Assistants and micro-apps on your live data.
Most ecommerce AI is a sample. CCEN assistants read and write the live operational data your team uses. They log in like teammates. They have permission rules just like your team. Every action is recorded. Build a custom app for your business in an afternoon.
The actual problem, grounded in reality.
Most ecommerce AI tools are demos. They scrape a sample of your data, run a model on it, and produce a confident-looking output. Your team reviews, finds an obvious error (wrong SKU, stale price, ignored VIP rule), and stops trusting the system. Trust dies on contact with reality.
The deeper problem is architectural. The AI tool sits outside the ledger. It reads stale samples. It cannot write. Its outputs are recommendations the operator has to manually copy into the real tool. Even when the AI is right, the friction kills the value. AI as a layer on top of fragmented data does not work.
CCEN treats assistants as first-class operators. They log in like teammates. They have permission rules just like your team. Every action is recorded. An assistant that drafts a PO drafts it in the actual PO surface, with the actual SKUs, the actual lead times, the actual reorder points. The operator approves, the PO sends, the audit trail records. Same workflow. Different actor.
What changes on CCEN
What AI looks like when it is a primitive of the platform, not a layer on top.
Assistants on the live ledger
Replenishment, margin, CS, listings QA, fraud watch, all read and write the actual data. No sampled feed. No nightly export. The same orders the ops team works on.
Same login, same audit log
Each assistant logs in like a teammate. Their actions go through the same permission rules your team has. When an assistant is wrong, you trace what happened and roll it back the way you would for a human action.
Customize any reference app for your business
Start from a working app, change it for your team's exact needs, keep it private or share it. Most teams build their first custom app in an afternoon. Prompt our AI for the change and review what it generated.
Reports composed in plain English
Describe what you want. CCEN sketches the report against your real data. Edit before saving. The half-formed Tuesday-afternoon question becomes a report you can pin to the home page.
Drawer-based assistant surface
A focused conversational drawer. Not a thousand sparkle icons spread across the UI. The drawer composes against the entity you're looking at. Assistants are tools, not theme.
App screenshot · Assistant drawer · drafting a PO
Real SKUs, real lead times, real reorder points. Approve, edit, decline. Placeholder image.
How CCEN assistants stay honest.
Trust dies on contact with bad data. CCEN's design constrains the failure mode so assistants stay useful in production.
Watch a 60-second assistant demo.
A real assistant drafts a real PO against fake-but-realistic SKUs. You see the reasoning, the proposed action, the audit log entry.
AI tools you'd typically replace
Most AI ecommerce vendors are bolted on. CCEN replaces the bolt-on with native assistants that share the data and identity model.
Reference setup
An AI-forward ops team at a $40M brand running CCEN.
We tried two AI ecommerce tools. They both produced confidently-wrong outputs against stale data. CCEN assistants work because they're on the live ledger and they go through the same approval flow our team does. The replenishment assistant saved us a stockout in week three.
Common questions about AI on CCEN
- Do assistants auto-execute?
- Configurable. Low-stakes actions (CS drafts, listing QA flags, report composition) default to draft-and-approve. Higher-stakes actions (POs, markdowns, refunds over a threshold) require human approval. Approval policies are explicit.
- What model do assistants use?
- Configurable per assistant. Frontier models for reasoning-heavy assistants (replenishment, margin). Smaller models for high-volume drafting (CS replies, listings QA). You can specify model and budget per assistant.
- Can we audit what an assistant did?
- Yes. Every assistant action records the actor (the assistant), the timestamp, the inputs, the outputs. You can replay any assistant's session like any human teammate's.
- What about hallucinations?
- Assistants read structured data through tool calls, not free-form text. They do not hallucinate SKUs because they cannot type SKUs that do not exist. The architecture constrains the failure mode.
- Can we build our own assistant?
- Yes. Start from a working assistant, prompt for the change you want, review the generated logic, deploy it. Most teams build their first custom assistant in an afternoon. Developer docs cover the deeper customization.
- What about data sent to model providers?
- Customer-managed routing supported. Route sensitive prompts through your own provider account, keep customer PII out of model context, or run on your own model deployment when residency rules require it.
Built for AI-native operations. See it run.
A 30-minute call with a real engineer. We connect a sandbox to your Shopify, Amazon, or EDI partner and walk through the workflow you care about. No slides. No discovery deck. The product, on data that looks like yours.