Platform
Product intelligence for complex-product commerce.
Smooshe combines automation, AI and human expertise to turn fragmented product information into structured, useful and actionable product intelligence.
Architecture
Six stages, from whatever you have to wherever it needs to go.
Product information enters in the shape it happens to be in. It leaves as structured, validated records that every channel can render.
Ingest
01- ERP
- PIM
- DAM
- Excel
- CSV
- Website
- Catalog
Understand
02- Products
- SKUs
- Attributes
- Categories
- Applications
- Documents
- Images
- Relationships
Structure
03- Taxonomy
- Normalization
- Product relationships
- Data models
Enrich
04- Content
- Attributes
- Applications
- Specifications
- SEO
- Product relationships
Validate
05- Completeness
- Consistency
- Accuracy
- Channel readiness
Activate
06- Website
- Ecommerce
- Distributors
- Sales
- Marketing
- Search
- AI
Inside enrich and validate
Four jobs, four stages, one guardrail at the end.
A single model asked to write a product description will happily invent a temperature rating that reads perfectly and is wrong. Splitting the work is what makes the output safe to publish.
- 01
Spec extractor
Pulls every attribute the source actually contains
Parses spreadsheet columns, cut sheets, scraped tables and free-text descriptions into a normalized attribute set. Units are converted and reconciled; conflicting values between sources are flagged rather than silently resolved.
- 02
Standards injector
Attaches the governing standards and category logic
Looks the extracted attributes up against the category knowledge model — materials, compatibility envelopes, and the standards that apply to that part class. Compliance is never inferred: a standard appears only when the source establishes it.
- 03
Description writer
Renders content from confirmed data only
Writes for the channel and the reader it serves — a specifying engineer, a procurement buyer, a distributor counter. Because the writer only receives the confirmed attribute set, every sentence traces back to a specification, a standard or an application constraint.
- 04
Validation guard
Rejects unsupported claims and empty adjectives
Cross-checks every numeric claim against the extracted set and blocks marketing language with no measurable referent. Anything a buyer needs that the source never provided is surfaced as an explicit gap for a human to fill.
Gaps are reported, not filled in. If your source data never established a rating, a standard or a compatibility, Smooshe flags it for a human rather than producing a plausible number. In technical markets that distinction is the entire difference between content you can publish and content you have to re-check.
Automation, AI and people
The parts a machine is good at, and the parts it isn’t.
Automation carries the volume. AI carries the structure and the drafting. People carry the judgement — which is where product content actually earns trust.
Automation
Ingestion, normalization, unit conversion, taxonomy mapping, change detection and re-publication run without anyone touching a spreadsheet.
AI
Attribute extraction, relationship inference and content drafting — bounded by what the source data actually established, and validated before anything is delivered.
Human expertise
Category knowledge, taxonomy decisions, exception handling and sign-off. Corrections feed back into the model for the category so later batches need less of them.