Agentic Commerce on AWS
We build production eCommerce agents on AWS — support, inventory, merchandising, margin, returns — and make your catalog legible to the agents that now do the shopping. Tool boundaries on every write, evals before launch, and a human on anything that moves money.
Why teams choose FactualMinds
The Shift
Most stores are only thinking about one of them. We build both.
Support and WISMO, inventory and reorder, merchandising and margin, returns and fraud, B2B quoting — and the multi-agent layer that coordinates them. Each one scoped to a single job, with a tool boundary around every write.
ACP, UCP and MCP surfaces so ChatGPT, Gemini and every other shopping agent can read your catalog, compare it honestly, and complete a checkout. This is the half most stores have not started.
Agents We Build
Every one of these has a published build guide behind it — architecture, tool catalog, approval gates, and what breaks when you skip one.
Order lookup, tracking, and returns handled end to end — with a hard escalation path the moment the agent is out of its depth.
What to reorder today, what is about to stock out, and what has quietly become dead stock — answered against your live data, not last month's report.
Pricing intelligence and margin analysis that surface the SKU losing money before the monthly close does.
Quote requests, reorders, and account management for buyers who do not shop like consumers — and will not tolerate a chatbot.
Refund investigation and return-abuse detection with the evidence attached, so a human approves a decision rather than guessing at one.
When one agent is not enough: a supervisor, shared memory, and clear ownership per action so agents do not fight over the same order.
A golden dataset per agent and a pass bar it clears before it ever faces a customer.
Cedar authorization on anything that mutates an order, a price, or a customer record.
Refunds, price changes, and purchase orders route to approval by design — not by exception.
Token budgets and per-conversation ceilings, so Black Friday does not arrive as an inference bill.
An honest list
We say no to the engagements that don’t fit so we can say yes to the ones that do.
The Platform Underneath
Agents are the visible half. These are the AWS practices that keep them standing on the day it matters.
AgentCore Runtime, Gateway, Memory, Identity, and Observability — the managed substrate agents run on, so you are not maintaining an orchestration framework as well as a store.
CloudFront at the edge, ElastiCache on catalog and session reads, Aurora with replicas across AZs. Built for elasticity, load-tested two weeks before the campaign — not on the day.
Cardholder data isolated in its own account and VPC, WAF managed rules on the checkout path, least-privilege IAM with Secrets Manager. Compliance as an architecture decision.
Token budgets, model selection by task, provisioned versus on-demand break-even analysis, and cost observability wired in from sprint one.
The join keys agents actually need — orders, customers, products, inventory — in a lakehouse they can query with evidence rather than guess at.
Peak-season readiness reviews, managed auto-scaling, and round-the-clock monitoring for platforms where an hour of downtime is a number the board sees.
What We Have Published
We would rather be judged on work that is already public than on a case study you cannot verify.
Part Field Guide
A part-by-part guide to building eCommerce agents on AWS, with copyable artifacts.
Free AWS Calculators
Cost and readiness tools, including Bedrock AgentCore pricing. No email gate.
AWS Certifications
Deep, cross-domain AWS expertise across our consulting team.
Clients Served
Enterprises, SaaS companies, and startups across 12+ industries.
Who We Serve
Agentic commerce is where we go deepest. The AWS practice underneath it serves every industry we have always served.
Peak season should be your biggest win, not your biggest outage. Agents that run the store, a catalog the shopping agents can read, and infrastructure that has already shipped Black Fridays.
Your cloud bill scales faster than your revenue. We right-size your multi-tenant infrastructure, cut idle spend, and architect for growth — so margins improve as you scale.
HIPAA compliance and innovation feel like opposites — until you have the right AWS architecture. We build privacy-first platforms that protect patient data and accelerate clinical workflows.
Regulators don't accept "the cloud is secure by default." We build PCI DSS and SOC 2-aligned AWS environments with real-time fraud detection and audit trails that survive scrutiny.
AWS credits don't last forever, but bad architecture costs do. We help startups spend credits wisely, build investor-ready infrastructure, and avoid the technical debt that kills growth.
When 50,000 students log in at 8am, you need infrastructure that holds. We build FERPA-compliant, auto-scaling AWS platforms and add AI-powered learning features that actually work.
We wanted to integrate generative AI into our product search experience, but model costs were skyrocketing. FactualMinds built a secure, cost-aware GenAI stack using Amazon Bedrock Agents Classic that reduced our inference costs by 40% while keeping all data within our AWS environment.
Megan Lawrence
VP of Digital Innovation — TargetBay
After a customer audit revealed gaps in our cloud posture, we turned to FactualMinds. Their AWS security assessment uncovered misconfigured IAM roles and open endpoints. In just two weeks, they remediated every risk and gave us a security baseline aligned with SOC 2 and ISO 27001.
Chris Delaney
CTO — Wonderfeel
Deploying GenAI in a HIPAA-regulated environment felt daunting until FactualMinds stepped in. They designed a privacy-first AI workflow using Amazon Bedrock with custom encryption layers. The result: a secure clinical documentation tool that cuts case summary time in half without touching patient data.
Dr. Nila Rao
Chief Innovation Officer — Little Sponges
How We Work
One agent in production beats five in a slide deck. We ship in that order.
We check whether your data, systems, and approval paths can support an agent at all — the failure mode that kills most eCommerce AI projects before the model is even chosen.
We pick the single workflow with the clearest ROI, define its tool catalog and write boundaries, and agree the human approval gate before a line of code exists.
Built on AgentCore with a golden dataset, a pass bar, cost caps, and an audit trail. It goes live when it clears the bar — not when the sprint ends.
Add the next agent against the same guardrails, then the supervisor layer that coordinates them. Your team owns the IaC and the runbooks when we leave.
Common Questions
Answers to what eCommerce and engineering leaders ask most before starting an agent engagement.
Four entry points into the field guide: the automation map, the architecture brief, the ROI priority score, and the readiness check.
Fifteen store automations scored as traditional vs agent — not 15 week-one builds. Reuse Gateway ~180 ms to ~95 ms and the ~$791/mo AgentCore silhouette; Baymard cart abandonment sits at 70.22%.
AWS AI agents for eCommerce on AgentCore Harness (GA June 17, 2026), Strands 1.0, and Bedrock Converse — not a native Shopify connector. Reuse Gateway ~180 to 95 ms and ~$791/mo at 50K sessions. Next.js is the ops dashboard, not the runtime.
Score automations as Volume × Frequency × Effort × Impact × Feasibility — not a promised return. Model the ~$791/mo platform silhouette at 50K sessions; reuse Gateway ~180 to 95 ms.
Score org-wide AI agent readiness /30 — data, integration, process, governance, priority. Below 16, skip writes. Reuse Gateway ~180→95 ms and ~$791/mo at 50K sessions.
Start with a readiness check. We will tell you which agent to build first — and which one you are not ready for yet.
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