Industry : Ecommerce
AI for ecommerce.
Ecommerce is operations at catalog scale: thousands of SKUs each needing content, stock truth across channels, and customer questions answered at 2am. Novistu builds the systems behind the storefront: catalog content pipelines, inventory reconciliation, support agents grounded in order data, and shopping assistants that convert. We have run complex catalog operations ourselves (Subara's custom-kit ecommerce platform).
The sector reality
What actually hurts here.
Catalog content never finishes
Every SKU needs titles, descriptions, attributes in every channel. Generation pipelines produce channel-native drafts at catalog speed, reviewed in bulk.
Stock lies somewhere
Marketplaces, warehouse and store disagree until a customer oversells. Reconciliation jobs with drift alerts keep inventory honest.
Support repeats itself
Where is my order, returns, sizing: answered badly at scale. Order-aware agents resolve the routine mass instantly.
Browsers do not buy
Visitors cannot find the right product among thousands. Shopping assistants translate intent into the right item, measurably.
Use cases
What we build in this sector.
01
Catalog content engine
Descriptions, attributes and SEO copy generated per channel with bulk human review.
02
Order-aware support
WISMO, returns and exchanges resolved against live order data; complex cases escalated with context.
03
Inventory reconciliation
Cross-channel stock sync with drift detection and campaign-safe overselling guards.
04
Shopping assistant
On-store concierge translating intent into products, with honest availability.
05
Review intelligence
Reviews and returns mined into product, content and merchandising decisions.
The automated workflow
A sector workflow, rebuilt.
- Catalog flows in
- Content generated
- Stock reconciled
- Shoppers assisted
- Signals returned
- 01
Catalog flows in
Feeds and platforms normalized into one product model.
- 02
Content generated
Channel-native drafts produced at scale for review.
- 03
Stock reconciled
Channels synchronized with drift alerts before campaigns.
- 04
Shoppers assisted
Intent translated to products; questions answered from order data.
- 05
Signals returned
Reviews, returns and searches feed merchandising decisions.
Measurement
KPIs this work moves.
Your baseline is your own; the direction is what automation changes. We measure before we build.
| KPI | Typically today | With automation |
|---|---|---|
| Catalog time-to-live | Weeks per category refresh | Days with generated drafts |
| Support first-response | Queued for hours | Instant for the routine mass |
| Oversell incidents | Recurring campaign pain | Guarded by reconciliation automation |
| Search-to-purchase | Browse-dependent | Assisted conversion measured per session |
Integration landscape
Honest section
What can go wrong here.
Product facts must never be generated: content drafts from structured truth only
Inventory automation needs reconciliation jobs; drift is a when, not an if
Review-mining must respect platform terms and customer privacy
Proof
Related work from this territory.
Sector questions.
Not when it is generated from structured product truth, differentiated per channel and human-reviewed. What search engines penalize is sameness at scale, which our pipelines are designed to avoid.
Yes: it reads live order data from your stack, so 'where is my order' gets a real answer instantly, and anything emotional or exceptional escalates with full context.
That is the standard starting point: platform APIs plus marketplace feeds cover most of the automation surface from day one.
Tell us what is eating your team's hours.
A short brief, answered within one working day. The first call is free, and if AI is not the right answer, we will say so on that call.
First call free : honest about fit : no obligation