Service Agentic Commerce
Typical Timeline 4–10 Weeks
Team Senior, 3–5 People

Agentic Commerce

AI agents that sell, support, and personalize at scale.

Production-grade AI wired into your Shopify stack chat agents that resolve tickets, personalization that lifts AOV, copilots that draft merchandising at speed. We build the prompts, the data plumbing, and the guardrails.

The work

AI in commerce only matters if it ships to production.

Plenty of agencies will demo a chatbot. Very few will wire it into your Shopify product feed, your Klaviyo flows, your help docs, your order history, your CRM and put guardrails on it that survive contact with real customers.

Seedcms is a leading AI integration agency for commerce. We build agents that earn their keep: support automation, personalization, merchandising copilots, predictive segments. Measured, monitored, and tuned in production.

We start with one agent that pays for itself. The roadmap grows from there.

What's included.

What an Agentic Commerce engagement covers the scope, the deliverables, and the operational reality of shipping each piece.

01

Personalization & ranking

Real-time recommendations and ranking surfaces tuned to behavior, lifecycle, and intent. PDP, cart, home page, search results.

  • Behavioral + cohort modeling
  • Real-time ranking APIs
  • Surfaces across PDP, cart, search
  • A/B harness for every model
02

Conversational agents

Tuned LLM agents wired to your help docs, order data, and product catalog. Resolves tier-1 tickets autonomously, routes the rest with full context.

  • RAG over your knowledge base
  • Order & account-aware
  • Handoff with full context
  • Hallucination guardrails & eval suites
03

Merchandising copilots

Bulk-generate on-brand product copy, alt text, meta, and translations. Outputs land as Shopify drafts for your team to approve.

  • Brand-voice tuned prompts
  • Bulk catalog generation
  • Draft-state in Shopify
  • Localization at scale

Our approach.

How we run an Agentic Commerce engagement. Sequenced so each phase de-risks the next.

Step 01

Frame

Define the agent's job. What does success look like tickets resolved, AOV lifted, hours saved? We agree the metric before we touch a model.

Step 02

Data

Audit and prep the data the agent needs help docs, order history, product catalog, customer signals. Vector DB, embeddings, ground truth.

Step 03

Build

Prompt engineering, tool design, retrieval pipelines, fallback logic. Built on OpenAI, Anthropic, or open models depending on the use case.

Step 04

Eval

Eval suites against a held-out set of real cases. Hallucination tests, bias checks, latency budgets. We don't ship to production without numbers.

Step 05

Launch

Ramped rollout 10%, 50%, 100% with kill switch ready. Live monitoring, error logs, customer feedback signal collected from day one.

Step 06

Tune

Continuous improvement. Every customer interaction is a training signal. We tune prompts, re-rank retrievals, and ship updates weekly.

0
Tier-1 tickets auto-resolved
Across deployed agents
0
AOV lift from personalization
Average across 6 launches
0
Saved per 1,000 SKUs
Merchandising copilot
0
P95 agent response time
Sub-2-second under load

Stack we trust.

Tools and partners we deploy on Agentic Commerce engagements. Certified, integrated, and chosen on purpose.

Honest answers.

The questions we get about Agentic Commerce before brands sign. If yours isn't here, ask us directly.

How accurate are the agents in production?

Depends on the use case. Our deployed support agents resolve ~62% of tier-1 tickets autonomously and route the rest to humans with full context. Personalization models typically deliver a 12–22% AOV lift within 6 weeks.

We don't ship anything we can't measure. Every agent has an eval suite, a held-out test set, and a defined success metric agreed before launch.

What about hallucinations? How do you stop the agent from making things up?

Retrieval-augmented generation, tight system prompts, output validators, and a fallback path to human handoff. We use grounded responses (cite-or-don't-answer) for support and finance use cases.

Pre-launch, every agent runs against an adversarial test, known confusing queries, edge cases, prompt-injection attempts. We don't ship until the failure modes are documented and bounded.

Where should we start? We have a lot of ideas.

With the one agent that pays for itself fastest. Usually that's support automation measurable, bounded scope, immediate ROI in deflected tickets. From there we sequence into personalization, then merchandising copilots, then predictive segments.

We'll do an ROI workshop in week one to rank your ideas by impact and effort.

How does the agent stay current as our catalog and content change?

Embeddings and retrieval indexes refresh on a schedule daily for help docs, real-time webhooks for product changes, hourly for inventory. The agent always sees the current state of your store.

Prompt and model improvements ship through the same CI/CD pipeline as the rest of your storefront.

What about data privacy and customer PII?

PII never leaves your perimeter without explicit need. We use redaction layers on inbound prompts, scoped API keys, and per-tenant isolation on retrieval indexes. SOC 2 and GDPR-compliant by design.

We can run on your infrastructure if the data residency requirement demands it.

Who maintains the agents after launch?

Most clients move to a monthly retainer with us after the initial build. Prompts drift, models improve, your catalog changes, customer expectations shift agents need active maintenance to stay sharp.

If you have an in-house ML team we transfer ownership with full documentation, runbooks, eval suites, and an embedded knowledge-transfer week.

Let's build

Tell us where you want commerce to go.

30-minute intro call. No deck, no sales theater just senior people, your roadmap, and an honest answer on whether we're the right fit.

Book an intro call