Launching today
StoreClaw
Grow your store profits with agents that know how to sell
1.3K followers
Grow your store profits with agents that know how to sell
1.3K 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.
@marina_jiang1 @steven76 @chrismessina @steven_zhou8 Hey Steven, congrats on shipping 👋
The GEO/AI search positioning ("recommended by ChatGPT and Perplexity") is the right wedge but also the hardest to measure honestly. Google SEO has years of established rank tracking, AI search citations drift week to week and platform to platform. How does StoreClaw measure GEO impact over time, and is the agent continuously re-optimizing based on citation feedback, or doing one-shot passes? Asking because this is where most "AI-search-aware" tools either nail it or quietly fall back to legacy SEO playbooks.
StoreClaw
@marina_jiang1 @steven76 @chrismessina @arturbrugeman Thanks Artur, fair question and an honest one. GEO measurement is noisier than SEO and we treat it that way: scheduled prompt panels against ChatGPT, Perplexity, and AI Overviews, tracking citation presence, position, and share-of-voice. We log drift rather than smooth it.
Re-optimization is continuous but on longer windows than SEO (30-day default, not 7) — because week-to-week citation flicker is often noise.
Where we're still early: predictive attribution from content change → citation outcome. Nobody's nailed that, us included. Closer to "measure honestly and iterate" than "predict precisely" for now.
@marina_jiang1 @steven76 @chrismessina Love this Idea might try it for my shopify store
Love the use of MCP to let agents execute multi-step SEO and copy changes directly onto live platforms like Shopify. When the agent is making proactive bulk edits, how does the architecture handle state reversals if a specific optimization negatively impacts conversion rates? I am curious if there is an automated rollback mechanism that allows the agent to self-correct based on live revenue signals.
StoreClaw
@rugved_chavan Thank you for your support,really thoughtful question,welcome to discuss this deeper with you at customer.support@storeclaw.ai.
StoreClaw
StoreClaw
@rugved_chavan Thanks for asking about our technical architecture! All bulk optimizations in StoreClaw require your manual approval before going live. We have built-in version history + real-time monitoring.
For deeper technical details, feel free to reach out to our team at custosmer.support@storeclaw.ai.
Using Claude for core intelligence, ChatGPT for intent, and Gemini for visual generation is an interesting multi-model architecture. Most AI products pick one provider and live with its limitations. The tradeoff is complexity — how do you handle latency when a single user query needs to hit three different models?
The "proactive suggestions it can execute on your behalf" approach with approval gates is the right pattern. I build automated agents and the biggest trust-builder is showing the user exactly what you're about to do before doing it. Pure autonomy sounds cool but nobody wants an AI agent repricing their entire inventory without asking first. How granular are the approval controls — can merchants set rules like "auto-execute anything under $X impact but ask for everything else"?
StoreClaw
@ytubviral We adopt multi-model collaboration to gather advantages from each one, and we are continuously optimizing scheduling logic to lower latency and ensure smooth usage experience.
Our approval mechanism supports fine-grained permission settings. Merchants can set personalized rules freely. Operations with small impact can run automatically, while relatively important adjustments will be paused and wait for your manual review. We keep optimizing this function to fit various usage scenarios.
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.
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.
StoreClaw
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.
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."