Understand
Read the request and the records behind it — the order, the SKU, the account — before proposing a next step.
Production AI agents
AI agents that run the work you already do
We find the workflows worth automating, then build agents that use your systems, ask for approval when money moves, and run in production. eCommerce is where we go deepest. AWS engineering is how they stay up.
- A customer intent arrives.
- The agent runs on Amazon Bedrock AgentCore.
- Read tools fetch catalog, orders and status.
- Write tools prepare a refund or place an order.
- Anything that moves money stops at an approval gate, authorized with Cedar.
- Approved work runs in production.
Why teams choose FactualMinds
AWS Select
Tier Services Partner
64
Part Field Guide
124
Free AWS Calculators
100+
Clients Served
SOC 2
Security Aligned
What could you automate?
Five kinds of work agents already take on
If the same tickets, reports, or decisions repeat every week, an agent can usually take the first pass. eCommerce is where we have published the deepest build guides.
Customer support
The same WISMO, tracking, and returns questions every day. An agent looks up the order, answers from live data, and hands off the moment it is out of its depth.
Customer support agent →
Sales & B2B
Quote requests, reorders, and account questions from buyers who will not tolerate a chatbot. An agent drafts the next step; a human still owns the relationship.
Sales & B2B agent →
Operations
The morning pile: exceptions, stalled orders, and the work nobody wants to triage. An agent ranks what needs a human today — it does not silently rewrite the warehouse.
Operations agent →
Inventory
What to reorder today, what is about to stock out, and what has quietly become dead stock — answered against live data, not last month's report.
Inventory agent →
Knowledge & catalog
If orders, customers, products, and inventory cannot be joined, an agent will guess. This is the data layer that makes every other agent honest.
Knowledge agent →
How an agent works
Not a chatbot. A worker with tools and a stop button.
Six steps, in this order. Skip approval or the stop button and you have a demo, not a production agent.
Decide
Choose the action a trained operator would take for this case, inside a written policy, not a free-form guess.
Use tools
Call only the systems it is allowed to use: ERP, CRM, warehouse, catalog. No open-ended access to “whatever looks useful.”
Act
Complete the step — reply, flag, draft, queue — and leave an audit trail of what it did and why.
Ask for approval
Refunds, price changes, purchase orders, and anything else that moves money wait for a human. By design, not by exception.
Learn
Every miss goes back into the eval set. The agent ships again only when it clears the pass bar — not when the sprint ends.
Where we go deepest
Agentic commerce has two halves
Most stores are only thinking about one of them. We build both — after the workflow is worth automating.
Agents you run
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.
See what we build
Agents that buy from you
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.
Read the primer
Why agents die between the demo and production
A demo is a prompt. Production is everything around it.
The model is rarely the hard part. What breaks in production is an agent with unbounded write access, no way to measure whether it is right, and no ceiling on what it costs when traffic triples. We build the boring parts first.Evals before launch, not after the incident
A golden dataset per agent and a pass bar it clears before it ever faces a customer.
Tool boundaries on every write
Cedar authorization on anything that mutates an order, a price, or a customer record.
A human on anything that moves money
Refunds, price changes, and purchase orders route to approval by design — not by exception.
Cost caps that still hold at peak
Token budgets and per-conversation ceilings, so Black Friday does not arrive as an inference bill.
An honest list
What we don’t do.
- Agent demos that fall over the first time real money moves.
- Autonomy on writes with no human, no evals, and no audit trail.
- Strategy decks that never reach production.
- Generic monthly retainers that grow without delivering.
- Vendor lock-in masquerading as 'best practices'.
We say no to the engagements that don’t fit so we can say yes to the ones that do.
How we work
From readiness check to a fleet that runs itself
One agent in production beats five in a slide deck. We ship in that order.
Readiness
We check whether your data, systems, and approval paths can support an agent at all — the failure mode that kills most agent projects before the model is even chosen.
Scope one agent
We pick the single workflow with the clearest owner and the cleanest data, define its tool catalog and write boundaries, and agree the human approval gate before a line of code exists.
Ship with evals
Built on AWS 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.
Expand the fleet
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.
What We Have Published
Proof you can read before you call us
We would rather be judged on work that is already public than on a case study you cannot verify.
0
Part Field Guide
A part-by-part guide to building eCommerce agents on AWS, with copyable artifacts.
0
Free AWS Calculators
Cost and readiness tools, including Bedrock AgentCore pricing. No email gate.
0+
AWS Certifications
Deep, cross-domain AWS expertise across our consulting team.
0+
Clients Served
Enterprises, SaaS companies, and startups across 12+ industries.
Who We Serve
Industries We Serve
eCommerce is where we go deepest. The AWS practice underneath serves every industry we have always served.
Retail & eCommerce
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.
Learn more
SaaS
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.
Learn more
Healthcare & Life Sciences
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.
Learn more
Fintech & Financial Services
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.
Learn more
Tech Startups
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.
Learn more
Education & EdTech
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.
Learn more
Clients We've Helped
What our clients say
From AWS security, GenAI, and HIPAA work — not agent-delivery case studies. Those are still ahead of us, and we will not pretend otherwise.
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
The platform underneath
Built on AWS, because that is the part nobody else does
Agents are the visible half. These are the AWS practices that keep them standing on the day it matters.
