Consumer & Retail
Margin won and lost in the moments your systems can't see.
The Challenge
The average retailer operates across 6+ disconnected systems - none of which talk to each other in real time.
The Solutions
ML-Based Demand Forecasting & Inventory Optimisation
From static replenishment rules to SKU-level, store-level intelligence - in weeks,
Pain Point
Static replenishment rules cannot respond to real-time signals, producing chronic overstock on slow movers and out-of-stocks on high-velocity lines.
Our Solution
ML models ingest POS history, promotional calendars, weather and local event data to generate SKU-level, store-level demand forecasts - driving automated replenishment and allocation without manual intervention
Impact
ML & LLM-Based Personalisation
Turn your loyalty and transaction data into revenue - automatically.
Pain Point
Retailers send identical promotions to all customers despite holding rich loyalty and transaction data - leaving significant revenue and engagement on the table.
Our Solution
Propensity models score each customer's purchase likelihood by category. LLMs generate personalised product descriptions, email copy and push notifications tailored to individual transaction history - deployed automatically at scale.
Impact
ML-Based Dynamic Pricing & Markdown Optimisation
Stop leaving margin on the table. Price dynamically - by product, channel and location.
Pain Point
Category managers price manually using margin targets and gut-feel benchmarks. Markdown timing is intuition-based, leading to margin loss or clearance failure on end-of-season stock.
Our Solution
ML models set prices dynamically by product, channel and location using demand elasticity, real-time competitor pricing, stock levels and sell-through trajectory. Markdown models identify the optimal reduction point and depth automatically.
Impact
Agentic Customer Service
Resolve more tickets. Deploy fewer agents. Deliver better experiences.
Pain Point
High volumes of order queries, returns and complaints require large contact centre teams, resulting in long wait times and inconsistent resolution quality across channels.
Our Solution
LLM agents resolve tickets end-to-end across chat, email and voice - processing returns, issuing refunds, answering order queries and escalating complex cases to human agents with full context pre-populated.
Impact
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