AisleMint.
AI-powered retail intelligence. Made for India.

Good retail instinct.
Even better decisions.

Turn sales history into smarter demand, inventory and promotion decisions with machine learning. Built for Indian retailers, with a vision to make planning as natural as a conversation.

Talk to the founder Meet your retail AI
For the neighbourhood store. And its next chapter.
A clearer view of your next move
AisleMint.Your store
Your planning workspace

Let’s make the next week count.

A bigger discount isn’t always better.

Compare three offers on an everyday essential.

Illustrative
Expected sales₹1,40,400780 packs sold
Contribution₹34,320₹2,320 more than no offer
Sales volume across the week
No offerWith offer
Illustrative daily sales under the selected promotion compared with no offerMonTueWedThuFriSatSun

The 10% offer leaves the most contribution, even though the 20% offer sells more.

Interactive example · not a live forecast
Protect the margin. Make the move.Better decisions start with the whole picture.
Built around your everyday decisions

Understand demand

Plan your stock

Make promotions count

Retail is full of decisions.
Guesswork gets expensive.

You know your customers. You know your shelves. But when demand, stock and offers move together, experience deserves a little more evidence.

“Will this sell next week?”

Last month’s sales don’t tell the whole story. Seasonality, local occasions and planned offers can change what customers pick up next.

“How much should I order?”

Too little means an empty shelf. Too much means cash tied up in stock. The right order needs more than a look at what’s left.

“Did that offer actually help?”

A busy checkout can hide a shrinking margin. More sales only tell half the story when discounts, costs and other products are involved.

Machine learning meets everyday retail

From “we think”
to “here’s why.”

Machine learning forecasts demand and estimates promotion response. Our planning tools connect those estimates with stock, costs and budgets, so your next offer and purchase order work from the same picture.

Promotions with a purpose.

Compare discounts, bundles and buy-more offers before you commit. See the trade-off between volume, revenue and contribution, with stock and budget in view.

Plan scenarios · Compare outcomes · Review recommendations
Which offer earns its place?Example
OfferSalesContribution
10% off Best here₹1,40,400₹34,320
15% off₹1,63,200₹32,640
20% off₹1,79,200₹26,880

A smaller discount can leave more behind.

Know what’s likely to move.

ML models use sales history, seasonal patterns and planned promotions to forecast demand. See a range of possible outcomes, with the uncertainty made visible.

Daily or weekly forecasts · Product and store level

Stock for what comes next.

Turn demand into replenishment suggestions. Bring together stock on hand, incoming deliveries, supplier lead times, case packs, shelf life and your purchase budget.

Replenishment drafts · Stock risk · Budget constraints
Enough for the everydaySee expected stock cover.
Ready for the promotion?Review gaps before demand picks up.

Make the last offer your next lesson.

Look beyond the sales spike. Compare observed sales with an estimated no-offer baseline, and explore shifts to other products and dips after the promotion.

Promotion hindsight · Product spillovers · Post-offer effects

Did demand grow, move from another product, or simply arrive a week early?

These are model estimates. Real-world impact needs retailer-specific validation.

Your data. Connected intelligence.

Bring sales, catalogue and inventory sources together through supported integrations. Trace related records and feed validated data into analytics and shop-specific models: the foundation for contextual AI.

See how it connects
AI with your business in mind

Your next decision
could start with
a question.

“What should I reorder?” “Which offer protects my margin?” “Why did demand change?”

Our vision is to make sophisticated retail planning as approachable as a conversation. We’re bringing together connected business data, predictive machine learning and a planned LLM assistant to help you explore the answer.

Meet the intelligence behind it

Ask AisleMint

Planned experience

Explore an example conversation

What should I order before the weekend offer?

AisleMint · Illustrative answer

In this example, planned demand is 780 packs. You have 420 on hand and 120 arriving before the offer, leaving a 240-pack gap.

Review safety stock, case packs and supplier lead time before deciding what to order.

Evidence to bring together
  • Demand forecast
  • Stock & inbound
  • Supplier lead times

Your next step: review a replenishment draft.

Scripted concept preview, not a live AI assistant. Figures are illustrative; contribution is before campaign costs and taxes.

In the working MVP

ML does the forecasting.

Demand and promotion models learn from sales history. Planning tools compare offers and replenishment options against costs, stock and budget.

In the working MVP

Connected intelligence adds context.

Bring business records and their relationships together. Trace the evidence behind an insight across sales, products, inventory and supported sources.

Our conversational roadmap

LLMs make it approachable.

Our planned assistant will help retailers ask questions, understand model outputs and explore draft plans in plain language, with the underlying evidence in view.

Advanced intelligence.
Everyday economics.

Our design direction pairs specialised ML with carefully selected, cost-efficient language models. We aim to balance answer quality, response time and running costs, so useful AI can reach the neighbourhood store as well as the larger retail team.

