Launching today
StoreClaw
Grow your store profits with agents that know how to sell
713 followers
Grow your store profits with agents that know how to sell
713 followers
StoreClaw is the first AI commerce platform with agents that know how to sell, so you can make more money with less effort and less stress. Connect StoreClaw to your existing store and it will study your numbers, current sales figures, and growth trajectory, and then offer proactive suggestions that it can execute on your behalf — once you give it your approval. Ask StoreClaw how your business is doing any time, anywhere. Sell more with less stress: StoreClaw.
Free Options
Launch Team / Built With
Raycast
Most "AI for ecommerce" tools still require that you copy/paste their outputs into Shopify's backend.
StoreClaw skips all that.
Connect your store via MCP and it goes in and does the thing — from bulk-editing meta descriptions, adding alt text, generating backlinks, and tweaking product copy. The SEO audit the team walked me through gave me a glimpse of the productive gains you can expect from StoreClaw.
StoreClaw isn't a chatbot, nor an "AI for your store" — it's a collection of agents that know how to sell.
It comes with 30+ preloaded skills, and can connect to where you already run your business (Shopify, WooCommerce, Amazon, eBay, plus your growth channels). It offers proactive plans that you approve before they're executed.
Use code PH300 (no card required) to claim 300 free credits to kick the tires. To get a sense for what this gets you, @marina_jiang1 told me she used 300 credits to spin up an entire storefront and run a full SEO fix across 12 skills — so it's enough to actually see what it does—not just poke around.
Congrats to @steven76 on the launch — would love to know what existing store owners want try first!
StoreClaw
@marina_jiang1 @steven76 @chrismessina
Thanks Chris — and especially for that one line: "a collection of agents that know how to sell." That's the framing we've been searching for. Every other word we tried ("copilot," "assistant," "platform") felt like we were apologizing for what the product actually does.
Two things I'd add for anyone reading this far:
First, the proactive plans Chris mentioned aren't just queued tasks — they're reasoned. The agent tells you why it thinks this listing needs rewriting before asking for the green light. So even when you say no, you walk away knowing something about your store you didn't know before.
Second, the 300-credit run Marina did was on a brand-new store. If you have an existing store with real data, StoreClaw goes deeper. It'll find revenue you didn't know was leaking.
We're early. The product is going to get a lot sharper as more operators run it against real stores. If you're one of them and you give it a try, we'll be listening closely.
HeyForm
Congrats on the launch!
One thing I'm curious about: can sellers set approval layers before actions run? Especially for pricing, inventory, or campaign changes.
StoreClaw
@itsluo Sure,but you’ll need to provide specific prompts when chatting with the AI, so it can carry out actions such as pricing updates, inventory adjustments, or marketing campaign changes according to your requirements.Welcome to reach out to us via customer.support@storeclaw.ai.
HeyForm
@lena_pan2026 Thanks for the answer! Good luck with the launch!
StoreClaw
@itsluo Thank you,we hope to provide you with the highest quality and most convenient service.
StoreClaw
@itsluo
One thing I'm curious about: can sellers set approval layers before actions run? Especially for pricing, inventory, or campaign changes.
StoreClaw
@itsluo Yes — approval layers are core to the design. Operators set scope per action type: lower-risk work (content, listing health, lifecycle) can run autonomously, while pricing, inventory, and budget changes queue for review by default. You dial it up or down per category as trust builds. Higher blast radius = more conservative default. Nobody wants an agent cutting prices autonomously at 2am.
StoreClaw
@itsluo Thank you for your support.
Feels like the hard problem here isn’t “AI for e-commerce,” it’s encoding operational judgment.
A senior operator knows when not to optimize aggressively because of seasonality, inventory risk, brand positioning, etc. Curious how much of that intuition can realistically become agent behaviour over time?
StoreClaw
@surabhi_minocha That’s a great perspective, and we completely agree. The question is difficult to measure precisely,but one thing we’re certain about is that we’re continuously working toward that goal.
StoreClaw
@surabhi_minocha
Exactly, agents handle high-frequency execution, while operator judgment is encoded as guardrails and constraints.
The operator shifts from micro-decisions to defining and refining the boundaries the system runs.
StoreClaw
@surabhi_minocha You've named the actual hard problem. Skills are commoditizing — anyone can wire up an SEO or copy agent. Judgment is the moat: knowing when not to optimize. Our approach is to make operator judgment first-class context that travels with every skill run — seasonality, inventory posture, margin tolerance, brand guardrails, launch phase. The engine isn't optimizing in a vacuum. Day-one match for a 15-year operator's gut? No. But the gap closes faster than people expect once judgment lives in the system instead of one person's head.
