Industry benchmark
Inventory accuracy
63%
Multi-store retail runs on 63% accurate stock data. Every decision downstream inherits the error.
Polestar, 2024
Early partners open
Dashboards describe. Agents decide.
Forecasting, replenishment, promo, assortment, pricing — decided by agents, approved by your team. Built for multi-store fashion, cosmetics, jewelry, and specialty.
Category manager · running now
Margin uplift — last 14 days
+8.4%
Price suggestions queued
12 +3 since yesterday
Categories rebalanced
4 this week
Low-stock alerts
3 next 48h
Kwanta · Suggesting 12 price changes in Decorative Cosmetics
Review →
With the first operators. Others are moving in.
Multi-store retailers in fashion, cosmetics, jewelry, and specialty are building on Kwanta’s agentic stack — ahead of the category.
“Kwanta replaces the guesswork in pricing, promo, and category. For the first time we see what each SKU is actually doing — in one place.”
Mir Kosmetiki Multi-store cosmetics retailer · Kazakhstan
Early partner slot — fashion operator, 4+ storefronts.
Early partner · Fashion Coming soon
Early partner slot — specialty / SKU-heavy operator.
Early partner · Specialty Coming soon
Become an early partner Limited slots · 3+ store pilots only
Dashboards don’t decide. Agents do.
The industry baseline is a category in pain. Our first pilot shows what closing the gap looks like in production.
Industry benchmark
Cart abandonment
49%
Half of omnichannel shoppers bail on friction — most of which a real-time signal layer would catch.
Polestar, 2024
Industry benchmark
Say agents are essential
75%
Three in four retailers now believe agentic AI is required to stay competitive.
Polestar, 2024
Industry benchmark
Piloting or evaluating
96%
The bus is leaving. 43% already in pilot, 53% actively evaluating. The question is not if — it is who ships first.
Polestar, 2024
Our pilot
Mir Kosmetiki · Multi-store cosmetics chain, Kazakhstan
98%
What closing the gap looks like.
Inventory accuracy up from the 63% industry baseline. The category manager agent catches stockouts before they hit the register — every week, across every storefront.
One agentic OS. Every retail workflow.
Kwanta is the AI platform that handles the weekly retail work that actually moves margin — forecasting, replenishment, promo, assortment, pricing. One live view across every store and every SKU. Your team sets the rules. Kwanta does the prep. You approve. The numbers update.
01 Forecasting
Demand signals tuned for fashion cycles.
Store × SKU × day demand, adjusted for drops, seasons, markdowns, and promo lift. One forecast every downstream agent reads from — not five disagreeing spreadsheets.
- Hierarchical forecast: category → SKU → store × day
- New-SKU cold start with analogs and size-curves
- Auto-reforecast on promo, stockout, or price change
02 Replenishment
Store-level orders, not a weekly spreadsheet.
Replenishment that honors lead times, pack sizes, min display, and supplier windows. The agent drafts the order list. Approve, override, or let it run on rails.
- Store × SKU orders on a weekly / bi-weekly rhythm
- Pack-size and min-display constraints honored
- Inter-store transfers proposed before supplier orders
03 Promo
Promo math, not promo theatre.
Every campaign pre-scored for incrementality, cannibalization, and halo. The agent drafts the offer, calendar, and segments. Your team approves what ships to the till.
- Pre-flight incrementality + halo score
- Segmented sequences wired to the till
- Post-campaign lift measured per segment, not aggregate
04 Assortment
Category rules that scale across stores.
SKU mix, depth, and breadth per store cluster. The agent flags laggards, spots gaps, and proposes moves — with the margin and turn trade-offs attached.
- Store-cluster roles + assortment width
- SKU-level ABC / XYZ + lifecycle stage
- Rationalization with margin × turn trade-offs
05 Pricing
Elasticity-aware pricing, on rails.
Every price change — list, markdown, promo — scored against elasticity, competitor, and guardrails. Approve each move or let the rules execute for you.
