Ecommerce teams • AI-mediated buying channels
Make Your Website Ready for AI-to-AI Transactions.
We implement the catalog, schema, trust, and checkout changes that help AI agents evaluate, recommend, and purchase your products with less friction.
AI Commerce Transaction Path
Catalog, schema, and page signals are machine-readable.
Trust, policy, and product facts are clear enough to recommend.
Checkout/API paths are hardened to reduce AI-assisted purchase failures.
What We Implement
AI-to-AI commerce infrastructure, packaged as a practical service.
Agent Discoverability Architecture
We restructure product pages, sitemaps, and feed signals so AI agents can reliably find your catalog.
Machine-Readable Product Clarity
We normalize specs, variants, pricing, and availability so agent evaluators can accurately compare and recommend.
Agent Trust Signal Engineering
We harden policy, warranty, support, and provenance signals so AI systems can score your products as safe to buy.
Checkout/API Transaction Readiness
We audit and improve checkout, webhook, and transaction paths so AI-assisted purchases fail less often.
AI Recommendation Conversion Lift
We optimize high-signal pages to improve recommendation confidence and downstream conversion quality.
Continuous AI Commerce Validation
We run implementation tests and iteration cycles so your site stays compatible as agent behavior changes.
Founder Intro Video
See how we approach AI-to-AI commerce implementation without hype.
Watch the 40-sec IntroFree AI Commerce Readiness Diagnostic
Enter a website URL. We run real checks for crawlability, structured product data, trust/policy coverage, checkout-path signals, and platform-aware heuristics (Shopify, WooCommerce, Magento, BigCommerce, Squarespace Commerce, Stripe).
This is a technical website scan, not a CRM pipeline score.
How We Execute
Straightforward rollout in three steps.
Commerce Surface Audit
We map your catalog, schema, policy, and checkout layers to identify AI-agent transaction blockers.
Implementation Blueprint
You get a prioritized engineering plan covering discoverability, trust signals, and transaction path fixes.
Deploy, Validate, Iterate
We ship the changes, run AI-commerce validation checks, and tighten performance based on real outcomes.
What You Get
Concrete deliverables, not vague advisory decks.
AI-Commerce Readiness Report
- • Catalog + schema coverage audit
- • Trust/policy signal gap analysis
- • Checkout/API transaction risk map
Implementation Sprint
- • Prioritized fixes shipped by impact
- • Structured data + page template updates
- • Transaction-path hardening tasks completed
Validation + KPI Baseline
- • Before/after readiness scoring
- • AI-mediated conversion baseline report
- • Next 30/60/90 day roadmap
Implementation Timeline + SLA
Week 1
Discovery audit + baseline capture
Week 2
Blueprint + prioritized implementation backlog
Weeks 3–4
Core fixes shipped with weekly update checkpoints
Week 5
Validation report + next-step roadmap handoff
Proof Framework (Before/After)
Every engagement is measured against a simple baseline-to-outcome template.
Baseline (Day 0)
Schema coverage %, checkout fail points, AI-mediated conversion baseline.
Checkpoint (Day 30)
Fix adoption rate, transaction path stability, recommendation-confidence proxies.
Outcome (Day 90)
Conversion trend from AI-mediated sessions, remaining blockers, next optimizations.
Trust + Claim Boundaries
What we do
- • Implement catalog, schema, trust, and checkout improvements
- • Measure readiness and transaction reliability deltas
- • Provide engineering-level recommendations and execution
What we do not claim
- • No guarantee of autonomous AI purchases on every platform
- • No guaranteed revenue outcomes independent of market factors
- • No legal/compliance guarantees outside your counsel review
Discovery data is used only for implementation evaluation, delivery planning, and follow-up. Sensitive production credentials should never be submitted through this form.
Proof This Works in the Real World
Product Structured Data Uplift (Google Merchant guidance)
Public platform guidance shows richer product structured data improves machine interpretation for eligibility and recommendation surfaces.
Source: Google Search Central — Product Structured DataSchema Reliability and Rich Result Validation
Public documentation demonstrates that validated schema coverage reduces ambiguity for automated evaluators consuming page metadata.
Source: Schema.org + Google Rich Results TestCheckout Friction Reduction and Conversion Impact
Industry UX research consistently links lower checkout friction and clearer trust/policy details to higher completed transactions.
Source: Baymard Institute — Checkout UX ResearchThese are publicly available third-party case studies and references, not BettersAI client claims.
Founding Client Program
We’re currently onboarding pilot clients at founder-led pricing.
Apply for Founding PricingGet practical AI-commerce implementation tactics weekly.
No fluff. Just concrete changes that improve AI-agent transaction readiness.
Simple Pricing
Starter
$500/engagement
- • AI-commerce readiness audit
- • Product/schema gap report
- • Transaction-friction scorecard
- • 2-week implementation roadmap
Growth
$1,500/month
- • Audit + core implementation sprint
- • Schema/trust signal deployment
- • Checkout path hardening
- • Validation + KPI baseline
Scale
Custom engagement
- • Continuous AI-commerce optimization
- • Cross-team implementation ownership
- • Experimentation + performance loop
- • Executive reporting + roadmap
Let’s Build Your AI-Commerce Transaction Surface
Frequently Asked Questions
Most clients get their first implementation sprint live in 7–14 days, depending on CMS and checkout complexity.
Ready for AI-to-AI Commerce Execution?
Let’s prioritize the exact catalog, trust, and checkout changes that make AI-mediated transactions more reliable.
Book a Strategy Call