Three ways to buy this

Frequently asked questions

Our warehouse already has a natural-language feature. Why do we need you?

Because the engine was never the hard part. Every warehouse now ships one, and they are good. What they do not have is agreed definitions, a domain pack that knows what a markdown decision looks like, and somebody accountable for whether the decision improved. We supply those three and leave your engine alone.

Does our data have to move?

No. The layer runs where your data already lives. That is a deliberate design choice, not a compliance concession — moving commerce data to a vendor creates a copy that immediately starts drifting from the original.

How accurate is it?

We will not quote you a number, and you should be suspicious of anyone who does. Accuracy on a real, messy warehouse is nothing like accuracy on a benchmark. What we will commit to is that an out-of-scope question returns an error rather than a confident wrong answer, and that quality is measured continuously rather than assumed at go-live.

What if the sprint shows it is not worth doing?

Then we tell you that in week six and you have spent six weeks instead of a year. That is the point of scoping a sprint against a named decision rather than a deliverable.

Who runs it afterwards?

We do, if you want us to. The pod is the normal path, because a decision layer that nobody operates decays in the same way the dashboards did. You can also take it in-house — it is running on your warehouse, so there is nothing to extract.

Start with one decision

Tell us the decision your team argues about most, and we will tell you what it would take to make it once, properly, on the data you already have.