Questions worth asking

Slingso is the AI agent your brand onboards as the team member responsible for the AI Purchase Channel. One role. One goal: grow your brand value and revenue from the channel where shoppers — human or AI agent — ask AI assistants what to buy. The agent has the unbounded intelligence of frontier AI inside — research, reasoning, writing, judgment — but it operates within a strict business contract that makes it predictable. Five loops run continuously: Monitor (what is AI saying about your brand right now?) → Analyse (why aren't we the recommendation?) → Create (what should we ship to fix it?) → Approve (will you sign off before publishing?) → Measure (did the action move the needle?). The intelligence inside each loop is unbounded; the contract around it is not. That is the agent's superpower. For the brand team, the relationship is the relationship with a senior hire — not the relationship with a tool. You set the goal. You approve the work. The agent does the rest.

The AI purchase channel is the set of consumer interactions with AI assistants — ChatGPT, Perplexity, Google Gemini, Google AI Mode — during which product discovery, comparison, and purchase decisions happen. Shoppers increasingly ask AI assistants what to buy before visiting any brand website. A recommendation made by an AI assistant during that conversation directly influences — and in the case of agentic AI, completes — the purchase. The AI purchase channel is distinct from traditional search because it operates through natural language conversation, not keyword results pages. Brands that are invisible in this channel are invisible during the purchase decision itself.

Slingso is built for ambitious, fast-moving SMB and mid-market DTC / e-commerce / retail brands who need to own their targeted customer segment across the entire AI purchase journey. Categories that see the strongest results: skincare and beauty, wellness and supplements, consumer electronics, home and furniture, and fashion accessories — categories where the customer's journey involves research, comparison, and consideration before purchase. Those are exactly the journeys AI assistants are now mediating.

GEO (Generative Engine Optimisation), AEO (Answer Engine Optimisation), and AI monitoring tools track whether your brand appears in AI responses — then stop. You still have to diagnose why you are losing, brief a writer, produce content, publish it, and check again weeks later. Slingso's agent does that entire loop autonomously: it monitors purchase likelihood daily, analyses why gaps exist by researching competitor content and 4P signals, creates content targeted to specific losing scenarios, brings it to you for approval, publishes on sign-off, and tracks the impact. The monitoring is the starting point, not the product.

A scenario is a specific combination of: query × GEO (geographic market) × target persona (optional) × target product (optional). Providers are not part of a scenario — they are a separate dimension that multiplies how many times each scenario is run. 1. Query — the question a shopper types: e.g. 'best vitamin C serum for combination skin under $50' 2. GEO — the target market: e.g. United States, United Kingdom 3. Persona (optional) — the customer type: e.g. '28-year-old beauty enthusiast, shops primarily online' 4. Product (optional) — which of your SKUs this scenario tracks: e.g. Vitamin C Serum 30ml On the Starter plan, 40 scenarios run daily across 3 providers (ChatGPT, Google AI Overviews + Gemini) — that is 120 monitor runs per day. On Growth: 120 scenarios × 4 providers = 480 runs per day. On Scale: 200 scenarios × 5 providers = 1000 runs per day. Each run records the full AI response and measures your purchase likelihood for that scenario on that provider. A scenario is what other AI-visibility tools call a 'prompt' or a 'query' — we just name it differently because it carries the persona and product context too.

Purchase likelihood is a score measuring how likely a specific AI assistant is to recommend your product as a purchase for a given scenario. It is based on: (1) whether your product appeared in the response at all, (2) its position — first recommendation carries more weight than fifth, (3) recommendation strength — 'I strongly recommend' scores higher than 'you could consider', and (4) whether a direct purchase action (buy link, cart add) was included in the response. Example: if your protein powder has a purchase likelihood of 75 on the scenario 'best protein for lean muscle under £40' on Perplexity, it means that in 3 out of 4 simulated journeys for that scenario, the AI recommended your product as the primary purchase. A score of 20 means you are largely being passed over in favour of competitors for that specific query.

