FLAGSHIP 02

An AI agent just read your product page and moved on.

It could not parse your attributes, your stock, or your terms, so it recommended a competitor it could. That traffic never shows up as a lost sale, because it never became a visit.

WHAT WE'D ACTUALLY BUILD

In plain terms: we make your product data complete and machine-readable, then publish it so AI shopping assistants can find, compare, and recommend your products instead of skipping past them.

THE PROBLEM

Why this is broken today

Your catalog, pricing, and checkout assume a human with a cursor. Agentic buyers need structured, machine-legible commerce surfaces.

Your product data differs between the site, the feed, and the ERP
You cannot say what share of sessions came from agents last month
A comparison agent would have to guess at your variant attributes

Agent-ready commerce means a non-human buyer can discover, compare, and transact with you without a browser UI. Catalog structure, price truth, and a documented transaction path become revenue infrastructure rather than hygiene work.

Copy written for shoppers

Attributes structured for comparison

Scraped by whoever shows up

Published feeds and documented APIs

Bot traffic filtered out

Agent traffic segmented and measured

UI-only checkout

Agent-accessible checkout with policy

A CROWDED SHELF, WHERE WE SIT

Everyone is selling an agent-readiness checklist. We ship the catalog fix.

Agentic commerce readiness became a category in 2026, and the market split into free scorecards on one end and 90-day enterprise reviews on the other. We deliberately sit in neither, and we say plainly where we are the wrong call.

Free readiness scorecards

Useful for a first temperature read. They stop at a score, no attribute schema, no endpoints, no owner.

Enterprise commerce consultancies

Structured 90-day reviews at six figures, sized for enterprise catalog estates. If that's you, take it.

Where we're sharper

Product-data and catalog legibility for agent buyers on live mid-market storefronts, the same architecture running in production behind our gaming and esports work.

The proof, not the pitch

A live youth esports franchise (name withheld) runs this signal-to-system architecture, returning roughly nine admin hours a week to staff.

Market landscape as observed in 2026. We update this when it changes.

WHAT YOU GET

Outcomes, not deliverable lists.

Machine-legible catalog and attribute structure
MCP endpoints for discovery, evaluation, and transaction
Agent-safe checkout and policy guardrails
Instrumentation for agent traffic versus human traffic

THE ARC

Four phases. 30–60 days.

01

Define

Signal inventory, success metrics, and constraints.

02

Design

Architecture, agent graph, and evaluation plan.

03

Build

Production deployment with observability from day one.

04

Measure

Outcome reporting against the metrics we agreed.

PACKAGES

Entry points and pricing

Most engagements begin with the Agent-Ready Commerce Readiness Audit, Scores catalog structure, data quality, and agent accessibility across five dimensions.

START HERE

Readiness Audit

Catalog, data, and agent-accessibility scoring.

$3,500 fixed

NEXT

Agentic Workflow Build

Production agent workflow across commerce systems.

from $8,000

NEXT

Transformation Blueprint

Agent-ready commerce architecture end to end.

$12,000–$18,000 fixed

NEXT

Managed AI Operations

Run, evaluate, and improve deployed agents.

$1,500–$4,500/month

PROOF

Agent-Ready Commerce engagements, written up in full.

Unnamed client, mid-market DTC brand · E-commerce Brands

A catalog graded the way an agent reads it, fixed revenue-first

A DTC brand with a large SKU count had a catalog written for human browsers. We scored every sellable SKU against the Agent Readiness Ladder and fixed attribute coverage on the products carrying the revenue first.

Read the full case study →

Unnamed client, B2B industrial distributor · E-commerce Brands

Compatibility data structured so an agent can answer 'will this fit?'

A B2B distributor's buyers were starting to arrive through AI assistants asking compatibility and lead-time questions the catalog could not answer. We made the fitment and availability data machine-readable and exposed it through a governed interface.

Read the full case study →

9 hrs/week

In production today

Admin time returned to staff in a live community-to-CRM build.

40+ products

Catalog records restructured so shopping agents can read specs, stock, and terms.

Evaluated before launch

Nothing goes live until it clears a documented eval threshold on your own data.

You own it

Code, data, connectors, and runbooks stay in your accounts after handover.

See how we work

NEXT STEP

Ready to scope Agent-Ready Commerce?

Bring your systems and constraints. We'll tell you in 20 minutes whether this applies.

Find your transformation