Launching today
CartAI
The AI agent that handles checkout.
730 followers
The AI agent that handles checkout.
730 followers
Checkout is the hard part, and most solutions clear it only where the merchant has integrated. CartAI completes checkout on any live merchant surface with no merchant-side work, cooperating with bot detection instead of evading it. One developer-first API, four products: Catalog (search and live pricing across merchants), Checkouts (clear and track orders to confirmation), Payments (PCI off your stack via Visa and Mastercard agent rails), Monetization (commissions on every agent sale).
completing checkout on any merchant surface with zero integration work is the hard part everyone else punts on, so props for actually shipping that instead of just a demo video. curious how disputes/refunds work in practice - if the agent picks the wrong variant or size because the merchant's page was ambiguous, does the normalized order data make it easy for the end user to catch that before it ships, or does it only surface after the fact like a normal order confirmation email would?
CartAI
@omri_ben_shoham1 So we do have a cart verification step where we have our QA agent check the work of our order placing agent to make sure the right variants, quantities etc were added to cart.
Also our order input api asks for the exact variant name and selection and we expect that to match what is on the website or the agent will not execute. The agent is trained not to "guess" or find "best match". It has to be a 100% match. That is also the reason we also have a catalog api service where we show the products and the exact variants available for sale with the merchants. We have made the path intentionally narrow to make sure there are no errors during execution.
The buyer-side and merchant-side approaches to this are converging fast, and the tradeoff you picked is the interesting one. Protocol-based integration only works where the merchant has already done the work, which is a thin slice of the web today, so completing on the live surface covers far more ground immediately.
The part I would watch is order state after confirmation. Refunds, partial cancellations and price changes get messy to normalize when you do not own the merchant's system of record.
Do the webhooks cover post-purchase events, or is confirmation the end of the tracked lifecycle?
CartAI
@paul_crinigan Today order confirmation is the terminal state. Once an order is placed the communication becomes direct between the merchant and the consumer. We do not insert ourselves in the middle of that conversation with email relay type techniques etc. We will see how this evolves over time
The protocol based approaches support post purchase messages a bit better(UCP for example) but there are no examples of UCP being pointed to 3rd party demand agents like @CartAI today so as you say, adoption is low at this point but we are hoping it picks up.
The agent-rails angle is genuinely interesting, especially how you're framing cooperation with bot detection as a feature rather than a workaround. One thing that would help me trust this more: add a public status page or webhook for failed checkouts, like when a merchant site changes its DOM and Catalog pricing or selection silently breaks. Right now I'd have no visibility into why an order stalled, which makes debugging painful. A simple event stream for "checkout abandoned at step X" would close that loop nicely.
CartAI
Hello @ece1080217 great points.
We do have a webhook that shows Failed when a checkout could not be completed. https://docs.cartai.ai/reference/webhooks
Also in our portal dashboard, we show the sequence of events in the checkout workflow and if there is a failure we show which step failed
We also capture screenshots as the agent is checking out so you can see a full gif of the checkout for audit and troubleshooting purposes if needed. See some examples here https://www.cartai.ai/see-it-in-action
I like that checkout works without merchant integration. How do you handle unexpected checkout changes on different stores? Publishing success rates by merchant could build more confidence.
CartAI
@alheri_murya
Thanks! That's a great question.
Handling dynamic checkout experiences is one of the core challenges we're solving. CartAI continuously adapts to changes in merchant checkout flows through our automation and validation layer, allowing us to remain resilient even as storefronts evolve.
I completely agree on merchant-level success metrics. As the platform grows, we'd love to publish coverage and success rates to give developers better visibility and confidence.
Thanks for the thoughtful feedback!
Me value the simple API approach for developers. What monitoring tools are available after checkout starts? Real time alerts could help teams respond faster.
CartAI
@hana_salazars
Thanks!
Excellent developer experience has been one of our core priorities from day one.
Once a checkout is initiated, developers can track its lifecycle through our Webhooks, receiving real-time events for important state changes and integrating those into their own monitoring or automation workflows.
We also provide a portal where teams can monitor orders, inspect checkout status, and troubleshoot issues when needed.
We believe every team has different operational needs, so rather than locking developers into a predefined alerting system, CartAI gives them the building blocks to create monitoring and notification workflows that fit their own stack.
Native email and SMS alerting are already on our roadmap and will be rolling out soon, making it even easier for teams to stay informed without building custom notification workflows.
Really appreciate the suggestion—feedback like this helps us prioritize what developers need most. 🚀
I like the idea, but checkout is one place where I’d still want a clear approval step. What happens if the price changes, an item is substituted, or the final total is higher than expected? Does CartAI pause and ask before placing the order?
CartAI
@liana_preston We do render an expected total based on item cost, shipping costs and tax using our checkout estimates api https://www.cartai.ai/product/catalog#estimates
When the agent goes to place the order and if the price encountered is higher, we have an upper bound setting that our platform customers can set around how much drift they can tolerate. If set to 0% and if the actual is even 1 cent higher, agent will not complete the order.
CartAI
@liana_preston
That's a great point, Liana. We actually have a feature in our backlog called "Confirmation Required Purchase." If this is something more developers are looking for, we'd be happy to prioritize it.
For now, our model is what we call "Intervention-Free Purchase." It works by ensuring:
The customer gives explicit confirmation before the agent begins checkout.
That confirmation includes a spending grant with a maximum amount the agent is authorized to spend for that specific order.
If the final checkout amount exceeds that approved limit, the transaction simply fails—the agent cannot overspend.
This gives developers a predictable, auditable flow while ensuring every purchase stays within the consumer's approved spending limit.
It would help a lot if the Catalog API exposed a confidence score or freshness timestamp with each price result, so we can decide when to refetch before sending the user to checkout. Right now we have to guess whether a cached price is still good enough to trust.
CartAI
@esmasagtekin
Thanks for the thoughtful suggestion!
Our approach is slightly different from a traditional Catalog API.
In addition to serving product data, CartAI's Checkout Estimates API gives developers an understanding of the expected checkout outcome— such as pricing and other checkout signals — before initiating the actual purchase.
During checkout, we also validate the latest merchant data to ensure the transaction uses the most up-to-date information available.
That said, exposing explicit freshness metadata or confidence scores is an interesting idea, and we'll definitely consider it as we continue to evolve the platform.
Thanks for the valuable feedback! Love it!