Increff

Increff presents a current retail SaaS suite spanning Smart Planning, Inventory Intelligence, Frictionless Fulfillment and Connected Retail; it reports 700+ brands and 35+ countries.

Increff describes itself as a Bengaluru-headquartered AI-powered B2B retail SaaS company with New York and Bengaluru branches, 250 employees, 700+ brands and 34+ countries; it lists Rajul Jain as co-founder/CEO and Vishal Raj as co-founder/CTO.

The policy identifies NextSCM Solutions Private Limited, a Bengaluru registered office and US/UK operations, and names Salesforce and Google Cloud as subprocessors or infrastructure.

Increff reports ISO 27001, SOC 1 Type 2 and SOC 2 Type 2 controls, role-based access, logging, monitoring and encryption; it explicitly says it does not process payments.

TVS Capital identifies Increff as a portfolio company founded in 2016 by Rajul Jain, Anshuman Agarwal and Romil Jain, providing products and services in India and MEA.

Microsoft Marketplace lists Increff's merchandising SaaS for planning, allocation, replenishment, markdown and forecasting.

The live module creates attribute-level demand, range and buy plans, calculates reordering quantities using lead time, safety stock and cover days, and allows user overrides.

MFP supplies AI growth suggestions, forecasts, scenarios, version freezes, alerts and collaborative approvals; users can override suggestions and planned discount strategies pass to markdown optimization.

The current WSSI/MSSI module provides weekly and monthly sales, stock and intake planning and open-to-buy control.

The module uses True ROS, Store-StyleRank, assortment, size mix and configurable guardrails to allocate and replenish; it can automate stock transfers and replenishment orders when demand or thresholds trigger.

The module recommends discount depth and timing at store, channel and SKU level and can integrate those decisions into brand or marketplace systems without manual data transfer.

Co-Pilot presents dashboards and quantified inventory, pricing and sales recommendations. Its headline claims fully automated decision-making and zero errors, while detailed copy describes recommendations rather than direct writes.

The AI page markets forecasts, what-if analysis, transfer and markdown suggestions, intelligent routing, automatic fallback, OMS query assistants, predictive returns and churn signals; it does not describe a multi-agent architecture.

The WMS directly executes scan-based receiving, putaway, wave picking, packing, dispatch, returns QC and disposition, FIFO/FEFO, kitting, B2B ASN/PO reconciliation, invoicing and cross-dock workflows; it includes 80+ dashboards, alerts, audit logs and role access.

OMS auto-allocates and reassigns orders across warehouses, stores and vendors, synchronizes inventory, routes and splits orders, centralizes returns and cancellations, and applies return QC rules and dispositions; it claims 1,300+ integrations.

The store system supports nearest-node routing with automatic fallback, BOPIS, BORIS, endless aisle, scan-pay-go self-checkout, returns, staff workload allocation and audit logs.

The module creates and bulk-generates labels and QR codes, synchronizes product masters, tracks items and exposes APIs; QR experiences can surface similar products and customer feedback.

The system exposes vendor inventory, automatically routes orders to vendors by stock and SLA, and tracks fulfillment, returns and reconciliation.

CWAS is a managed warehousing service across roughly 30 Indian cities and 36 warehouses, priced pay-per-use by cubic feet stored and orders processed; company-reported context includes 4.84 million live inventory and 34 million+ order items.

The live catalog lists Shiprocket and other logistics, marketplace, ERP and web-store integrations, supports REST APIs, webhooks and scheduled sync, and says connectors typically take two to four weeks.

The partner program covers implementation, consulting, sales, cloud and technology partners including Accenture, Infosys, PwC, Microsoft and Google; payment providers are partners, not proof that Increff processes payments.

The official documentation covers cloud OMS, WMS and store fulfillment, including inventory synchronization described as under 30 seconds.

The architecture documents OMS ingestion, allocation, SLA routing, reservation and returns across Channel Connect, WMS and store fulfillment.

The documentation exposes authenticated API endpoints, payloads, data fields and error behavior for standard channel connections.

Integration guides document authentication, rate limits, sandbox testing and production setup.

The official architecture surface documents information flow among Increff product components and external systems.

The release notes document order rejection triggering order hopping, posting retries and audited actions, demonstrating current deterministic exception execution.

The documented API estimates likely order splits and delivery dates for fulfillment scenarios.

The official support portal provides searchable help content and ticket submission for product users.

The support article documents operational Shiprocket order-creation, AWB, label, pincode, address and phone-validation errors and resolutions.

An anonymous sportswear brand achieved 10% improved inventory health and reported 4% and 31% revenue lifts across transfer lanes.

An anonymous UK 3PL reported 20% productivity improvement, 67% fewer clicks and sub-week deployment.

GIVA is reported to have moved inventory accuracy from below 95% to above 99.5% while sustaining 50% more order volume; the hub displayed a different date.

An anonymous footwear company reported 2–3x inventory turns, 99.9% bin accuracy, shorter SLA and lower holding days; its 300% increase and 3x wording conflict arithmetically.

