Live orders and SLA exceptions
Bring order events into a real-time KPI board. Route exceptions while the delivery decision can still change.
You receiveLive KPI board
Delivered through Real-Time IntelligenceDecide while the order
is still moving.
Connect order events, inventory and customer workflows so operations can act while the delivery decision still matters.
Live operating decisions
Spot the exception while the order is moving.
Customers get an answer
Ground support agents in the retailer’s own procedures.
One number across teams
Use shared definitions for merchandising and operations.
Up to 10 minutes → seconds decision latency · Swiggy
Keep pace with
the live order.
Orders, stock and support conversations change while batch reports wait. Put the decision on current events and give every team the same metric definitions.
Catch the live exception
Read order events against the operating window.
Keep customer context
Connect support workflows to the permitted customer record.
Agree the metric once
Reuse the model across channels and teams.
This is the right partner when
- Order exceptions surface after the SLA is missed.
- Store, app and partner feeds disagree.
- Support teams repeat the same policy lookups.
Data and AI work
for retail operations.
Start with the decision, follow the data, and leave each engagement with something your team can use.
Support grounded in your procedures
Connect agents to approved support knowledge and tools. Keep escalation available when the customer needs a person.
You receiveGrounded tool path
Delivered through Agentic AIShared metrics across retail teams
Align merchandising, marketing and operations on a semantic model. Give Power BI users the same definitions.
You receiveSemantic model
Delivered through Analytics & BIStore and app data together
Map store, app and partner feeds into a validated model. Make the next source a repeatable onboarding task.
You receiveOnboarding runbook
Delivered through Data IntegrationCost engineered into the platform
Measure legacy workloads before sizing the target estate. Price the migration sequence against actual retail demand.
You receiveCosted wave plan
Delivered through Cost Optimization
The Mandelbulb Kit Retail & E-Commerce editionBuilt and maintained in-house
Engineered in-house.
The Mandelbulb Kit.
Accelerators, agents and products we build and maintain ourselves, brought into an engagement wherever they fit.
Real-time accelerator · Watch the order
Pulse
Your own operational events, turned into decisions while the work is still moving.
- Takes operational events into live KPIs
- Raises anomaly alerts and automated actions
- Starts with two weeks on one stream family
During fulfilment
Explore more about PulseAlso in this edition
- TheTranquilIn customer supportVoice agents that detect tone, handle interruptions, keep context across turns and escalate cleanly to a supervisor., explore TheTranquil
- BlumcoreAcross operating workflowsThe runtime every Kit agent runs on: it reads your data, acts in your systems and audits every step., explore Blumcore
Proof from
retail and commerce.
Swiggy
Quick commerce · Retail
How Swiggy Closed the Gap Between an Order and the Shelf
Swiggy’s operating decisions needed fresher information than batch dashboards could provide. A live event path connected order activity to the decisions teams make while work is moving.
- Decision latency
- Up to 10 minutes → seconds
- Events processed daily
- 1B+
Pine Labs
Payments · Merchant services
Terminal Support by Voice for Pine Labs Merchants
Merchants on Pine Labs terminals needed help at the counter, in their own language, without a queue. The voice assistant follows Pine Labs’ own support procedures and hands over with the full context.
- Answers merchants by voice, in their own language
- Follows Pine Labs’ own support procedures
- Runs the routine checks itself
Myntra
Retail · Online fashion
How Myntra Moved Attrition Insight From Monthly to Daily
Myntra’s HR reporting brought shared data and self-service together. It demonstrates the reporting foundation that lets business teams answer questions from agreed definitions.
- Less dependence on the BI team, through Copilot self-service
- About 45%
- Less report generation time
- 65 to 70%
Questions buyers ask.
How is AI used in retail and e-commerce?
In retail and e-commerce, AI is most useful where decisions must happen while the order is still moving: catching SLA exceptions, answering customers and keeping one set of numbers across teams. We bring order events into a real-time KPI board, ground support agents in your own procedures with escalation to a person, and align merchandising, marketing and operations on one semantic model.
Which data platform suits a retail or e-commerce team?
Retail teams need a platform that handles live events as well as batch reporting, and the choice is yours: Microsoft Fabric, Databricks or BigQuery for data, with Azure OpenAI, OpenAI, Anthropic, Gemini or a self-hosted model for AI. Pulse, the Kit accelerator for live order data, runs natively on Fabric or Databricks. We measure legacy workloads before sizing the target estate, so cost follows actual retail demand.
How should a retailer start with real-time data and AI?
Start with the event stream, support workflow or reporting delay that matters most today. Bring it to a 30-minute working session and we scope the next step. A common first step is a live KPI board on one stream of order events, so exceptions are routed while the delivery decision can still change.
What governance does customer-facing AI in retail need?
Customer-facing AI needs clear limits on what it can see and do. Connect support agents only to approved knowledge and the permitted customer record, scope the actions they may take and keep escalation to a person available whenever the customer needs one. On the data side, map store, app and partner feeds into a validated model so reports and agents work from the same checked definitions.
What does a first retail engagement deliver?
A first engagement delivers one working artifact tied to a live decision. Depending on the starting point, that is a live KPI board for order events, a support agent grounded in your procedures, a semantic model for shared retail metrics or an onboarding runbook for store, app and partner feeds. Each phase is fixed price and fixed scope.
What we do.
For Retail & E-Commerce, the four practices run as one loop.
Strategy & Advisory
Judgment before the build. We map what your data can actually do for the business, price the path, and hand you a plan you keep, whichever way you decide.
Modernization
We move your data out of aging systems onto a foundation built for what comes next, without losing a single number along the way.
Data & AI
The work your teams feel every day: live operational intelligence, reporting people trust, and AI that acts inside the systems you already run.
Data Estate Optimization
A running estate quietly gathers waste. We find it, tune it, and turn the savings into the budget for whatever you build next.
Partners and recognition.
Bring the order
that needs attention now.
Bring the event stream, support workflow or reporting delay. We will scope the next step in a 30-minute working session.