The Problem
Today, many retail and e-commerce organizations still rely on manual efforts to manage and organize their existing inventory. Retail store employees and e-commerce operations teams manually search across ERPs, warehouse management systems, marketplaces, supplier product information, and other sources of product data, filtering by SKU, product line, color, etc. and using verbal descriptions of previous season items such as "that black running shoe with the white sole from last year." Because of the manual and siloed nature of this search process, products often are classified as out of stock before they need to be, resulting in backfill orders or make-to-order manufacturing requests that are unnecessary.
That process creates expensive errors.
The "not found" event is often a search failure rather than an actual inventory shortage. A product may be available, even if the correct keywords were not used to search for it. It may be located in a different warehouse, in receiving, or even in inventory as a different article that substantially meets the customer's requirements. If a retailer does not make these discoveries, they can end up over-buying, over-producing and over-shipping, all the while disappointing their customers.
Carrying more stock results in higher carrying costs, greater markdown risk, bigger spending on manufacturing than is optimal, and hidden operational friction across sales, fulfillment and procurement teams.
The Solution
Inventory intelligence, powered by AI, transforms the process from manually looking up product information to making confident decisions with it.
Unlike many other systems that rely on exact matches of the SKU or keyword of an item, this platform also uses multimodal and semantic searching in order to locate products currently in the company's inventory.
- Product attributes (size, color, category, season, brand)
- Natural language descriptions from sales teams or customers
- Image-based similarity ("find items that look like this")
- Historical synonyms and naming variations across systems
Using AI, the platform normalises product information from around diverse sources into one searchable index or repository. It then returns the confidence level for exact matches, comparable products, in transit products, and products in back order. The upshot for retailers is that they receive the information they need to decide whether to purchase, whether to go for an in transit product and incurr extra delivery costs, to seek alternative products that are closest in match, or to replenish faster.
- Fulfill from existing inventory
- Substitute with an approved equivalent
- Wait for inbound stock
- Trigger a targeted backfill or manufacturing order
A decision flow diagram showing how semantic search results lead to specific operational outcomes: fulfilling from current stock, substituting with semantic equivalents, waiting for inbound stock, or triggering backfill orders.
This changes our approach from search and guess to search and verify.
ROI & Business Value
| Outcome | Impact |
|---|---|
| Lower operating cost | Reduces avoidable backfill and unnecessary manufacturing requests |
| Better inventory utilization | Finds existing stock before creating new supply |
| Faster order handling | Sales and support teams resolve customer requests in minutes, not hours |
| Reduced stockouts and overstock | Improves replenishment timing with better visibility into true availability |
| Fewer markdown losses | Prevents duplicate buying that leads to excess inventory |
| Cross-team efficiency | Aligns sales, warehouse, and procurement around the same real-time inventory signal |
Unlocking the biggest gain for Product teams: Better decisions. Stop treating uncertainty as a shortage.
How We Solved It with Jarvis AI
The solution combined a custom backend agent implementation with a business friendly Jarvis Chat frontend.
This is made possible by three coordinating layers: an agent gateway that secures and routes every inventory query, an agent registry that catalogs all approved data connectors across ERP, WMS, and supplier systems, and an agent orchestrator that sequences those agents to reconcile and rank results in real time. For a detailed breakdown of how these components work together, see our guide on agent gateway, agent registry, and agent orchestrator architecture.
We have built an agentic flow in the backend, utilising Jarvis Registry in order to continuously crawl and reconcile information from a multitude of sources across the leviathan that is the backend's inventory systems – ERP, warehouse management systems, supplier catalogues, and marketplace feeds to name a few. This custom agent layer allows for the generation of a single master list of inventory across the backend, ensuring that everyone has a single source of truth when making decisions throughout the business, eliminating the need to constantly cross reference information across a multitude of independent systems.
We strove to keep the data as current as possible. By reducing the number of disconnected sources a client must query to compile up-to-date inventory information, we help to facilitate near real-time status reporting. For even the most complex configurations, this agent-based approach helps to dramatically reduce latency and increase consistency across systems, resulting in a lower number of false "not found" returns.
Front-end users are able to query the inventory using Jarvis Chat without having to involve the technical team. The interface enables front-end users to search for products by size and description in natural language to retrieve actionable inventory answers.
Makes refill and backfill decisions much easier for your team. Accurately determines if existing inventory is available, whether there are alternative sources for the item, or if replenishment is even required. This builds real cost savings by reducing the number of purchase orders and manufacturing work orders for which your team is responsible.
Why This Matters for Future Retail Programs
Most retailers don't need more dashboards. What they really need is to make fewer expensive mistakes in their daily operations.
This use case demonstrates the value of applying AI to frontline decisions such as "Do we already have this item in stock?" and "Do we really need to Replenish Now?". By making accurate answers to these simple questions, significant reduction in operating cost and improved health of inventory can be achieved – all without increasing process complexity.
For teams looking to launch AI in Retail, one of the highest levers for ROI comes from inventory discovery and backfill accuracy. The architectural foundation — agent gateway, agent registry, and agent orchestrator — is what makes that accuracy possible at scale. Learn more in our deep dive on agent orchestrator workflows for retail operations.