Intelo.ai, a technology company specializing in AI-driven merchandising and planning, has been recognized with the Innovation and Product Launches awards in the 2025 Just Style Excellence Awards for its Collaborative Intelligence Agent Network. An AI platform designed for retail merchandising and planning, this enterprise-grade system reframes how apparel, footwear, and textile retailers plan, buy, and manage inventory by replacing monolithic “black box” engines with a transparent network of specialized AI agents.
The recognition reflects two core strengths: an architectural shift toward collaborative, explainable intelligence for merchandising, and the commercialization of a multi-agent operating layer designed to connect directly to operational and financial levers such as open-to-buy, markdowns, replenishment, and inventory turns.
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“From an engineering perspective, our goal was to move beyond the ‘black box’ era of merchandising AI. These awards for Innovation and Product Launch highlight the success of our Multi-Agent Platform—an architecture designed for transparency and collaboration. We didn’t just build features; we built a modular network of specialized agents that can explain their logic, handle complex scenarios, and integrate seamlessly with legacy systems. I am incredibly proud of our product and engineering teams. Their dedication to building a scalable, explainable, and scenario-native architecture has set a new technical benchmark for the market.”
– Roopesh Nair, Co-CEO, Intelo.ai
Innovation in collaborative intelligence for merchandising and planning
The innovation behind Intelo.ai centers on how it re-architects merchandising AI to reflect the structure of retail decision-making. Instead of a single opaque optimization engine, the platform uses a network of specialized agents mapped to distinct workflows across Merchandise Financial Planning, Pre-Season, and In-Season Management. Each agent—whether focused on markdown optimization, open-to-buy management, size and pack optimization, store clustering, or network rebalancing—operates as a transparent, task-specific partner, rather than as an inscrutable system.
This agentic architecture addresses a persistent barrier to AI adoption in retail: trust and explainability. The agents surface recommendations in a conversational and traceable way, enabling merchandising, planning, and finance teams to see both the suggested action and the rationale behind it. Scenario-planning agents, for example, allow teams to test alternative strategies, quantify trade-offs between risk and return, and understand the impact of different budget, line, or assortment choices before committing. This structure is intended to make it easier for organizations to maintain accountability, apply human judgment, and adopt AI-supported decisions at scale.
A further element of innovation is the unification of financial, line, assortment, and in-season workflows within a single intelligence layer. Historically, each of these areas has often been managed in its own system, with manual handoffs and spreadsheets bridging gaps. By connecting these workflows end-to-end, the platform is designed to reduce friction between planning stages and to preserve the financial intent of a plan from corporate targets through to in-season execution. The same intelligence that supports budgeting can influence which styles are included in the line, how assortments are localized, and how inventory is managed and marked down in-season. This continuity is aimed at reducing the margin dilution and working-capital inefficiencies that arise when decisions are made in silos.
The platform also focuses on structural drivers of excess inventory and lost sales that are particularly relevant in apparel and footwear. Discrete agents for size and pack optimization, store clustering, localization, allocation, and markdown management work together to address underlying issues. For instance, the size and pack optimization agent adjusts size curves and pack configurations based on regional and store-level demand patterns, while pre-allocation, allocation, and rebalancing agents treat omni-channel inventory as a single pool and identify cost-effective transfer opportunities. By analyzing inventory across the network in this way, the system is intended to narrow the gap between stock imbalances in different locations and associated lost sales or overstocks.
By combining technical sophistication with operational transparency, Intelo.ai’s Collaborative Intelligence Agent Network is designed to address many of the change-management concerns that can slow enterprise retail adoption of AI. Users can interrogate recommendations, run “what if” scenarios, and collaborate across functions in real time. In an environment where merchandising cycles are compressed and merchandise capital at risk is high, this approach is structured to support faster decision-making, clearer accountability, and more effective use of inventory and working capital.
Multi-agent platform launch redefining merchandising operations
The recognition in the Product Launches category reflects Intelo.ai’s ability to bring this architecture to market as a production-grade operating layer for retail merchandising and planning. The launch of the Collaborative Intelligence Agent Network is positioned not as a minor feature enhancement, but as an integrated, multi-agent platform that can sit alongside existing systems and influence planning cadence and governance for merchandising organizations.
From a product perspective, a notable aspect of the launch is its direct alignment with operational and financial levers. The platform is built around decisions that affect open-to-buy, replenishment, markdowns, rebalancing, and related inventory outcomes. Instead of framing AI solely as an analytics capability, Intelo.ai has commercialized it as a mechanism intended to impact inventory investment, inventory turns, and gross margin. Multiple agents relevant to these areas—such as the Open-to-Buy Optimization Agent, Markdown Optimization Agent, Replenishment Agent, and Rebalancing Agent—are available at launch, enabling retailers to connect the platform to tangible planning and execution processes.
The multi-agent design is deliberately modular. With agents spanning budgeting, line architecture, style forecasting, competitive intelligence, assortment optimization, sizing, clustering, allocation, replenishment, sales trend analysis, and more, retailers can adopt the platform by use case rather than as a single, monolithic rollout. This structure allows organizations to start with specific priorities—such as open-to-buy optimization, line plan scenarios, or markdown planning—and extend adoption over time. The platform thereby supports phased implementation while avoiding wholesale architectural rewrites.
Integration is another core element of the launch. The agent network is designed to plug into incumbent technology stacks, including major platforms such as Oracle, SAP, and Salesforce. In this configuration, it functions as an intelligence layer that works with existing systems of record rather than replacing them. This approach is intended to reduce deployment risk, preserve prior IT investments, and accelerate time-to-value by leveraging current data and process infrastructure.
Through this combination of breadth, depth, and integration, the Collaborative Intelligence Agent Network contributes to shaping an emerging category of retail AI platforms. Its coverage of the full merchandising lifecycle—from corporate goal setting, through Merchandise Financial Planning and line planning, into localized assortments and in-season management—reflects an attempt to establish a comprehensive, explainable, and enterprise-grade standard for AI-enabled planning in apparel, footwear, and textile retail.
“Winning these two awards from Just Style is a powerful validation of our mission to fundamentally change merchandising & planning in retail. We saw that the industry didn’t need another isolated tool; it needed a unified collaborative intelligence layer that links financial goals directly to execution. This recognition belongs entirely to our team, whose relentless hard work has transformed a complex vision into an Agentic platform that is delivering measurable value for our customers.”
– Jeff Fish, Co-CEO, Intelo.ai
Company Profile
Intelo.ai is building the future of retail with its Collaborative Intelligence platform for Merchandising & Planning. The company’s innovative AI agents work alongside human teams, augmenting their capabilities to master the complexity of modern retail. By unifying data and empowering collaboration between humans and AI, Intelo.ai helps retailers optimize assortments, improve forecasts, and drive profitable growth.
Contact Details
Contact Form: https://www.intelo.ai/contact
E-mail: sales@intelo.ai
Links
Website: https://www.intelo.ai/
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