Introducing ChatKa – AI Root-Cause Analyst for Grocery Operations
Most grocery and fulfilment teams know this feeling:
- a KPI drops,
- everyone sees it on the dashboard,
- and then the real work begins.
Analysts start pulling extra reports.
Managers chase explanations.
Leaders ask, "What's actually going on?"
Hours or days can pass before anyone has a clear picture of why the KPI moved—and by then, the damage to margin and service has already happened.
ChatKa was created to close that gap.
From "what" to "why"
Traditional BI tools are very good at telling you what happened:
- Perfect Orders dropped 3%.
- Substitutions spiked in chilled.
- On-Time Inbound fell below target.
But they rarely tell you why in a way that an operations leader can use immediately.
ChatKa's focus is simple:
When a KPI moves, explain why it moved, in minutes—not days.
What is ChatKa?
ChatKa is an AI Root-Cause Analyst for Grocery Operations.
It connects to a retailer's existing data warehouse (Snowflake, BigQuery, Redshift, Teradata, SQL Server, Oracle, etc.), models key operational KPIs, and explains unexpected KPI movements in plain English inside Microsoft Teams or Slack.
Instead of being a generalist conversational BI layer, ChatKa is built specifically for:
- online grocers,
- supermarket fulfilment centres,
- and grocery-adjacent retail operations.
How it works at a high level
1. Connect to your data warehouse
ChatKa connects with read-only access to your existing data warehouse. No migration is required.
2. Model your KPIs
Together we define how KPIs such as Perfect Orders, On-Time Inbound, Substitutions, Waste, and Service Levels are calculated from your tables.
3. Detect movements
ChatKa watches for meaningful movements in these KPIs—such as a drop in Perfect Orders at a specific CFC or a spike in substitutions in chilled.
4. Analyse root cause
When something moves, ChatKa:
- inspects inbound, stock, and fulfilment data,
- looks for patterns (e.g. supplier delays, specific SKUs, time windows),
- and builds a chain-of-cause explanation.
5. Deliver explanations inside Teams or Slack
The result is delivered where teams already work, with:
- a short explanation,
- key drivers,
- and an estimated margin or service impact.
A concrete example
Imagine a sudden drop in Perfect Orders at CFC3 for chilled SKUs.
Instead of manually stitching together data from multiple dashboards and exports, ChatKa might return something like:
- Root cause: inbound delays from three chilled suppliers led to stock shortages on six SKUs.
- Effect: increased substitutions and unfulfilled orders.
- Impact: an estimated margin loss over the period.
The explanation is short enough to share in a Teams or Slack thread, but grounded in your actual warehouse data.
Why start with grocery?
Grocery operations are:
- high volume,
- margin-sensitive,
- operationally complex,
- and tightly coupled across inbound, stock, and fulfilment.
They are also rich in data but constrained in time.
By focusing on this environment first, ChatKa can:
- provide meaningful root-cause analysis in a short 4-week pilot,
- prove value with a small set of KPIs and locations,
- and then expand gradually.
Where ChatKa fits in your stack
ChatKa is not trying to replace your existing BI stack.
It is designed to:
- sit on top of your data warehouse,
- reuse your existing business logic,
- and reduce the manual work needed to understand KPI movements.
Think of it as a specialised analyst that only answers one class of question extremely well:
"Why did this KPI move?"
What's next
Right now, ChatKa is working with a small number of organisations in grocery and grocery-adjacent fulfilment.
If you are:
- a Supply Chain Director,
- Head of Online Fulfilment,
- or responsible for operational performance in a grocery context,
and you're interested in a focused pilot, you can reach out via the contact options on the homepage or book an intro call.
The aim is simple:
turn KPI firefighting into a faster, clearer, and more repeatable process.
ChatKa – AI Root-Cause Analyst for Grocery Operations.