Grounded in your data.
Clear about uncertainty.
Always your decision.
From shelf to sale, and back again

A better decision
starts a better cycle.

Start with the data you have. Build confidence in one focused pilot. Keep the retailer in control of every decision.

  1. 1

    Connect your business

    Map your sales, products and stock. Check data quality before it powers a plan.

  2. 2

    Explore the possibilities

    Forecast demand, compare offers and review suggested orders against your constraints.

  3. 3

    Review, run, learn

    Approve the plan, follow the results and use the next cycle to make a more informed choice.

Room for your ambition

One shop or the next hundred.
Every margin matters.

Built with small, medium and larger Indian retailers in mind.
Start with a scope that fits your business.

Your experience.
A clearer view of the numbers.

For kiranas, independent grocers and local supermarkets. Bring structure to everyday decisions, from replenishing essentials to deciding whether that weekend offer makes sense.

Tell us about your store
  • Start with your core products and sales history
  • Review stock needs alongside planned offers
  • Explore OneSales checkout with INR and GST workflows
  • Get a guided assessment of your data readiness
Illustration of a neighbourhood Indian grocery shopkeeper behind his counter
Inspired by India’s everyday retailers. AI-generated illustration.
Our starting point is India

Built for the retailer
behind the counter.

AisleMint began with a simple observation: too many important retail decisions still depend on a guess. What to order. What will sell. Which discount might work.

We’re building an AI-powered retail planning company for India: machine learning for better forecasts, connected intelligence for business context, and a vision for LLM-assisted conversations that make it easier to act on both.

Rupees, stock and real margins

INR planning and GST-aware OneSales checkout workflows.

Your calendar, your context

Plan around local occasions and promotions, with effects validated against your own history.

AI that works with your judgement

Understand the evidence. Explore the options. Approve the decisions that affect your business.

Akash MalasettyFounder, AisleMint
A few things you might be wondering

Let’s make
things clearer.

Have a different question?
Ask Akash directly.

What stage is AisleMint at?

AisleMint is an AI-powered retail planning startup with a working MVP covering ML forecasting, promotion recommendations, inventory planning, connected intelligence and OneSales checkout. A broader conversational planning assistant is on our roadmap. We’re inviting Indian retailers for demos and scoped pilots, with retailer-specific validation and deployment assessment as part of onboarding.

How does AisleMint use AI and machine learning?

Our existing ML models estimate demand, no-promotion baselines and promotion response. Forecasting and optimisation tools turn those estimates into scenarios and draft recommendations. Connected intelligence links business records and evidence. Our roadmap adds an LLM assistant to explain results and help retailers explore plans in plain language.

Can I talk to an AI planning assistant today?

The full conversational planning experience shown here is a concept on our roadmap. Connected intelligence currently provides evidence search and an optional local language-model integration; that integration requires separate setup and evaluation. No cloud LLM is configured by default. We’re working toward conversations grounded in business data and planning-tool outputs, with retailer review before action.

How will you keep AI practical for smaller retailers?

Our approach is to use specialised ML for forecasting and numerical planning, and evaluate efficient language models for questions and explanations. Model selection will weigh usefulness, reliability, response time and operating cost. The aim is accessible retail intelligence; pricing and model choices will be validated during development and pilots.

Can it suggest a promotion if I don’t have one in mind?

Yes. The recommendation workflow compares supported offer candidates against costs, margin constraints, available stock and budget. It can also recommend running no offer when the available options don’t make financial sense. Recommendations are drafts for you to review.

Do I need to replace my existing billing software?

We start by assessing your existing data and supported integration options. The shared data layer can bring in compatible sales, catalogue and inventory feeds through configured connectors or authenticated APIs. Connection work depends on your system; universal plug-and-play compatibility is not assumed. OneSales is available as an integrated checkout workflow.

What data do I need to get started?

Start with product-level sales history, regular and selling prices, product identifiers and unit costs. Promotion history helps with offer evaluation; stock balances, supplier lead times and inbound deliveries support replenishment. We’ll assess completeness and the amount of history available before recommending a pilot scope.

Are the forecasts and profit improvements guaranteed?

No. Forecasts are estimates, and recommendations depend on your data, costs and execution. We show assumptions and uncertainty and validate models for the retailer. The examples on this page are illustrative, not customer results. Estimated promotion impact from historical data is not proof of cause and effect.

How do pricing and early access work?

We’re discussing early retailer pilots directly. Scope and pricing depend on your stores, data sources, integration needs and planning requirements. Email the founder for a walkthrough and a conversation about fit; there is no payment or signup commitment on this page.

Help shape the next chapter of retail AI

Let’s make every aisle
a little more informed.

Tell us about your store, your data and the decisions
you’d like AI to help you understand.

Let’s talk AisleMint A real conversation with the founder. No sales maze.

For retailers.
For better decisions.
For India.

akashm@aislemint.com