"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
- Rob Machado
IndustriesRetail & Commerce
eCommerce & Retail Software Development, Driven by AI
Today's ecommerce leaders face relentless pressure: rising customer expectations, fragmented platforms, and thinning margins. Agile Infoways LLC, a trusted ecommerce development company in USA, helps enterprises build scalable, secure digital storefronts that convert browsers into loyal buyers. From headless architecture to AI-driven personalization, our teams engineer commerce platforms built for resilience and growth. We partner with business leaders to modernize legacy systems, streamline operations, and unlock new revenue channels, turning complexity into competitive advantage, backed by enterprise-grade delivery.
Industry Challenges
The Pressures Facing Modern Retailers
Overstock write-downs, cart abandonment, rising return rates, supply chain volatility, and the demand for hyper-personalisation at scale are squeezing margins and challenging every retail operator regardless of size or channel.
6 key challenges
01
Inventory Overstock & Stockouts
Retailers lose an estimated $1.75T annually to combined inventory imbalances — overstock drives costly markdowns and working capital lock-up while stockouts send customers directly to competitors.
02
Cart Abandonment
Average e-commerce cart abandonment rates sit at 70–75%, representing hundreds of billions in recoverable revenue. Without real-time personalised intervention, the majority of this opportunity is permanently lost.
03
Personalisation at Scale
Customers receive generic email campaigns, untargeted promotions, and irrelevant product recommendations — while the data required for true 1:1 personalisation sits fragmented across CRM, CDP, and transactional systems.
04
Supply Chain Disruption
Port delays, supplier lead time volatility, and climate-related disruptions have made traditional supply planning models unreliable — exposing retailers to stockouts, air freight escalation, and margin erosion.
05
Return Rate Management
Online fashion and electronics return rates averaging 30–40% are consuming up to 65% of item margin through reverse logistics, reprocessing, and inventory depreciation — a problem worsening with every year.
06
Price Optimisation Complexity
Manually managing prices across thousands of SKUs, multiple channels, and competitor price changes is operationally impossible — leaving retailers either underpriced against willingness to pay or overpriced against market.
Retail & Commerce Services
Retail & Commerce ServicesWe Deliver
End-to-end AI and commerce technology solutions for retailers, brands, and marketplaces — from demand forecasting and product discovery to dynamic pricing, customer intelligence, and returns reduction.
Demand Forecasting AI
ML-powered demand forecasting that ingests sales history, promotional calendars, seasonality, weather, and external signals to generate SKU-level forecasts with significantly higher accuracy than statistical baselines.
SKU-level demand forecasting with 94-day horizon
Promotional uplift modelling & event decomposition
Supplier lead time risk & safety stock optimisation
Automated replenishment recommendation engine
Product Recommendation Engine
Real-time personalisation engine that surfaces the right products to the right customer at every touchpoint — homepage, PDP, cart, email, and post-purchase — increasing conversion and average order value.
Collaborative & content-based filtering hybrid models
Real-time session-based recommendation (cold-start capable)
Cross-sell, upsell & bundle recommendation logic
A/B testing framework with revenue-weighted optimisation
Visual Search & Discovery
Computer vision-powered search and discovery that allows customers to find products by image, style attribute, or similarity — dramatically improving discoverability and reducing search abandonment.
Image-based product search & similar item retrieval
Attribute extraction & auto-tagging for catalogue enrichment
Semantic search with natural language query understanding
Shoppable content & social commerce integration
Dynamic Pricing AI
Continuous price optimisation across the full product catalogue — reacting to competitor pricing, demand elasticity, inventory levels, and margin targets to maximise revenue without sacrificing volume.
Real-time competitor price monitoring across channels
Demand elasticity modelling per SKU & category
Markdown optimisation & end-of-season clearance AI
Margin floor guardrails & pricing rule governance
Customer Segmentation AI
Advanced customer analytics that identifies high-value segments, predicts churn, calculates lifetime value, and enables precise targeting of acquisition, retention, and win-back campaigns.
RFM & behavioural segmentation with real-time scoring
Customer lifetime value prediction & tier classification
Churn propensity modelling & early intervention triggers
Lookalike audience generation for paid media targeting
Returns Prediction & Prevention
Predictive models that identify high return-risk orders at the point of purchase — enabling size guidance, product clarity interventions, and fulfilment routing that reduce return rates and protect margin.
Order-level return probability scoring at checkout
Size & fit recommendation with return-rate feedback loop
Product page gap analysis (images, descriptions, reviews)
Returns cost attribution & category profitability reporting
Use Cases
Real Results from Retail & Commerce AI
How retailers and e-commerce brands used our AI to recover abandoned revenue, optimise inventory, increase basket value, and reduce return costs.
01Case Study
31%Revenue from Recommendations
RecommendationsPersonalisationE-Commerce
AI Recommendation Engine Revenue Lift
The Challenge
A mid-market fashion e-tailer with 2.4M active customers relying on manually curated bestseller lists for homepage and email merchandising — generating click-through rates of 1.2% and below-average conversion.
The Outcome
Personalised recommendation engine deployed across homepage, PDP, cart, and email touchpoints. Revenue attributable to recommendations increased 31%. Average order value up 18%. Email click-through rates increased from 1.2% to 4.7%.
02Case Study
18%Inventory Cost Reduction
Demand ForecastingInventorySupply Chain
Demand Forecasting & Inventory Optimisation
The Challenge
A multi-category retailer with 48,000 active SKUs carrying £22M in excess inventory and experiencing 340 weekly stockout events — both driven by statistical forecasting models that could not account for external demand signals.