An anonymous menswear brand reported 30% higher sales quantity, 24% healthier inventory and movement of 70% of slow stock.

An anonymous fashion brand reported 300% online-sales growth, 4–5x ecommerce revenue and 99.9% SLA compliance; the growth statements have unresolved scope or arithmetic differences.

An anonymous home-furnishing brand reported regional fulfillment rising from 78.5% to 91% in five months.

An anonymous ecommerce company reported 138% higher rate of sales in seven months plus picking, packing and receiving improvements.

Brands For Less reported 99.9% fulfillment accuracy, 59% less supervision and multiple warehouse-efficiency changes; this is the older source for the merged BFL case.

Increff and ChannelEngine describe a named MENA marketplace-expansion solution with unified inventory and execution; no realized customer outcome is quantified.

An anonymous fashion brand reported 100% inventory accuracy, 100% fill and a 300% operations increase in three weeks.

BESTSELLER is reported to have achieved near-complete demand and store analysis and allocation accuracy through demand planning.

An anonymous casual-wear brand reported 28% higher sales velocity, 13% healthier inventory and 50% lower NOS allocation.

An anonymous denim brand reported 22% higher revenue, 18% lower working capital and 80% less allocation effort.

An anonymous designer brand reported 71% higher ROS, about 6% more margin and twice-frequent pricing decisions.

An anonymous formalwear brand reported inventory health moving from 70% to 82%, while body copy separately calls the improvement 8%; holding fell 25%.

An anonymous sportswear brand reported sell-through margin rising 8.6 percentage points, markdown depth improving and pricing decisions becoming two to three times faster.

An anonymous watch retailer reported 24% higher revenue per day, 17% higher MRP sales and 30% higher top-seller availability.

An anonymous Middle East retail group reported 16.8% higher sales units and AED 2.29 million incremental sales comparing 2024 and 2025.

An anonymous saree retailer reported reducing replenishment decision time from about one day to 30–40 minutes.

An anonymous value-retail chain describes better price-point and planning decisions without a quantified realized outcome.

An anonymous electronics retailer identified a potential 9.1% ASP increase and potential 22% revenue loss; these are modeled opportunities, not realized outcomes.

Brands For Less reported 30% higher warehouse performance, 25% lower cost, 99.9% accuracy and 59% less supervision; this is merged with the older BFL page.

Celio reported full-price sell-through five percent higher than plan across a 600-plus point-of-sale network.

DaMensch reported 99.997% inventory accuracy, 100% SLA fulfillment and roughly 25% lower warehouse cost.

Hirawats reported 36% higher revenue, twofold inventory turns and 85% less planning time.

Jaypore's page conflicts between style availability rising from 45% to 87% and top-seller availability reaching 65% or improving 23%; the headline calls 87% top-seller availability.

Libas reported turnaround falling from seven-to-nine to four-to-five days plus lower processing, returns, cancellations and failed-to-ship rates.

Pepe Jeans reported saving more than three days, reaching 85–90% stock utilization and optimizing about 6% of buy quantity.

Styli reported 99.97% SLA fulfillment, 13-hour average dispatch, approximately 99.8% order accuracy and 99.5% inventory accuracy.

The Indian Garage Co. reported scaling from 3,000 to 25,000–30,000 daily orders with 99.99% accuracy and under-48-hour fulfillment.

An anonymous UAE fashion company reported fulfillment rising from about 50% to 94% within one to one-and-a-half weeks.

PUMA reported regional utilization rising from 42% to 57% and logistics-cost savings of about $16,000.

Lenskart's four-week A/B test reported 23% higher store revenue, 28% higher per-piece revenue and a three-point merchandise-sold difference.

An anonymous celebrity-fashion brand reported 13% higher full-price sell-through, 25% lower assortment width and 12% higher ASP.

An anonymous ecommerce company processed 11,200 refurbished units per month on average and 16,900 at peak with full traceability.

Woodland reported footwear unit growth on 18% lower stock and multiple stockout, days-on-hand and discount changes; several stated deltas conflict with endpoint arithmetic.

An anonymous Indian fashion retailer reported near-complete accuracy, full fulfillment for seven months and about 50% lower warehouse manpower cost.

Pintola reported 79% lower order-processing time, 22% lower turnaround time, 50% fewer returns and cancellations and 30% more space utilization.

PUMA reported 99.9% fulfillment, 40% lower holding, threefold online sales rate and processing falling from three days to one day.

GlobalBees reported inventory accuracy rising from below 70% to 99.9%, 99.95% SLA and 60% lower processing time.

An anonymous jewellery brand reported 34.5% higher revenue, 26.3% more sales units and 18.7% higher revenue per store.

An anonymous sportswear brand page gives endpoint changes that conflict with its stated deltas for excess inventory, top-seller availability and size mismatch.

An anonymous large Indian fashion retailer describes moving from six-month seasons to monthly buying and weekly drops across more than 5,000 stores.

The policy governs prices of products sold through an Increff marketplace surface; it does not disclose B2B SaaS subscription pricing.

The live hub inventories the official customer-story corpus used to enumerate 44 canonical case URLs.