Agentify your retail enterprise with a Decision Execution Layer
Retail runs on hundreds of decisions per shopper. We sit between your Systems of Record OMS, PIM, POS, ERP, CDP, ecommerce platform, marketplaces, WMS and CRM and your agents, so every discovery, pricing, merchandising, promotion, fulfilment and returns decision carries context, governance, and memory.
Platform-agnostic approach ensures the best solution for your requirements.
Retail is full of agents in silos. We give them shared decision memory.
Pricing agents on the ecommerce platform. Merchandising bots on the PIM. Shopper chat on the storefront. Review triage scanning marketplaces and CDP signals. All making decisions without a shared Context Object no entities, no state, no record of which decisions worked. The Decision Execution Layer is the reasoning tier that sits between your OMS, PIM, POS, ERP, CDP, ecommerce platform, marketplaces, WMS and CRM and the vertical agents that act on top a Context Repo with data, governance and knowledge graphs that turns every order, basket and return into feedback for the next decision.
Margin-aware pricing across ecommerce, POS and marketplaces
Shared Context Repo across OMS, PIM, CDP and CRM
Hyper-personalised merchandising with outcome tracking
Governed returns and review recovery across marketplaces
Map, Build, Compound
Map
We map the retail stack OMS, PIM, POS, ERP, CDP, ecommerce platform, marketplaces, WMS, CRM and the Systems of Knowledge (Notion, Slack, Confluence) around them. We pinpoint where decisions are made today, where Context Objects are missing, and where outcomes never flow back.
Build
We build the Decision Execution Layer the Context Repo, knowledge graphs and governance rules then wire vertical agents (pricing, merchandising, returns, fulfilment) and general agents on top. Every Context Object carries entities, state, decisions and outcomes.
Compound
Outcomes feed back. Markdown decisions inform next week's prices. Cross-sell hits and misses tune timing. Return and review escalations sharpen governance. The Decision Moat deepens with every order.
Why lemongrass.dev for retail
Retail is full of fragmented systems and shallow AI pilots. You need a partner who treats the enterprise as an operating system and inserts the decision layer that makes agents informed, governed, and compounding across store, ecommerce and marketplaces.
We align with the metrics that matter: GMV, margin per order, conversion and AOV, sell-through and markdown risk, return rate, CLV, and brand health across marketplaces and social.
We wrap your existing OMS, PIM, POS, ERP, CDP, ecommerce platform, WMS, CRM and marketplace integrations SaaS stays, but gets re-bundled around agentic use cases.
Guardrails are baked into the execution layer, not bolted onto individual agents. Governance rules are first-class citizens inside the Context Repo.
Map, Build, Compound from decision-layer consulting through agent development, Context & Knowledge Graphs, and agent onboarding into live merchandising, pricing, fulfilment and CX ops.
Pre-AI vs agentified retail
Discovery: flat category grids on the storefront and marketplaces become prioritised, personalised recommendations drawn from Context Repo history.
Pricing: static, rule-based prices and markdown cadences become dynamic, margin-aware decisions that learn when promotions grow revenue and when they burn margin.
Order: a simple checkout on the ecommerce platform, POS or marketplace becomes a logged decision entity, state, decision, outcome feeding the next one.
Returns and fulfilment: hardcoded policy in the OMS and WMS becomes optimisation for customer lifetime value, inventory position and carrying cost.
CX: static responses become continuous improvement via outcome tracking across CX tools, marketplaces and social low-impact issues handled in-brand, high-risk escalated with full context.
The Decision Moat: every order, basket, promotion and return becomes a compounding advantage competitors cannot replicate.
Near-term agentic use cases for retail
Margin-Aware Pricing
Pricing agents on top of the ecommerce platform and POS reason over the Context Repo current demand, sell-through, competitor pricing, inventory position, past outcomes and decide when to mark down to win revenue versus when to hold price to protect margin.
Price and promo decisions become dynamic and margin-aware across storefront, POS and marketplaces. Every call is logged so the next one is sharper.
Hyper-Personalised Merchandising
Product recommendations, bundles, cross-sell, loyalty-tier offers not only what to surface but when. Agents read the Context Object across CDP, CRM and OMS and trigger offers at the moment most likely to convert.
On-site recommenders, basket assistants and email agents share one Context Repo. Hits and misses feed back into the decision memory.
Agentic Support
Shopper queries across website chat, WhatsApp, email and phone order status, size and fit, returns, loyalty handled by brand-consistent agents drawing on the Context Repo for order history, preferences and basket state. Complex or high-value issues escalate to a human with the full Context Object attached.
Every ticket becomes a logged decision what was asked, what was offered, what worked sharpening the next response.
CX Monitoring
Agents watch the full shopper journey OMS alerts, basket abandonment, store visit signals, post-purchase surveys, return reasons and flag friction early. Issues are routed to the right team before they become a negative review, with the full Context Object attached.
CX becomes a live signal on the operating floor, not a post-purchase post-mortem.
Social & Marketplace Monitoring
Agents scan marketplace reviews, Trustpilot, Reddit and X around the clock. Sentiment, recurring themes and SKU-level issues land in the Context Repo with the shopper, product and order linked in so recovery can start before the next review goes live.
Brand health moves from a monthly dashboard to a live, SKU- and store-level signal agents can act on.
AI-Led Operations
Merchandising, allocation, replenishment, fulfilment and store ops run off a shared Context Repo. Agents sequence tasks based on sell-through, stock-outs, promo calendars and SLA risk humans approve edge cases, agents handle the rest. Every completed task feeds back into the next day's plan.
Daily operations stop being run from spreadsheets and shift handovers and start compounding.
Decision layers, proven in production
How retail and enterprise teams put the Decision Execution Layer between Systems of Record and their agent fleet and what compounded after.
Let’s map your Decision Execution Layer
Tell us which retail systems your agents touch OMS, PIM, POS, ERP, CDP, ecommerce platform, marketplaces, WMS, CRM. We’ll map the decision points, design the layer, and start compounding.