The Outcome
ML demand forecasting reduced forecast error (MAPE) from 34% to 11%. Inventory holding value reduced by £4.1M (18%). Stockout frequency fell 62%. Working capital freed for investment in growth categories.
03Case Study
4.2xCart Recovery Rate Improvement
Cart AbandonmentCRMRevenue Recovery
Abandoned Cart Recovery System
The Challenge
A home furnishings e-commerce brand with 73% cart abandonment and a single generic recovery email generating 2.1% recovery rate — leaving an estimated £6.8M in recoverable annual revenue unclaimed.
The Outcome
Multi-signal abandonment system using browse history, cart value, and session behaviour deployed personalised sequences across email, SMS, and push. Recovery rate increased from 2.1% to 8.9%. Incremental revenue of £2.9M in the first 12 months.
04Case Study
62%Return Cost Reduction per Order
Returns ReductionSize AIApparel
Returns Prediction & Size Guidance
The Challenge
A DTC apparel brand with a 38% return rate — 23 points above category average — driven by sizing uncertainty. Reverse logistics and reprocessing costs were consuming 58% of gross margin on returned items.
The Outcome
Return probability model and personalised size recommendation engine reduced return rate from 38% to 24% within 8 months. Annual reverse logistics cost reduced by £1.6M. Customer satisfaction scores improved 14 points with faster delivery due to fewer split shipments.
Proven Impact
The Numbers Behind Retail & Commerce AI
Measurable revenue, inventory, and margin outcomes from AI deployments across retail, e-commerce, and omnichannel brands.
Top Result
31%
Recommendation Revenue LiftAcross personalised e-commerce touchpoints
18%
Inventory Cost ReductionVia ML demand forecasting on 48,000 SKUs
4.2x
Cart Recovery ImprovementWith personalised multi-channel sequences
62%
Return Cost ReductionVia predictive size guidance and risk scoring
Ready to Transform Retail & Commerce with AI?
Book a free 45-minute AI discovery session with one of our retail & commerce AI specialists.
Before You Invest
AI in Retail & Commerce
What retailers ask before investing in AI — shopping assistants and support, fraud prevention, in-store computer vision, unifying fragmented data, generative content, and getting started.
8 questions answered
01
Can AI power a shopping assistant and automate customer support?
Yes — conversational AI can guide shoppers to the right product, answer sizing and stock questions, and handle returns or order status across web, app, and WhatsApp. It lifts conversion during the visit and deflects routine support tickets. These conversational AI assistants run 24/7 and escalate complex cases to your team.
02
How does AI catch ecommerce fraud and chargebacks?
AI scores every order in real time against behavioral and device signals, catching fraud and friendly-fraud patterns that rules miss while letting good customers through. It adapts as tactics shift, cutting chargebacks and false declines together. The engine runs on predictive models and anomaly detection trained on your transaction data.
03
Can computer vision cut theft and shrink in physical stores?
Yes — vision models on existing cameras flag shoplifting, sweethearting at checkout, and shelf gaps in real time, alerting staff before losses mount. They also track planogram compliance and footfall patterns. We build these as custom AI models tuned to your store layout, products, and accuracy thresholds.
04
Our customer data is fragmented across CRM and CDP — can AI still personalize?
That fragmentation is the usual blocker, not a dead end. We unify CRM, CDP, web, and transactional data into a clean, connected layer first, so models see each customer whole. Real 1:1 personalization depends on that foundation. Solid data engineering turns scattered systems into a single, usable signal.
05
How can generative AI scale our product content and merchandising?
Generative AI drafts product descriptions, category copy, alt text, and localized variants in minutes, keeps tone on-brand, and refreshes thousands of SKUs at once — work that previously bottlenecked launches. Editors review and approve, not write from scratch. AI automation turns catalog content from a constraint into a throughput advantage.
06
How do we get started, and what’s the ROI?
Start with the metric that hurts most — overstock, abandonment, or returns — prove the lift on one category or channel, then scale. Cost scales with scope, so a focused pilot is far cheaper than a full platform. Many retailers augment their team with dedicated AI engineers to launch quickly.
07
What does ecommerce software development cost?
Ecommerce software development usually starts around GBP35k to GBP80k for a focused storefront or conversion flow and can exceed GBP180k for larger omnichannel platforms with catalogue, fulfilment, loyalty, and analytics. Cost depends on integrations, checkout complexity, and scale. When brands need broader platform delivery beyond AI layers, we often pair it with custom software development.
08
How does AI improve retail personalisation?
AI improves retail personalisation by ranking products, offers, and content against each shopper's behaviour, context, and likely intent instead of showing the same catalogue to everyone. Recommendation engines learn what drives conversion, basket value, and retention over time. Those models usually perform best when fed by clean demand, browsing, and transaction data from predictive analytics.
Client Stories
From Vision to Verified Results
Behind every number - 40% faster deliveries, 60% less admin workload, 50% quicker data processing - is a client who trusted us with a real business challenge. These aren't just demos. They're live products, running at scale sustainably, delivering results clients can measure.
"Great company with great management quality developers were really dedicated to get the job done in a timely cost-effective manner."
- Alexandar Salahsour
"They consistently delivers reliable, high-quality development solutions with exceptional communication, value, and trusted partnership."
- Joe Pellegrino, Jordan Pellegrino
Get In Touch
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