Maximize profit per SKU with elasticity-aware repricing for high-volume Shopify and Amazon brands.
MAP is a hard floor in the optimizer — breaches raise a friendly alert, never a price cut.
Native Shopify Plus and Amazon Seller Central sync, CSV and API for the rest.
65W GaN USB-C Charger EL-ANC-USBC-65W | $44.99 | $41.99 | ▼ -2.31 |
Selvedge Slim Denim 32×32 AP-DNM-SLM-IND-32 | $128.00 | $134.99 | ▲ -1.32 |
Merino Crew Socks 3-Pack AP-SCK-MER-CRW-3P | $28.00 | $29.99 | ▲ -0.72 |
ANC Over-Ear Headphones EL-HPH-ANC-OVR | $179.00 | $172.00 | ▼ -2.04 |
Organic Heavyweight Tee / M AP-TEE-ORG-BLK-M | $38.00 | $40.99 | ▲ -1.03 |
90 days post-launch, 41 pilot catalogs
Across apparel, electronics, home
Suggested, not blindly applied
Median R² on held-out weeks
“We had been matching the cheapest reseller for two years. CogniSpan showed that a third of our catalog was inelastic — we raised those prices 6% and lost almost no units.”
Head of Merchandising, DTC apparel group (name withheld pre-launch). 12-week pilot, Shopify Plus, three competitor domains tracked hourly.
Hourly price truth from every marketplace you compete on.
Variant-level matching resolves size, colour and capacity so a 32x32 selvedge denim is never compared against a 30x30. Every observation is timestamped and kept for 36 months.
A demand curve per SKU, not a category rule of thumb.
A hierarchical Bayesian model pools thin-data SKUs with their category, separates promo lift from base demand, and reports elasticity ε with a 90% credible interval instead of a single number.
Price moves that defend margin, inside limits you set.
The optimizer searches for the contribution-margin peak, not the volume peak — then clamps the result to your floor, ceiling, MAP and maximum daily move before anything reaches your storefront.
Connect
Point CogniSpan at your storefront. We import SKUs, costs and 90 days of order lines. No shopper PII ever leaves your store.
Observe
Add up to ten competitor domains. The scraper starts building the price panel immediately and flags MAP breaches from hour one.
Model
Elasticity is fit per SKU against your own demand history, pooled by category where data is thin. Confidence is reported, never hidden.
Decide
Review the suggested price, the expected margin delta and the credible interval. Apply one SKU, a filter, or the whole catalog.
For brands proving out elasticity pricing on a core catalog.
For catalogs where pricing is somebody's full-time job.
For multi-brand groups and marketplace operations.
Yes. Thin-data SKUs borrow strength from their category through a hierarchical model, and the confidence score tells you exactly how much to trust each recommendation. Below 0.35 confidence we recommend holding price.
No PII. CogniSpan reads order-line aggregates only: SKU, date, price, units, cost. Nothing about individual shoppers is imported, stored or modelled.
MAP is a hard floor inside the optimizer. A suggested price can never fall below it. When a competitor breaches your MAP, we raise an alert with evidence rather than cutting your price to match.
Nothing moves without your approval on Starter, and on Growth every automated move respects your floor, ceiling and ±7% daily cap. Every applied price is versioned and reversible in one click.
About thirty minutes of work on your side, then one overnight model run. Most brands review their first suggestion set the next morning.
Rolling 36 months of price and demand history, exportable as CSV and deletable on request within 30 days.
We onboard in cohorts of 25. You receive a scheduling link and a sandbox project preloaded with a catalog shaped like yours so you can judge the output before connecting anything.
We onboard in cohorts of 25 brands. Tell us your catalog shape and we'll prioritise the right fit — you'll get a sandbox project before you connect anything.
Currently onboarding · Cohort 03 · Q4 2026