Bedrock & AgentCore
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.
Learn more →
Peak-traffic architecture
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.
Learn more →
PCI-scoped checkout
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.
Learn more →
Inference cost engineering
Token budgets, model selection by task, provisioned versus on-demand break-even analysis, and cost observability wired in from sprint one.
Learn more →
Data & analytics
The join keys agents actually need — orders, customers, products, inventory — in a lakehouse they can query with evidence rather than guess at.
Learn more →
24/7 managed operations
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.
Learn more →
Common questions
Questions before you start
Plain answers for operators who have not built an agent yet — and for the teams who already have.
What is an AI agent, in business terms?
An AI agent is software that does a job a person already does — looking up an order, deciding what to reorder, drafting a B2B quote — by reading your systems, choosing the next step, and taking the action, with a human in the loop when money or customers are at risk. It is not a chatbot that only answers from a FAQ. If it cannot use your tools and cannot be stopped, it is not ready for production.
Do we need to know AWS — or already have AI — to start?
No. The first conversation is about the work: which tickets, reports, or decisions repeat every week, who owns them, and what happens if the answer is wrong. AWS matters because that is where production agents (and the rest of your stack) usually run, and we are an AWS Select Tier Services Partner. You do not need to speak Bedrock to get a useful recommendation. If you are not ready for an agent, we will say so.
What is agentic commerce?
Agentic commerce covers two shifts happening at once. First, AI agents run parts of the store — answering WISMO tickets, flagging what to reorder, investigating refunds. Second, AI agents shop on behalf of customers, which means your catalog now has to be readable by a model rather than only by a person. Open standards have started to formalise the second half: the Agentic Commerce Protocol (OpenAI and Stripe) and the Universal Commerce Protocol (led by Google with Shopify, Etsy, and Walmart). Most merchants are working on the first half and have not started the second.
Which AI agent should we build first?
Whichever one has a clear owner, clean data behind it, and a decision a human already makes the same way every time. In practice that is usually customer support and WISMO, because the questions repeat, the data is in your order system, and the escalation path already exists. We publish the scoring method we use — volume, data readiness, blast radius if it is wrong, and effort — in the AI Agent ROI guide, so you can run it yourself before talking to us.
How do you stop an AI agent from doing something expensive or wrong?
Four controls, in this order. A tool catalog that defines exactly which actions the agent can take. Cedar authorization on every write, so mutating an order or a price is a permission decision rather than a prompt decision. A human approval gate on anything that moves money — refunds, price changes, purchase orders. And an eval suite with a golden dataset and a pass bar the agent has to clear before it goes live. Guardrails and token budgets sit on top of all four.
What does it actually cost to run an eCommerce AI agent on AWS?
It depends on conversation volume, how many tool calls each conversation makes, and which model you route to. The dominant costs are usually model inference and the AgentCore runtime, not storage or compute. We publish a free Amazon Bedrock AgentCore pricing calculator so you can model your own numbers before committing, and we set per-conversation cost ceilings in every build so a traffic spike cannot become an inference bill.
Does this work with our platform, or do we need to be on Shopify?
Platform is not the constraint — data access is. Agents talk to your storefront, ERP, CRM, and warehouse systems through a tool layer, so what matters is whether those systems expose the reads and writes an agent needs, and whether your order, customer, product, and inventory records share reliable join keys. We check that in the readiness assessment before anything gets built, because missing join keys is the failure that stops most eCommerce AI projects before a model is even chosen.
Why does AWS matter if the agent is just calling a model?
Because the model is the easy part. What breaks in production is everything around it: peak-season traffic, a PCI-scoped checkout path the agent must not wander into, inference costs with no ceiling, and an audit trail your finance team will eventually ask for. FactualMinds is an AWS Select Tier Services Partner with AWS-validated practices in RDS, CloudFront CDN migration, and S3 and CloudFront image delivery — the infrastructure work that makes an agent survive Black Friday rather than merely demo well.
What does AWS Select Tier Partner status mean?
AWS Select Tier is an official AWS designation awarded to partners who meet validated technical competency standards, maintain certified engineers, and demonstrate successful customer deployments. It means AWS has reviewed our delivery practices and customer references — it is validation, not endorsement.
Do you offer fixed-price or time-and-materials engagements?
Both. Readiness assessments and first-agent builds are fixed-scope with a locked price, because those are the engagements where scope creep does the most damage. Ongoing optimization, fleet expansion, and managed operations run as retainers. You own the infrastructure-as-code and the runbooks either way.
Start here
Read the full field guide »Four entry points into the field guide: the automation map, the architecture brief, the ROI priority score, and the readiness check.
11 min
AI Agents for eCommerce: 15 Automations Store Owners Can Implement in 2026
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%.
11 min
How FactualMinds Builds Production AI Agents for eCommerce on AWS (2026)
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.
7 min
AI Agent ROI for eCommerce: How to Decide What to Automate First (2026)
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.
6 min
The eCommerce AI Agent Readiness Assessment: Is Your Business Ready? (2026)
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.
Find your first AI agent opportunity
Tell us the repetitive work that is eating the week. We will say whether an agent is the right move — and if it is not, we will say that too.