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Skarp Global

Agentic commerce

Help customers find, choose and track what they buy.

We build AI shopping and order assistants connected to your catalogue, inventory and order systems - so answers reflect what you actually sell and what is actually in stock.

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storefront / shopping assistantConcept interface
Concept interface: a shopper searches in natural language for waterproof, wide-fit trail shoes under $150, the assistant recommends matching products with reasons, and shows an order status update. Illustrative products and prices.
Waterproof trail shoes, wide fit, under $150
  • Ridge GTX trail shoeBest matchWaterproof · wide fit$139
  • Fell Runner 3Waterproof · regular fit$124
Order #48213 · arriving ThursdayIn transit

Commerce challenges

Keyword search and static filters only go so far

  • 01

    Needs, not keywords

    Customers describe situations - “shoes for wet trails, wide fit” - that rarely match catalogue attributes directly.

  • 02

    Comparison effort

    Choosing between similar products means reading specifications side by side, which many shoppers abandon.

  • 03

    Post-purchase questions

    Where is my order, can I change it, how do I return it - routine questions with answers in your systems.

  • 04

    Accuracy matters

    An assistant that invents a feature, price or delivery date damages trust faster than no assistant at all.

Our product · Built by Skarp Global

Storely - our AI commerce agent for online stores

From shopper intent to completed order, in one conversation.

Storely is the ecommerce AI assistant we’re building at Skarp Global. It runs as a conversation on a retailer’s existing storefront and takes shoppers from intent to a completed order, working from the store’s own catalogue, offers and policies.

  • Product discovery
  • Live offers
  • Basket building
  • Checkout in the conversation
  • Order tracking
  • Policy-grounded support with human handoff
Visit storelycommerce.com (opens in a new tab)Watch the demo of Storely (opens in a new tab)
storely / shopping conversationIllustrative
Illustrative Storely conversation: a shopper asks for a chicken alfredo for four, Storely matches seven of nine ingredients to store products and asks the shopper to choose for the other two, then adds the matched items to the cart.

A creamy chicken alfredo for four, please.

I matched 7 of 9 ingredients to products in stock. Two need your pick.

FettuccineParmesanHeavy creamChicken breast+3 more2 to choose
7 items added to cartCheckout in chat

What we build

Commerce assistants grounded in your catalogue

Each capability reads from your own product, inventory and order data - never from guesswork.

  • Product discovery

    Semantic search that maps everyday language to catalogue attributes, categories and availability.

  • Conversational shopping

    Assistants that ask clarifying questions, narrow options and explain trade-offs between products.

  • Recommendations

    Suggestions based on stated needs, browsing context and catalogue rules - with the reason shown.

  • Order assistance

    Order status, delivery updates and return eligibility, answered from your order and logistics systems.

Illustrative shopping journey

From a vague need to a confident order

How an assistant might guide one customer, using live catalogue and order data at each step.

Conceptual journey. Product data and rules come from your own systems.

Flow

Illustrative

  1. 01Customer describes a need

    “Waterproof trail shoes, wide fit, under $150.”

    storefront chat

  2. 02Search the catalogue

    The need is mapped to attributes - waterproof, wide fit, price - and filtered by stock in the customer’s size.

    catalogue · inventory

  3. 03Recommend and compare

    Two or three matching products, with the attributes that make each one a fit.

  4. 04Assist after purchase

    Delivery updates and return eligibility answered from the order system.

    order management

Commerce system integrations

Connected to how your store actually runs

Agentic commerce works when the assistant can read - and, where appropriate, act on - your commerce data.

  • Catalogue and attributes

    Product data, variants and specifications structured so the assistant can reason about them.

  • Inventory and pricing

    Live availability and price checks so recommendations stay accurate.

  • Orders and logistics

    Read access to order status and shipment tracking; write actions only where you approve them.

  • Merchandising rules

    Business rules - margins, promotions, exclusions - respected in what the assistant suggests.

Implementation approach

How we would approach it

Every engagement is shaped by your systems and constraints. A typical path looks like this.

  1. 01

    Audit product data

    Assess catalogue quality and attributes - the foundation for useful search and recommendations.

  2. 02

    Define the journeys

    Choose where assistance adds most value: discovery, comparison, checkout questions or post-purchase.

  3. 03

    Build and evaluate

    Test against realistic shopper queries and check every answer against catalogue truth.

  4. 04

    Measure and refine

    Monitor conversations and outcomes, then improve prompts, data and coverage.

Related solutions

  • AI Customer Support Agents

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    Explore AI customer support

  • Predictive and Decision Systems

    Turn business data into forecasts, operational insights, anomaly detection, and decision support.

    Explore predictive systems

  • Enterprise AI Solutions

    Integrate AI into your business through knowledge systems, retrieval, software integrations, evaluation, security, and production infrastructure.

    Explore enterprise AI

Have a business problem AI could solve?

Let’s understand the challenge and explore what a useful solution could look like.

Discuss your projectContact us

Or email info@skarp.global