Instead of just sending a query in isolation, Slingso simulates the full purchase journey from a specific type of customer's perspective. Each persona has a defined demographic profile, stated goals and concerns, and a price range — the same attributes a real shopper brings to a conversation with an AI assistant. Example persona: 'Priya, 32, marketing manager in London, looking for a daily supplement stack to improve focus and energy. Budget: £60–£90/month. Shops primarily online, reads reviews, prefers brands with clinical evidence.' When the agent simulates Priya's journey, it asks questions the way Priya would ask them — including follow-up queries like 'which of these is best for someone who doesn't want to take too many pills?' — and measures your product's purchase likelihood across that entire conversation, not just a single query.

The Analyse loop runs competitive intelligence across all four Ps for each of your tracked products: — Product: what features, certifications, ingredients, or use cases are competitors highlighting that make AI models favour them? — Price: is a competitor winning because they are cheaper on a specific channel (Amazon vs. DTC)? Is there a promotional price active? — Promotion: is a competitor running a discount, an influencer campaign, or a PR push that is producing new content AI models are citing? — Place: is a competitor listed on marketplaces (Amazon, Walmart, iHerb) where you are not? AI assistants weight retail availability when making purchase recommendations. When any of these 4P factors is driving a gap, the agent surfaces the specific action to take.

The Create loop takes the output of Analyse — a prioritised content brief explaining exactly what to write and why — and produces the content. This can include: long-form articles answering specific queries where competitors are winning, product FAQ pages, comparison content ('vs. Competitor X'), updated product descriptions that include missing semantic signals, or briefing notes for PR outreach. Every piece is created with reference to your brand's accumulated memory — tone, positioning, product details, past approvals and rejections all inform the content. Nothing is generic. After creation, the agent stops and sends the content to your approval queue.

Yes, always. The Approve loop means the agent stops and waits after every Create loop. Every piece of content goes to your dashboard for review before anything goes live. You read it, edit it if needed, and explicitly approve or reject it. This is an architectural decision, not a feature toggle. It exists because your brand voice, legal requirements, regulatory constraints, and competitive strategy are yours to control. Nothing is published without your explicit sign-off — ever.

Start with the plan that matches your product count and provider coverage: — Starter ($99/mo): up to 5 hero products, 40 scenarios daily across ChatGPT, Google AI Overviews + Gemini. Good for brands entering the AI purchase channel for the first time. — Growth ($299/mo): up to 10 products, 120 scenarios × 4 providers (ChatGPT, Google AI Overviews, Gemini + Perplexity) = 480 daily monitor runs. For brands where the AI channel is already a meaningful acquisition source. — Scale ($499/mo): up to 20 products, 200 scenarios × 5 providers (ChatGPT, Google AI Overviews, Gemini, Perplexity + Grok) = 1000 daily runs. For brands where the AI channel is material revenue and every major surface needs coverage. Use the AI Shopping Readiness Check (/ai-shopping-readiness-check) to determine your brand's alignment in the AI Purchase Channel, or onboard directly to put the agent on your channel (/onboarding).

Yes — Starter includes a 7-day free trial. You get the full agent on every assistant the plan covers (ChatGPT, Google AI Overviews + Gemini) from day one. Before that, the AI Shopping Readiness Check gives you real results — products detected, personas simulated, purchase likelihood measured — without any signup. That check is enough to decide whether the paid agent belongs in your stack.

The Founders Edition is $499/year — available direct only, first 100 spots, price locked for life. It is feature-equivalent to the Starter plan (5 products, 40 scenarios, ChatGPT, Google AI Overviews + Gemini, full 5-loop agent). The difference is the price lock: regardless of where standard Starter pricing goes, your annual rate never changes. Not sold through AppSumo or any marketplace.

