Demand forecasting
SKU-by-store or SKU-by-pincode forecasts that factor in promotions, seasonality, weather and marketplace signals.
Retail & D2C
AI for retailers and direct-to-consumer brands.
From the first click to the last mile, we help brands forecast demand, personalise every touchpoint and price with confidence, turning scattered commerce data into margin.
Forecast accuracy0%
SKUs enriched0
Stockouts--
The challenge
What is holding retail and D2C brands back.
01
Inventory in the wrong place
Too much of what does not sell and too little of what does, because forecasts are spreadsheets built on last year.
02
Rising acquisition costs
Paid channels keep getting more expensive, so retention and lifetime value matter more than ever.
03
Messy catalogues
Inconsistent titles, attributes and images hurt search, recommendations and marketplace listings.
04
Data spread across too many tools
Storefront, marketplaces, ERP, CRM and ads platforms each hold part of the truth.
Where AI pays off
Use cases we build and run.
Each one is scoped to a measurable business outcome and shipped to production, not left as a proof of concept.
Personalisation
Recommendations, ranked search and triggered journeys tuned to each shopper's intent.
Pricing and markdowns
Elasticity models that set prices and clearance plans to protect margin without killing sell-through.
Catalogue intelligence
Vision and language models that write titles, fill attributes and tag images at scale.
Customer service agents
Agents that resolve order, return and refund queries end to end, handing over only what needs a human.
Unified commerce data
One customer and product view across D2C, marketplaces and offline stores.
Our approach
How we deliver in Retail & D2C.
The same disciplined loop we use everywhere, adapted to the data, risks and rhythms of your industry.
Built responsibly
- DPDP Act, 2023 consent for marketing data
- Consumer Protection (E-Commerce) Rules
- Marketplace content and listing policies
- Brand safety review for generated content
Typical stack
01
Weeks 1 to 2
Commercial discovery
We find the levers that move gross margin and repeat rate.
- Unit economics review by channel and category
- Data audit across storefront, ERP, marketplaces and ads
- Ranked backlog of use cases with expected margin impact
02
Weeks 2 to 3
Design for the season
Plans are timed around your trading calendar, not ours.
- Unified data model for customers, orders and products
- A/B test design and success metrics for each use case
- Integration plan with your storefront and ERP
03
Weeks 3 to 8
Build and test live
Everything ships behind experiments so impact is measured, not assumed.
- Forecasting and recommendation models in production
- Merchandising and marketing dashboards teams actually use
- Weekly readouts on lift versus control
04
Ongoing
Scale across channels
Winners roll out to more categories, regions and channels.
- Automated retraining ahead of peak seasons
- Playbooks for merchandising and CRM teams
- Cost and performance monitoring
Case studies
Results from the field.
Case study 01
Fashion D2C brand
Buying less, selling more with SKU-level forecasting
The challenge
Buyers placed orders on gut feel and last season's sell-through, leaving deep discounts on slow movers every quarter.
What we did
We built size-level demand forecasts that combine web traffic, returns and marketplace data, feeding an open-to-buy tool for the buying team.
Results
31%fewer stockouts on core styles
22%less end-of-season inventory
+2 ptsgross margin
Case study 02
Beauty and personal care brand
Personalisation that lifted repeat purchase
The challenge
Every customer received the same emails and on-site recommendations regardless of skin type or purchase history.
What we did
We unified CRM and order data, trained a replenishment and recommendation model, and wired it into email, WhatsApp and the storefront.
Results
11%higher average order value
18%increase in 90-day repeat rate
Case study 03
Omnichannel electronics retailer
A catalogue that writes itself
The challenge
Thousands of new SKUs a month were listed with inconsistent titles and missing specs, hurting search and marketplace ranking.
What we did
A vision and language pipeline now extracts specs from supplier sheets and images, writes channel-specific listings and routes low-confidence items for review.
Results
90%of listings generated automatically
5xfaster time to list
Case studies illustrate the kind of engagements we run. Details are generalised and figures are indicative, not guarantees of results.
Other industries
Explore more of our work.
Financial Services
Credit risk, fraud detection, KYC automation and AI-assisted operations.
Healthcare & Life Sciences
Clinical documentation, claims processing and patient-journey analytics.
Manufacturing & Supply Chain
Predictive maintenance, quality inspection and inventory optimisation.
SaaS & Technology
AI features inside your product, usage analytics and support automation.
HR & Talent
Screening, matching, workforce planning and people analytics at scale.
Let's talk about Retail & D2C.
Tell us where you are. We will come back within one business day with a point of view, not a sales deck.