As AI challenges traditional e-commerce journeys, more than ever, brands need the kind of stickiness that keeps customers engaged.
The glue comes in two layers:
- Strengthen brand visibility and trust in front of third‑party AI agents that now broker discovery and selection
- Build on‑site agentic experiences that keep customers engaged and loyal to your own channels
The GAIN Platform for Agentic Commerce gives your teams a single, production‑grade foundation to do both: an agent‑ready, answer engine optimized (AEO) catalog and content layer for third-party agents to surface your brand, and a composable agentic CX stack for personalized discovery, conversational shopping, payments, and post‑purchase engagement capabilities that preserve relationship ownership and loyalty at scale, all wired into MCP and A2A agentic protocols, with connectors to UCP, ACP, AP2, and more.
Most commerce platforms are built for the store you had
It is not enough to bolt a chatbot onto legacy commerce. Most stacks are under‑represented in external AI agents, constrained by shallow personalization, and limited by architectures that cannot support end‑to‑end agentic journeys.
Catalogs, policies, and signals built for human shoppers, not AI agents.
Product data is inconsistent, returns and warranties are buried in PDFs, and reviews are not exposed in structured form. AI agents cannot reliably parse or compare your offers, so competitors with cleaner, machine-readable data win the recommendation even when your value is stronger.
Shoppers expect sites and agents to recognize intent, history, and context within a single session and across journeys.
Many retailers still deliver static search and rules‑based personalization, with no brand‑controlled agents to guide discovery or service on owned channels. Returning buyers are treated like first‑timers, a search for “chicken dinner for 2” returns “no results found” instead of ingredients, cookware, recipe steps and a wine pairing on promotion, and high‑value conversion moments pass without timely, contextual offers.
Agentic CX stalls when legacy platforms block agent workflows from running at scale.
Traditional commerce stacks were engineered for page‑based, human‑driven flows, not autonomous agents coordinating end-to-end journeys. Monolithic systems, where experience capabilities sit on separate rails, and checkout flows lack clean support for agentic protocols, produce agentic pilots that work in demos but fail in production.
Ready to build AI-powered commerce experiences that drive conversions and loyalty?
Built to solve every layer of the problem
Make your catalog agent‑ready
Expose clean, outcome‑driven signals to AI agents
Turn your fragmented catalog data into an AI‑ready asset that external agents prefer to consume. Enrich product data, policies, and proof points once, then expose them through consistent schemas that answer engines, shopping agents, and agentic protocols can actually trust.
The result: AI agents can reliably understand, compare, and recommend your brand.
Orchestrate AI‑driven experiences
Enable every surface to behave like an intelligent guide
Unify semantic discovery, personalized merchandising, and conversational agents into a single context layer that adapts in real time. Session intent, behavior, and customer history flow across search, browse, recommendations, shopping assistants, customer support, and rich customer-lifecycle messaging (e.g., order and delivery updates, support and returns, replenishment reminders, loyalty and retention offers), so customers get consistent, context‑aware guidance instead of isolated interactions.
The result: Each interaction becomes a tailored path, not another generic page view.
Run agentic journeys on a composable, protocol‑aware stack
Give agents a runtime that matches their ambition
Refactor your commerce stack into MACH‑aligned (Microservices, API-first, Cloud, Headless), composable services that agents can orchestrate safely. Core capabilities like search, pricing, inventory, checkout, and post‑purchase workflows are exposed through APIs and events, then wired into MCP, A2A, UCP, ACP, and AP2 so shopping agents, business agents, and internal copilots can execute end‑to‑end tasks under clear guardrails.
The result: Complex architecture is production-ready for agentic journeys that survive scale, audits, and the next protocol wave.
Platform coverage
Agentic commerce across the customer journey
Purpose-built AI capability at every stage, not generic augmentation layered on existing workflows.
In production. Measured results
+7% total revenue
Challenge: 450k SKUs, messy data, and manual rules made relevance and merchandising impossible to scale.
Solution: Unified marketplace data and powered discovery with Vertex AI Search for Commerce, Catalog Enrichment, and MXP‑driven merchandising automation.
Impact: 50% less manual merchandising, 7% total revenue lift, and 25% YoY online growth in peak season.
+17% conversions
Challenge: Keyword search failed on intent-based queries like “How do I stay cool at night?”, and in‑store Sleep Expert® knowledge was scattered and hard to access.
Solution: Built a triage-first discovery layer on Vertex AI Search for Commerce + MXP, plus SleepExpert.AI as an AI knowledge, sales, and training agent for in-store associates.
Impact: 17.5% conversion lift, 2% AOV increase, and 18% sales share growth for a strategic brand.
+11% add-to-cart rates
Challenge: High zero-result rates and inconsistent attributes blocked discovery across a Fortune 100 B2B catalog.
Solution: Replaced legacy search with Vertex AI Search for Commerce and Gemini-powered Catalog Enrichment to harmonize attributes and visuals.
Impact: 11% more add-to-cart events, 4% revenue-per-visitor growth, and 86% of zero results eliminated on key categories.
95% faster response times
Challenge: Store teams were overwhelmed with messaging and calls for fitment and availability checks.
Solution: Built a GenAI WhatsApp agent using Gemini and ACES/PIES enrichment to handle the full journey from fitment search to order placement.
Impact: Orders placed in 3–5 seconds, 24/7 digital labor across languages, and freed store staff for higher-value work.
Your enablement journey to agentic commerce
A structured, low-risk path from readiness assessment to production and continuous improvement.
Grid Dynamics supports every phase, from assessment through production operation.
Agentic readiness assessment
Timing: 1–2 weeks
- Assess existing tech stack and APIs
- Audit catalog quality and data gaps
- Map customer journey for agentic use cases
- Define KPIs and success metrics
- Scope and prioritize the pilot use case
Enablement and implementation
Timing: 2–12 weeks
- Enrich product catalog and descriptions
- Expose commerce capabilities through agent-ready interfaces
- Build conversational UX across web and mobile
- Integrate search, discovery, and shopping workflows
- Connect checkout, messaging, customer service, and support flows
Production rollout and continuous improvement
Timing: Ongoing
- Deploy in phases
- Monitor conversion, AOV, CSAT, and agent quality
- A/B test agentic vs. traditional flows
- Build closed-loop feedback into optimization
- Expand incrementally into new use case
Ready to build commerce that doesn’t stand still?
Our Forward Deployed Engineers will assess your current commerce stack and map a path to measurable outcomes.