Who's Building Where
Agentic-Commerce Infrastructure
"Agents are becoming the storefront. Without a new foundation, brands vanish from the shelf—and agents take forever to be wrong."
Intent
→
Discovery
→
Evaluation
→
Checkout
→
Fulfillment
Most of the decision happens before checkout—when a buyer is comparing options and deciding what's acceptable on price, delivery, and returns.
Distribution becomes 'be in the agent' (catalog integration)
Agents can explore, but time is limited → results must come fast
Exploration becomes retrieve → rerank → shortlist (not 30 PDPs)
Index + retrieval infra becomes the "shopping shelf"
Shortlists concentrate outcomes; ranking matters more
Top 3 results get ~64% of clicks (Amazon analog)[2]
If key attributes and policies aren't structured, you may not qualify
The hard part is keeping many integrations correct and current
When facts drift, you don't always "rank lower"—you quietly stop qualifying. Merchants only notice after conversions drop.
A single bad fact makes the agent look unreliable. The model gets blamed for the data.
Variants, duplicates, and inconsistent policies make decision-quality comparisons impossible
Scraping and calling dozens of APIs per query doesn't meet latency and reliability requirements
Turn messy catalogs into structured, comparable offers + machine‑readable policies — with provenance, timestamps, and freshness monitoring
A fast, reliable retrieval API that returns decision‑ready candidates (not links) with the signals agents need to choose confidently
An open publishing + discovery substrate where merchants publish once and agents integrate once — so the ecosystem can proliferate without bespoke integrations or closed‑catalog lock‑in
Subscription for agent-readiness + syndication/refresh + monitoring/diagnostics
Usage-based retrieval API + paid SLAs (latency, freshness, provenance)
Private deployments + custom integrations (internal catalogs, procurement, regulated workflows)
Existing budget: product data (PIM/PXM) + feed/syndication + integration ops
Context: AI in retail projected ~$96B by 2030[4]
Initial supply SAM (wedge): ~100k mid-market/enterprise merchants × $20–50k/yr = ~$2–5B
Expansion: as more agents launch, 'integrate once' becomes the default path
Market sizing is easiest to see as budget consolidation. Merchants already spend on product data systems, syndication, and ongoing integration ops—we replace and unify that spend for the agent era.
Near-term, the wedge is supply-side subscriptions for 'integrate once → syndicate everywhere.' Over time, demand-side usage expands as agent shopping query volume grows.
Co-Founder
We've shipped AI agents and enterprise-grade integrations, and built conversion-critical commerce experiences. Now we're applying that playbook to agentic e-commerce end-to-end.
[1] Baymard Institute — cart abandonment benchmark (~70%):
https://baymard.com/lists/cart-abandonment-rate[2] Algolia — e‑commerce search impact (2.4× buy likelihood; 2.6× spend):
https://www.algolia.com/blog/ecommerce/e-commerce-search-and-kpis-statistics[3] Salesforce — consumer AI shopping / agent adoption signals:
https://www.salesforce.com/in/news/stories/new-research-shows-how-ai-agents-can-step-in-as-consumer-trust-slips/[4] Mordor Intelligence — AI-in-retail market sizing ($14.2B → $96.1B, 2025→2030):
https://www.mordorintelligence.com/industry-reports/artificial-intelligence-in-retail-market[5] Lunar.dev (Report 2024) — integration troubleshooting time (~36% of eng time):
https://www.lunar.dev/report-2024[6] Aviso — agent latency expectations (<2s for "natural" workflows):
https://www.aviso.com/blog/how-to-evaluate-ai-agents-latency-cost-safety-roi