- Elasticity per SKU × store × channel
- Guardrails: floor, ceiling, MAP, margin
- Markdown cadence tuned to sell-through target
Surfaces
Where the agents show up — and how your team interacts with them.
POS
Agentic till.
Multi-store, Kaspi + card rails, clienteling built in. Writes the clean data the agents think in.
Admin & Franchise
Back office.
SOPs, roles, and rails per region. One brand standard, every storefront — partners keep autonomy.
Analytics & Models
Live P&L.
SKU × store × day view and the retail math agents decide with. Open BI export to your warehouse.
Marketing & Passport
Sequences wired to the till.
WhatsApp, email, Telegram. Cross-merchant customer passport — consent-first, not purchase history.
Built for fashion, beauty, jewelry, and specialty.
SKU-heavy, multi-store, category-dense. The verticals where dashboards fail first — and where agents compound the most.
Fashion
Fashion
Collections, drops, markdowns. Variants, sizes, and seasons as first-class concepts.
- Markdown cycles
- Sell-through rules
- Size-curve allocation
Cosmetics
Cosmetics
SKU-heavy assortments with testers, sets, and consignment. Clienteling built in.
- Testers & sets
- Consignment
- Clientele profiles
Jewelry
Jewelry
High-ticket, serial-numbered inventory. Clienteling, consignment, and trunk shows with full provenance.
- Serial tracking
- Trunk-show mode
- Clienteling
Specialty
Specialty
Category-heavy, consultative retail. Rules that match how your merchandisers already think.
- Category rules
- Rule-based pricing
- Consult-to-sale flow
Live at Mir Kosmetiki. The numbers that come with it.
Pulled from live pilot data with Mir Kosmetiki — a multi-store cosmetics chain running Kwanta across pricing, promo, and category review.
“Kwanta replaces the guesswork in pricing, promo, and category. For the first time we see what each SKU is actually doing — in one place.”
MK
Mir Kosmetiki
Founding retailer · Multi-store cosmetics chain, Kazakhstan
14h → 3h
Weekly ops time, per store lead
Pricing, promo prep, and category review — from spreadsheets to one agentic review cycle on the retail graph.
+8.4%
Margin uplift on priced-for-elasticity SKUs
Trailing 14 days vs. the prior month on matched SKUs. Elasticity context per store × channel, guardrails on every move.
Weekly
Decision loop cadence
Forecasting → replenishment → promo → assortment → pricing. One graph, one rhythm, every storefront, every week.
Founders
Retail operators. Data engineers. One team.
Kwanta is built by people who have spent years running multi-store retail P&Ls and shipping production data platforms. We know why the last attempt failed.
AI
BA
Closing the gap, together.
One pilot live. Early partners opening. The category forming now. Start a short conversation from whichever side of the table you’re on.
Retailers
Pilot with us.
Fashion, cosmetics, jewelry, or specialty · 3+ stores. Early partners get honest pricing, a direct line to the founders, and the five agents live inside the first 30 days.
- 3+ stores, SKU-heavy
- 30-day go-live
- Founder-led onboarding
Investors
Back the category.
96% of retailers are now piloting agentic AI. Kwanta is the retail-native layer underneath — one graph, five agents, real P&L. Thesis, wedge, and traction, in one brief.
- Seed round, CIS + CEE wedge
- Live pilot, measured P&L
- 5 RevOps loops in production
Builders
Build with us.
Retail ops, data engineering, agent infra, design. Small senior team, real customers, real P&L on the line. Early stage — honest about it.
- Senior team, zero PMs
- Remote-first, Almaty + EU
- Equity + P&L exposure
Monthly brief
Ship notes, investor updates, open roles — once a month.
Agentic retail is moving fast. We send one crisp email when something real happens: a new agent lives in production, a pilot closes, a round opens, a role goes live. No fluff, no cadence theatre.