Electronics
- Data scale
- 120K SKUs · 8M transactions
- Knowledge graph
- Category × spec × vendor (3-axis)
- Accuracy
- 94% top-1, 99% top-3
- Latency
- P50 80ms / P95 220ms
For commerce platforms, agent builders, and retail networks
Verified decisions for autonomous commerce.
When AI agents discover, compare, and transact at scale, hallucinated recommendations, hidden reasoning, and vendor lock-in become operational risks. GoPX binds each recommendation to verified rules and evidence so agentic commerce can be audited before it acts.
See it on commerce data Three forces converging
Why now, and why this is a category.
- 01
Agentic commerce protocols are live.
Google's Universal Commerce Protocol with Shopify, Walmart, and Flipkart. OpenAI's Agentic Commerce Protocol with Stripe. MCP as the agent-to-commerce backend interface. The protocols are no longer theoretical — they're shipping.
- 02
Open commerce networks are at infrastructure scale.
Hundreds of thousands of sellers. Hundreds of millions of transactions. Thousands of cities. The pipes are big enough that what runs through them at scale matters more than what they look like in a demo.
- 03
Trust is the bottleneck.
When agents act on behalf of buyers and sellers, every wrong recommendation is a transaction someone has to unwind. The bottleneck isn't speed or coverage — it's whether the agent's recommendation is one a regulator or a counterparty will accept.
Three risks at scale
Where agentic commerce fails when trust isn't built in.
-
Hallucinated recommendations.
At hundred-million-transaction scale, a 1% hallucination rate is a million-plus flawed interactions. The error rate isn't an LLM-leaderboard number — it's an operational failure that compounds.
Multiply 1% × 150M monthly transactions = 1.5M flawed interactions per month. Now imagine the dispute volume.
-
No audit trail.
When the recommendation flows from prompt to LLM to action, there's no defensible record of why it was made. When something breaks, there's nothing to investigate.
Regulators, partners, and auditors all ask the same question: 'show me the trail.' If you can't, you don't operate.
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Platform lock-in through AI.
If the agent only works with one model or one vendor's catalog, your platform inherits that vendor's roadmap. Sovereignty erodes one integration at a time.
Model-agnostic isn't a feature — it's a moat against the lock-in tax that compounds quietly until it's too late to migrate.
How it works
Six steps from raw catalog data to verified agent-ready logic.
The same engine that lifts contracts to logic on the legal side runs commerce-shape data through six stages. Each step produces inspectable, editable, auditable artifacts.
- 01
Representation
Catalog entries, transactions, and metadata are encoded into FAISS-indexed vector representations that preserve commercial structure (price, category, attributes, supply tier).
- 02
Clustering
DBSCAN-driven density clustering surfaces the natural groupings inside your network — what's actually substitutable, what shares a buyer base, what fragments the catalog.
- 03
Explainable characterization
Knowledge representation trees (KRTs) and decision trees translate cluster behavior into rules a human can read. Not 'embeddings cluster 47 — buy this,' but 'this group converts at 3× the median when bundle-priced.'
- 04
Few-shot annotation
Operators tag a small number of high-signal examples. The system propagates the labeling pattern across the network rather than asking for thousands of human annotations.
- 05
ANCO-HITS scaling
Authority and hub propagation across the seller-buyer-category graph identifies signal sources at network scale — without losing the explanation chain.
- 06
Lowering
The verified logic is bound back to executable rules. The agent that takes action runs against those rules, not against a free-form prompt. The audit trail comes free.
Three deployed reference implementations
What the engine looks like running on commerce-shape data.
Three reference implementations deployed by our Phoenix engineering team — same core engine, three vertical contexts. Numbers are real, sanitized for public display.
Electronics
Travel
Grocery
Runs on the model you choose. Stays on infrastructure you control.
Llama, Mistral, Qwen, DeepSeek, Gemini — switch the model without rewriting the rules. Decisions stay on your infrastructure; the audit trail is yours, not a vendor's. Sovereignty is not a feature; it's a design constraint.
We're rolling out our first commerce pilots over the next 60–90 days and are in active discussions with platform operators and agent builders. Open to custom pilots with teams running agentic transactions at scale.
Inquire about a pilotSee it on your documents.
A 45-minute session. We run your contracts, policies, or regulations through GoPX live and return structured logic, a decision walkthrough, and an honest read on fit. No pitch deck.
- Run on your documents, not a generic demo
- Structured logic + decision walkthrough
- Honest read on fit — no pitch deck