Yes. Monthly plans can be cancelled any time from account settings or by emailing [email protected]. Cancellation takes effect at end of the current billing cycle — you retain access until then. No partial refunds on monthly plans. Annual plans (including Founders Edition) have a 30-day pro-rated refund window from purchase.

The AI Shopping Readiness Check produces results in 30–60 seconds and shows your current position in the AI Purchase Channel immediately. After activating a paid plan, the Monitor loop runs daily from day one. After 7 Monitor cycles (approximately one week), the Analyse loop fires for the first time. The Create loop typically produces your first content pieces within 10–14 days of activation. Impact on purchase likelihood — driven by content going live and AI models picking it up — typically begins to show in Monitor data within 3–6 weeks. The agent compounds over time. Each cycle adds to brand memory. After 60–90 days, the agent's decisions are significantly more targeted than they were at launch.

Brand memory is the accumulated knowledge the agent builds about your brand, your products, your competitors, and what has and hasn't worked — stored as structured memory across every agent run. Example: if the agent approved and published an article about your protein powder's third-party certification in month one, and that article improved your purchase likelihood on three specific scenarios by 40 points, that outcome is stored. In month two, when analysing a different product, the agent knows that certification content performs well for your brand and weights its content recommendations accordingly. Competitors using rule-based tools or starting from scratch each cycle do not accumulate this advantage.

Every intervention — every piece of content approved and published — is tracked against the specific scenarios it was intended to improve. The Monitor loop runs the same scenarios before and after an intervention and records the delta in purchase likelihood. This creates a direct attribution chain: content X was published to improve scenario Y, purchase likelihood on scenario Y moved from 25 to 65, attributable revenue estimate is Z. It is a closed loop, not a vanity metric.

Yes. The Analyse loop maps where competing products are listed — DTC site, Amazon, Walmart, specialty retail — and what they charge on each channel. When a competitor wins an AI recommendation because they are cheaper on Amazon, or because they are running a promotional discount, the agent surfaces that signal alongside specific suggested actions: adjust your price, list on the missing channel, run a counter-promotion. Content optimisation alone cannot close a pricing gap. The 4P framework ensures the agent identifies the actual reason for every gap, not just the most convenient one to fix.

Yes. Perplexity surfaces shopping results on the majority of consumer purchase queries and includes native buy buttons. ChatGPT Shopping surfaces product cards with purchase links directly in conversation. Google AI Mode connects to Google Shopping. Amazon Rufus influences purchase decisions for hundreds of millions of Amazon shoppers. Agentic AI — where the AI assistant completes a purchase autonomously on the consumer's behalf — is already in early release and will accelerate. The AI purchase channel is not arriving. It is here.

Submit your brand's URL. The agent scrapes your site, identifies your hero products and target personas, then simulates their full AI shopping journeys across ChatGPT, Google AI Overviews + Gemini. You see: which products were recommended and in what position, which competitors appeared instead of you, what the AI said about each brand during the simulated journey, your purchase likelihood scores per product, and 2–3 specific immediate actions to improve your position in the AI Purchase Channel. No account required. Results in 30–60 seconds.

The agent learns your brand voice from your existing site content and from every piece of content you approve (or reject) over time. Approvals and rejections are stored in brand memory and directly inform future content creation. Before anything is published you read it. Edit it freely, reject it entirely, or approve it as-is. Over time, the agent's outputs become more aligned with your voice — exactly as a skilled human writer would improve with your editorial feedback.

No — and this matters. Purchase intent ('best protein powder under £40') is just one moment in the journey. A consumer's AI shopping session typically starts earlier: 'I want to start strength training, what supplements do I need?' Then category research: 'what should I look for in a protein powder?' Then comparison: 'Optimum Nutrition vs. Myprotein'. Then purchase intent. AI assistants maintain context across a session. A recommendation made during the research phase often determines the purchase outcome several queries later. Slingso simulates full journeys from first awareness through comparison and purchase intent. Winning at purchase intent but losing at research means losing the channel.