Cross-Platform Commerce Orchestration
Agentic multi-merchant cart unification, cross-platform product sourcing, and fulfillment coordination — one checkout from any combination of merchants
The problem & context
This case is grounded in the SEN commerce problem — not generic product search, but the genuine complexity of buying from multiple US merchants in a single coordinated transaction. A user wants an iPhone from Apple.com, headphones from Amazon, and a book from a specialty retailer. Without an orchestration layer, that means three browser sessions, three checkout flows, three shipping timelines, three return policies, and hours of coordination. The engineering challenge is creating one reliable agentic workflow that spans arbitrary merchant combinations — and keeps the human in control before any money moves.
Multi-merchant purchasing is unsolved not because search is hard, but because the coordination is. Merchant APIs return incompatible product structures. Checkout flows don't compose. Delivery comes from three different sources. Returns require contacting three different support teams. The friction is so high that users either limit themselves to a single platform or abandon the purchase entirely. A commerce assistant that only improves search misses the actual problem.
I built the backend architecture for an agentic commerce workflow: Saya (the AI) parses the user's intent, runs parallel product discovery across merchant APIs, normalizes incompatible product data into comparable candidates, assembles a multi-merchant cart, and presents a structured approval before executing anything. The execution layer then places orders across merchants, tracks delivery from each source, and manages returns through a single interface. The system is explicitly designed so no payment is ever triggered without a human decision.
AI system design
Type colors:● AI● System● Human● Output● Data
By the numbers
Operational metrics and modeled business impact. Hover bars for exact values.
Measurable outcomes
Each ring animates to its final value on load.
Checkout time saved vs manual
Return friction removed
Human approval coverage
Cross-platform data normalized
What changed
Let a user buy from any combination of US merchants — in one conversation, one approval, one checkout.
Why this architecture, not another
The core insight is that multi-merchant commerce is a coordination problem, not a search problem. Any single merchant already has good search. The unsolved problem is: how do you combine a cart from five different stores, normalize incompatible product data, and present one coherent decision to the user before any money moves? The agent-per-responsibility model is the answer. Each stage — discovery, normalization, assembly, quality control — owns its own failure mode. The approval gate isn't optional: it's the trust boundary that makes the rest of the automation acceptable to users.
Architecture principles
The choices that made this system practical in production: clear ownership, controlled failure modes, and business evidence before automation.
One agent, one responsibility
Discovery, normalization, assembly, and quality control are separate agents. Failures are isolated — one broken merchant integration doesn't collapse the whole workflow.
Human approval at every irreversible boundary
The AI orchestrates everything up to and including the recommendation. Payment and order placement require explicit user approval.
Evidence before action
Every recommendation carries source data, price breakdown, shipping estimate, and validation result so the approver understands exactly what they're approving.
How the implementation was structured
Merchant API abstraction
Build a connector layer that normalizes product data, availability, and pricing from each merchant into a common schema.
Agent orchestration
Implement parallel discovery, cart assembly, and quality gate agents with structured outputs and per-agent retry logic.
Approval + fulfillment
Wire the approval gate to multi-merchant order placement, delivery aggregation, and centralized returns management.
What made it safe to scale
Scalable
Merchant connectors are independent — adding a new platform is a new connector, not a rewrite.
Reliable
Failures are isolated by agent responsibility; one unavailable merchant doesn't abort the whole workflow.
Trustworthy
Payment is always gated by human decision. The system never acts on money without explicit approval.
AI agent responsibilities
Business impact
- ✓Users can request products from any combination of US merchants in a single conversation — the complexity of multi-merchant coordination is fully absorbed by the system
- ✓Checkout time for a complex 6-item, 3-merchant order drops from ~3 hours of manual browsing to ~13 minutes of AI-assisted workflow
- ✓All irreversible actions (payment, order placement) sit behind a human approval gate — the AI prepares, the user decides
- ✓Returns and delivery tracking from multiple merchants are managed through one interface instead of separate vendor support queues
- ✓Service fee model captures value at the coordination layer — the harder the shopping task, the more value the platform delivers
Need something like this?
Book a free 90-minute audit. We'll look at your workflow and tell you honestly whether the same approach applies.