StoreClaw
@surabhi_minocha This is the hard part, and it’s the heart of our work.Agents follow the boundaries you set, and over time, they get better at anticipating those judgment calls, but you always hold the final call.
Lessie AI
Most "AI for e-com" tools I've tried are just dashboards with a chatbot bolted on. The fact that StoreClaw takes action rather than giving recommendations changes the math on hiring.
StoreClaw
@alexia_li
What separates this from tools that only adding AI features is the action layer. Every existing tool is still a dashboard — it shows you what's happening and tells you what to do. StoreClaw actually doing the thing is the leap.
StoreClaw
@alexia_li
That’s exactly the gap we’re targeting. StoreClaw isn’t built to advise operators — it’s built to be one, with real execution across workflows instead of static insights.
StoreClaw
@alexia_li Nailed it. Dashboard + chatbot is still a productivity tool — the human is doing the work. An engine that executes changes the category, and the hiring math with it. The question shifts from "do I need another SEO/lifecycle/content hire" to "what channel can I launch next without adding payroll."
StoreClaw
@alexia_li This is the core difference. Chatbots tell you what to do; our agents do it. That’s where the real leverage comes in,no more hiring just to click through the same repetitive tasks.
Triforce Todos
does it handle seasonal shifts? My business has huge Q4 peaks and summer deaths. If I connect it in January will it know that February is supposed to be slow, or will it panic and recommend I cut everything?
StoreClaw
@abod_rehman You can share your industry background and historical business data with the AI, and it will identify patterns based on your business performance and industry cycles.
That said, providing clear context and goals in your prompts can help the AI make even more accurate decisions for your business.
StoreClaw
@abod_rehman Fair concern — "panic and cut everything" is the failure mode of most optimization tools. Two things. One, the engine reads category and competitive seasonality, not just your store in isolation — so day one in January, it already knows February is a trough for your category. Two, your own store history gets ingested at connection, so your specific seasonality shape (Q4 ramp, summer dip) feeds into how current performance is read. Slow February isn't a problem to solve — it's a baseline to plan against.
StoreClaw
@abod_rehman
No worries at all — we’ve got that covered. StoreClaw is built to handle those shifts safely without overreacting 🚀
StoreClaw
@abod_rehman Great question,we built this specifically for seasonality.It won’t panic. On day one, it uses category-level benchmarks and historical market trends to know February is typically slow. Then, as your own data comes in, it learns your specific seasonal pattern and adjusts its recommendations accordingly.
Congrats on the launch! Love the direction. My biggest question would probably be trust — especially when autonomous systems start making operational decisions that affect revenue.
StoreClaw
@crystalmei
Trust has two parts:
Competence: Does it make good decisions?
Alignment: Does it optimize for what you actually care about?
It’s built through transparency, human approval for high-impact actions, and the system knowing when to pause. The goal isn’t full autonomy. It’s cheaper and faster than human oversight.
StoreClaw
@crystalmei Thank you. Before AI executes key operational actions, you’ll be able to review and confirm important parts so the system stays aligned with your preferences and business needs.
StoreClaw
@crystalmei Thank you, great point.
Trust is core to how we built this. Agents always generate plans first and nothing is executed without user approval, so you stay fully in control of any changes affecting revenue.
Appreciate you raising this, curious what use cases you’d be most cautious with.
StoreClaw
@crystalmei Appreciate the question,we built control into the core. All operational changes come with clear reasoning, and you review and approve everything before it impacts your revenue.
Congrats on shipping this! Does StoreClaw have a learning period, or does it optimize from day one using a pre-trained baseline? Curious how it handles a new SKU with zero sales history.
StoreClaw
@jocky Thanks Jocky! Both, actually. Day one, StoreClaw runs on pre-trained baselines from category-level benchmarks and public signals (search trends, competitor listings, public data) — so a zero-history SKU still gets a real plan, not a guess. Then as your store accumulates data, it shifts to your actual numbers. The handoff is gradual, not switch-flipped.
StoreClaw
@jocky
Thanks, Jocky! Both.
From day one, StoreClaw operates on pre-trained category baselines plus public market signals (search trends, competitor activity, and broader category data), so even a brand-new SKU gets a grounded strategy, which is not a blind guess. As your store data comes in, it gradually shifts toward your own performance history. The transition is smooth and continuous, not a sudden switch, so optimization becomes increasingly tailored to your business over time.