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Personalization and recommendations
Real-time personalization with merchant-curated guardrails, A/B test infrastructure, and the explainability dashboards merchandising teams actually use.
AI & Machine Learning × Retail & E-Commerce
AI for Retail & E-Commerce.
Production AI for retail: personalization, demand forecasting, vector search, customer service automation, and content generation. Privacy-respecting by design, peak-season-tested, integrated with the commerce stack rather than bolted on.
- PCI-DSS
- Aligned
- GDPR · CCPA
- Compliant
- Global Privacy Control
- Honored
- SOC 2 Type II
- In flight
The reality
Retail AI fails when it competes with merchandising rather than working with it.
The pattern across retail AI engagements: a personalization engine that runs against stale data; a recommendation algorithm that surfaces clickbait at the cost of brand identity; a demand forecast that ignores promotion calendar reality; a vector search that's slower than the legacy keyword search it replaced; and a customer-service AI that escalates to humans on the easy questions. Retail AI succeeds when it works with merchandising, when data freshness matches operating cadence, and when the engineering meets peak-season reality.
Prosigns ships retail AI engineered against retail's actual rhythms. Personalization with merchant-curated guardrails and explicit data-freshness SLAs. Demand forecasting that respects promotion and price-elasticity reality. Vector search at sub-100ms. Customer service AI grounded in product catalog and policies with explicit refusal patterns. Privacy-respecting by design, peak-season-tested through chaos engineering, integrated with the commerce stack rather than bolted on.
Where it ships
6 workloads, scoped to retail & e-commerce.
Concrete applications where ai & machine learning unlocks measurable value inside retail & e-commerce delivery constraints.
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+22%
forecast accuracy
Demand forecasting
Multi-horizon demand forecasting respecting promotion calendar, price elasticity, and seasonal patterns. Integration with replenishment and capacity planning.
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Vector and semantic search
Semantic search with embeddings, faceted filters, and merchandising overrides. Sub-100ms latency at peak load with the operational discipline catalog volatility requires.
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−47%
ticket volume
Customer service automation
Tier-1 response automation grounded in product catalog and policies. Refusal patterns, explicit escalation gates, and the integration with human agent tooling that actually works.
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Content generation
Brand-consistent product descriptions, marketing copy, and merchandising assets with style guardrails, fact-checking against approved sources, and editorial review queues.
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Pricing and promotion AI
Price elasticity modeling, promotion impact prediction, and dynamic pricing. With explicit guardrails against fair-pricing failure modes and full audit trail.
How we engage
4 phases, named in the SOW.
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to retail & e-commerce delivery.
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Merchandising-respecting design
Discovery starts with the merchandising team, not with the data science team. We learn the product hierarchy, the promotion calendar, the editorial guidelines, and the fairness constraints before we touch a model. AI use cases land against merchandising reality.
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Privacy-respecting personalization
Consent management integrated with personalization at the data layer. Honored Global Privacy Control, regional differences (GDPR / CCPA / PIPEDA) handled architecturally, explicit retention per data class.
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Peak-season-tested engineering
Models sized for peak load with explicit headroom, chaos-engineering rehearsals 4–6 weeks before peak, and the FinOps discipline that returns the cloud bill to baseline post-peak.
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Operating cadence
Pre-peak readiness reviews, war-room cadence during peak weeks, and post-peak retrospective. Quarterly model retraining against drift and seasonal patterns. Most engagements continue under Managed Services through multiple seasons.
Capabilities
What’s in scope.
- Personalization and recommendations with merchant-curated guardrails
- Demand forecasting with promotion calendar and price elasticity awareness
- Vector / semantic search with sub-100ms latency at peak
- Customer service automation: refusal patterns, escalation, agent tooling
- Content generation: product copy, marketing assets, merchandising aids
- Pricing and promotion AI with audit trail and fair-pricing guardrails
- Computer vision: visual search, virtual try-on, planogram compliance
- Privacy-respecting design: GPC honor, regional data handling, retention
Stack
Tools we use in production.
- Foundation models
- Anthropic ClaudeOpenAI GPTAWS BedrockCohere
- Recommendations
- Vertex AIPersonalizeCustom embedding pipelinesAlgolia AI
- Vector search
- PineconeWeaviateQdrantAlgoliaOpenSearch
- Forecasting
- ProphetDeepARCustom XGBoost / LightGBMNixtla
- Commerce platforms
- Shopify PluscommercetoolsBigCommerceSaleor
- Privacy & consent
- OneTrustIubendaCustom consent infrastructure
Compliance overlay
Frameworks we engineer to.
Every retail & e-commerce engagement carries the evidence collection that procurement and audit teams expect on day one.
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PCI-DSS
AI workloads designed to keep PCI scope minimal: tokenization at the network edge, segmentation that isolates AI workloads from cardholder data flow, and the QSA-supportable evidence pipeline running continuously.
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GDPR / CCPA / Global Privacy Control
Consent management integrated at the data layer. GPC signals honored. Regional differences handled architecturally rather than through cookie-banner fixes. Explicit retention per data class. Right-to-erasure flows engineered.
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Fair pricing and consumer protection
Pricing AI engineered against fair-pricing failure modes: price discrimination, predatory pricing, dynamic-pricing transparency. Audit trail per pricing decision; explicit guardrails on protected-class signals.
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Marketing claims and content
Generated content reviewed against brand standards, factual claims fact-checked against approved sources, and explicit refusal patterns for compliance-sensitive content. Editorial review queues for high-impact assets.
Selected work
Quantified outcomes, not adjectives.
Where this fits
Open the parent practice or industry hub.
Common questions
Asked before the first call.
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Can your personalization survive peak season?
Yes: peak-season readiness is part of the discipline. Models sized for peak with explicit headroom, chaos-engineering rehearsals 4–6 weeks before peak, war-room cadence during peak weeks, and a post-peak retrospective. Most clients see 5–10x peak personalization throughput vs their prior architecture.
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How do you handle privacy in retail AI?
Engineered at the data layer, not the cookie banner. Consent management integrated with personalization, regional differences (GDPR / CCPA / PIPEDA) handled architecturally, Global Privacy Control honored, and explicit retention per data class. Right-to-erasure flows engineered into the data substrate.
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Can the AI work with our merchandising team?
Yes, designed for it. Recommendation engines surface merchant-curated picks alongside personalization, with explicit blending parameters merchandising can tune. Explainability dashboards that surface the 'why' behind recommendations. We don't ship algorithms that override editorial judgment without a path back.
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Vector search vs traditional search, which is right?
Both, layered. Vector / semantic search wins for natural-language queries and product discovery; traditional faceted search remains essential for filter-driven shopping. We build hybrid retrieval that uses both with explicit ranking blends, with merchandising overrides for high-priority terms.
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What about fair pricing and consumer protection?
Pricing AI engineered against fair-pricing failure modes: explicit guardrails on protected-class signals, audit trail per pricing decision, transparency frame appropriate to the jurisdiction. We tell you when a pricing use case is fundamentally regulator-sensitive and design alternatives.
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What does a retail AI engagement cost?
Discovery: 3–5 weeks, $40K–$120K. Personalization / recommendations: 4–8 months, $300K–$1M. Demand forecasting program: 4–8 months, $300K–$1M. Customer service AI: 4–6 months, $250K–$700K. Multi-use-case AI programs: $1M–$3M+. Managed Services: $30K–$150K monthly retainer.
Talk to us
Bring us a hard AI & Machine Learning problem. We’ll bring a senior Retail & E-Commerce engineer.
A senior engineer plus the CORTEX department lead joins the first call, both with prior retail & e-commerce delivery experience.