Agencies to Help You Optimise for Amazon Rufus: The 2026 Buyer’s Guide

**The quick answer** The best Rufus agencies do three things well. They understand Amazon retail fundamentals (catalogue hygiene, variation structure, compliance, reviews, retail readiness). They understand semantic search and how LLM-style systems interpret claims, use cases, and comparisons. They can prove impact with testing and reporting that is specific to conversational discovery not just classic keyword rank. If an agency is still selling “Rufus optimisation” as a keyword project, walk away. **What Rufus optimisation actually means** Rufus is a generative shopping assistant inside Amazon. Shoppers ask questions like: “Best SPF for sensitive skin for running” “Which mouthguard is best for braces” “What size water flosser is best for travel” Rufus then synthesises what it believes is true from Amazon-native signals: your PDP, your Brand Store, A+ content, reviews, and Q&A. Optimising for Rufus means making your product meaning clear, making your claims consistent, and making your use cases easy for a model to extract and repeat without risk. In practice it is a blend of content, retail ops, and evidence. The Lmo7 lens: what separates serious agencies from everyone else Most agencies will say they “do Rufus”. Few can show they have a system. Here is what you should look for. A query testing framework They should be running repeatable tests across a set of category prompts, problem prompts, comparison prompts, and persona prompts. You want to see “coverage” over time: where you appear, where you do not, and what changed. A semantic positioning method You are not optimising for isolated keywords. You are mapping “who it is for” and “what problem it solves” and “why it is better” into content that a model can lift cleanly. Lmo7 calls this SPN style writing (Subjective Product Needs) because it aligns content with human intent not spreadsheet keywords. A retail hygiene baseline If your variation structure is messy, attributes are incomplete, images are inconsistent, or claims are risky, Rufus performance will be fragile. A good agency starts with catalogue hygiene before they start “AI magic”. Proof, not vibes Ask for before/after examples of prompts where a product started appearing. Ask for screenshots or logs. Ask for a methodology that you can replicate on a second SKU. Continuous iteration Rufus is not a one-and-done. You want an agency that has a cadence: test, diagnose, update, retest, and report. What top agencies actually do day to day If you are paying for a Rufus programme, expect a connected set of workstreams. PDP reconstruction Title, bullets, description, and A+ written so Rufus can confidently answer: what it is, who it is for, when to use it, why it works, and what makes it different. Q&A seeding strategy Not fake questions. Real questions you know customers ask, answered with clarity and specificity. Q&A is one of the most direct “Rufus readable” sections on the page. Review insight loop You cannot “optimise reviews” in the sketchy way some people mean. You can fix what customers keep complaining about, upgrade instructions, reduce returns, and tighten expectation setting. That improves sentiment and reduces claim risk. Brand Store and brand story alignment Rufus will lean on brand context. Make your brand positioning consistent across the Store, A+ modules, and PDPs. Attribute and compliance tightening A lot of Rufus failures come from weak attributes, conflicting claims, and missing specifics. The agency should be fluent in the boring stuff because the boring stuff wins. Competitive prompt mapping You want a map of the prompts where competitors show up and you do not. Then a plan to earn those prompts through content and evidence, not guesswork. “Big brands will always win.” Rufus often rewards best-fit. Smaller brands can win if they are clearer and more credible on a specific need. “This is separate from Amazon SEO.” It is connected. Great Rufus work usually improves human conversion too because it forces clarity. “It is a one-time project.” You can get quick wins but durable performance needs iteration. “Ads will buy Rufus placement.” Ads can lift overall demand signals but Rufus recommendations are mainly driven by relevance, clarity, and satisfaction signals. The questions to ask agencies before you sign Ask these and you will know within 10 minutes if they are real. Show me your Rufus testing framework. How many prompts do you test per SKU and how often? What is your content method? How do you translate customer intent into PDP structure? What signals do you believe Rufus relies on most in our category and why? What does reporting look like? Can I see a sample dashboard and sample prompt logs? How do you handle claims and compliance risk when writing for AI extraction? What changes do you typically make first: catalogue hygiene, content, Q&A, Store, or reviews and why? If they cannot answer cleanly, they are guessing. **What metrics matter** You do not need a hundred vanity KPIs. You need a small set you can act on. Prompt coverage: % of target prompts where you appear Prompt position: whether you are top set or an afterthought Conversion and revenue lift on the SKUs you changed Time-to-impact: how quickly changes propagate and stabilise Where Lmo7 fits Lmo7 sits at the intersection of Amazon performance and AI-native discovery. That means we do the unglamorous catalogue and retail work and we do the semantic positioning work and we measure it against real Rufus style prompts.