From conversation to a live, AI-assisted dashboard, in days.
Built from what you say, answered in plain English by an embedded assistant, and changed on demand as your questions change. Sales, margin, trade spend, and service, on the one screen your team opens every morning.
- 1Faster turnaroundFrom the first conversation to a working sales and margin cockpit in days, not quarters.
- 2A wireframe in 2 to 3 daysLook, feel, filters and colours on sample data, before any build spend.
- 3A closed loop with AIWhat you say in the room is recorded, becomes the brief, and reaches the leadership view as it lands.
- 4An assistant built into the screenAsk in plain English about brands, channels, margin or trade spend. It reads the screen and the data underneath.
- 5No BI licence dependencyNo per seat Power BI or Tableau. Any view, embedded anywhere, with each user seeing only their slice.
- 6Safety and securityHosted on infrastructure you choose, with a signed data agreement, no training on your data, and clear protocols on our side.
Sales vs plan, worst first
| Brand | Sales vs plan | Gross margin |
|---|---|---|
| Surya Edible Oils | -3.9% | 12.3% |
| Sparkle Home Care | -2.9% | 29.4% |
| Aanand Atta | -1.6% | 19.0% |
| Komal Personal Care | -0.9% | 44.0% |
| Namkeen Nagar | -0.1% | 34.3% |
| Chai Junction | +0.2% | 38.1% |
| Tashan Biscuits | +3.8% | 33.3% |
| Pavitra Spices | +4.0% | 35.9% |
| Zing Beverages | +4.9% | 41.0% |
| Frostbite Foods | +7.1% | 29.6% |
| Honeybee Naturals | +7.4% | 38.8% |
Trade spend vs plan
| Brand | Actual | Plan | Gap |
|---|---|---|---|
| Komal Personal Care | 15.9% | 16.0% | -0.1% |
| Zing Beverages | 15.4% | 15.5% | -0.1% |
| Frostbite Foods | 13.9% | 14.0% | -0.1% |
| Honeybee Naturals | 13.3% | 13.5% | -0.2% |
| Sparkle Home Care | 13.2% | 13.0% | +0.2% |
| Tashan Biscuits | 12.2% | 12.5% | -0.3% |
| Namkeen Nagar | 12.0% | 12.0% | +0.0% |
| Chai Junction | 11.1% | 11.0% | +0.1% |
| Pavitra Spices | 9.0% | 9.0% | +0.0% |
| Aanand Atta | 6.6% | 6.5% | +0.1% |
| Surya Edible Oils | 4.9% | 5.0% | -0.1% |
Service and inventory
| Brand | Fill rate | Inv. days |
|---|---|---|
| Surya Edible Oils | 90.7% | 30 |
| Frostbite Foods | 91.7% | 65 |
| Honeybee Naturals | 94.6% | 55 |
| Komal Personal Care | 95.0% | 47 |
| Pavitra Spices | 95.2% | 46 |
| Chai Junction | 95.5% | 42 |
| Sparkle Home Care | 95.8% | 50 |
| Tashan Biscuits | 96.3% | 43 |
| Namkeen Nagar | 97.0% | 44 |
| Zing Beverages | 97.3% | 37 |
| Aanand Atta | 97.6% | 29 |
Net sales vs plan, by category
Net sales by channel
Net sales and contribution trend
| Brand | Exception | Period | Severity |
|---|---|---|---|
BR003 | Channel split below brand total | 2025-10-31 | High |
BR007 | Channel split below brand total | 2025-11-30 | High |
BR009 | Missing submission | 2024-02-29 | High |
BR005 | Service below threshold | 2026-02-28 | High |
BR005 | Inventory days outlier | 2026-01-31 | Medium |
BR002 | Margin step-down | 2026-03-31 | Medium |
How this gets built
From the first conversation to a living dashboard. Click a step to see what actually happens.
- 1Discovery callWe listen and capture
- 2Instant wireframeTwo to three days
- 3Wireframe reviewThey react, we capture
- 4The full buildPhased to go live
- 5Change on demandA continuous loop, in days
The discovery call
We sit with the commercial, finance, and supply teams and we listen. The recording becomes the specification. No long requirements document, the conversation is the brief.
In the room
- Rakesh, CFO (margin has to be clean and reconciled)
- Meera, Commercial Head (channels and sales versus plan)
- Sundar, Supply Chain (fill rate and inventory)
What they actually said
What we captured
- One morning cockpit with a red, amber, green view across every brand
- Net sales, volume, gross margin, contribution, trade spend, all against plan
- Fill rate and inventory, with a flag when service drops below 92 percent
- A data health layer so the numbers can be trusted
And where the data lives
From the same conversation we map every source the numbers come from: the distributor system, the field app, the retailer portals, the e-commerce export, and how it all reconciles. That map is the data architecture.
[00:12] Bharathwaj Thanks for the time. Before I show anything, I want to understand how you run the month. Where does your sales data live today, and how long does it sit before someone actually looks at it?
[00:31] Meera (Commercial Head) It lives in four places. Primary sales out of the distributor management system, secondary off the field app, modern trade off the retailer portals, and e-commerce in spreadsheets the team pulls every Monday. By the time it is all in one sheet it is the tenth working day.
[00:58] Bharathwaj So you spend the first third of every month assembling last month. What is the one number the leadership team argues about most?
[01:14] Rakesh (CFO) Margin. We see net sales growing but the gross margin keeps slipping and nobody can say cleanly whether it is mix, input cost, or trade spend running ahead of plan. I want gross margin and contribution by brand, on one screen, with a plan comparison.
[01:41] Bharathwaj Right, so the headline is net sales, volume, gross margin, contribution, trade spend as a share of sales, and how each sits against plan. Anything on the supply side?
[02:03] Sundar (Supply Chain) Fill rate and inventory. When a brand is short on shelf we lose the sale and the retailer remembers. I want fill rate by brand and days of inventory, and I want a red flag the moment service drops below ninety two percent.
[02:29] Meera (Commercial Head) And the channel split. General trade is still most of the book, but quick commerce is growing fast and the margins are different. I need net sales by channel without exporting three systems and stitching them.
[02:52] Bharathwaj Understood. Let me play it back. A morning cockpit: a red, amber, green view across every brand so you know in ten seconds what is on track and what is not. Then the headline strip Rakesh wants, sales and margin against plan. Then a layer for the commercial and supply teams, sales versus plan worst first, trade spend versus the planned rate, fill rate and inventory days. Channel mix on one tile. Click any brand to see its own profit and loss, its channels, its service. Have I got it?
[03:28] Rakesh (CFO) That is it. The one thing I will add: I do not trust the numbers until they reconcile. If a channel split does not add up to the brand total, I want to see that flagged, not buried.
[03:47] Bharathwaj Then we build a data health layer into it. Every month it checks the obvious things, channel splits tying to the brand total, a missing submission, a stale price, a service miss, and it separates a data problem to fix from a real business risk to escalate. The record is the spec. We will have a clickable wireframe on sample data in two to three days.
The instant wireframe
The transcript is the brief. We turn it into a clickable picture of the cockpit, on sample data, so you see your own asks rendered before a rupee of build is spent. That removes the loop of build, reject, rebuild that stalls reporting projects.
What the wireframe shows
- The headline strip the CFO asked for
- The red, amber, green brand heatmap
- The commercial and supply layer, channel mix, and the data health tile
The wireframe review
We walk them through the wireframe and capture every reaction. That second transcript becomes the build list, eleven precise changes.
The change list
- 1 Contribution shown right next to gross margin
- 2 Heatmap tile shows sales versus plan, not just the value
- 3 Fill rate drives the red, amber, green colour
- 4 Trade spend versus the planned rate as its own view
- 5 One contribution definition used everywhere
- 6 Every brand on the heatmap clickable through to its own page
- 7 Net sales by channel on a single tile
- 8 Days of inventory and working capital by brand
- 9 Data health exceptions list, exportable with the figures
- 10 Net sales and contribution trend over the last four quarters
- 11 Hosting and data location agreed in writing
[00:20] Bharathwaj This is the wireframe from your transcript. Everything here is sample data. Tell me what is wrong, that becomes the build list.
[00:44] Rakesh (CFO) The headline is close. But contribution needs to sit right next to gross margin, not two cards away. The story is the gap between the two, that is where trade and advertising live.
[01:09] Meera (Commercial Head) The channel donut is good. Can the heatmap tile show sales versus plan rather than just the value? I want to see who is behind the number, not just who is big.
[01:33] Sundar (Supply Chain) Fill rate should drive the colour. A brand can be ahead on sales and still be amber if we are missing service. Do not let a good sales number hide a supply problem.
[01:58] Rakesh (CFO) And one definition of contribution everywhere. The same gross margin, the same trade treatment, in the strip, the table, the brand page, the assistant. If the assistant and the screen ever disagree, I stop trusting both.
[02:26] Bharathwaj Agreed, and that is the most important governance point. One number, computed one way, everywhere. The assistant does not invent figures, it runs a query and reads the result back, so it ties to the screen by construction. I will put the change list together: contribution next to margin, the heatmap tile shows sales versus plan, fill rate drives the colour, trade spend versus plan as its own view, and one contribution definition across the whole thing.
The full build
The change list becomes the live cockpit. The numbers are governed, every figure is a door into the detail, and you ask questions in plain English.
What landed
- Governed numbers: one contribution definition everywhere, tied to what is on screen
- Filter by category or region and the whole cockpit recomputes
- A built-in data health layer
- Cleo, an assistant that answers over the full data in plain English
- Testing ready about a week, after data access and the source systems are confirmed
- Sign off on real numbers
- Go live
Change on demand
You are never done. When something needs to change, you do not file a ticket and wait a quarter. You talk, we capture it, and we apply it, in days.
For example
You say it, we make the change, it is live. The dashboard keeps up with the conversation.