Amazon
Rufus AI
Seamless AI-Powered Search
Meet the Team
Lopeka Attreja
Business Administration
Dallas, Texas
Grayson Mann
Mechanical Engineer
Fort Worth, Texas
Darshil Rayjada
M.S. MIS
College Station, Texas
Angelo Mazzilli
Electrical Engineer
Houston, Texas
What This Deck Covers
01
The Problem
Why Rufus needs to change and why now
02
The Solution
AI-Guided Search, Personalisation & Review Intelligence
03
What we're building and what we're not
04
User personas and the targeted consumer base
05
Launch Roadmap
Four phases: Design → Beta → Limited → Full
06
Success Metrics
The three KPIs we're aligned around
Scope Clarity
Targeted User
01 The Problem
1000s
of results per
search query
Users feel overwhelmed
before they even decide
Discovery Gap
Most customers don't know Rufus exists. There's no visible prompt or on-ramp in the standard search flow.
Interface Friction
Customers who do find Rufus must leave the search flow entirely a context switch that kills adoption.
Product Quality
Existing Rufus responses lack accuracy and relevance, eroding trust and discouraging repeat usage.
Evidence: Customer survey (Google Forms) consistently flags decision fatigue and product discoverability as top pain points.
02 Targeted Users
Who we are building for grounded in real user research.
Business Professional
Age 25–40 · 50+ hrs/week · Purchasing, Supply Chain, Operations
“I just need the right product fast — I don’t have time to scroll through hundreds of listings.”
Goals
Efficient with time · Right quality at the right price · Tailored results for their role
Frustrations
Too many product variations → overwhelmed · Products not meeting expectations · Delivery reliability concerns
Tactics
Cross-reference third-party sites · Bulk ordering with fewer steps · Minimise cost-per-unit for the business
Rufus features: AI-Guided Search · Personalisation Engine · Bulk-friendly filtering
College Student
Age 18–22 · None or part-time employment · Undergrad or Grad
“I want the best deal I can find, delivered fast — with as little effort as possible.”
Goals
Items within budget · Fast delivery · Easy reordering of past purchases · Deals & comparisons
Frustrations
Hard to find budget-friendly quality · Trend-driven products (TikTok Shop effect) · Inflexible return policies
Tactics
Third-party sites to compare prices · Looking for discounted Prime / free shipping · Filters for college essentials
Rufus features: Budget-Aware Recommendations · Review Intelligence · Smart Reorder
03 The Solution
Embed Rufus directly in the search experience from the first query to the final pick.
User
enters query
▶
Rufus asks
2–3 questions
▶
AI filters
& curates
▶
5–10 product
shortlist
▶
Fast, confident
purchase
AI-Guided Search
Clarifying questions narrow results to a curated shortlist of 5–10 relevant products no duplicates, no sponsored ranking.
Personalisation Engine
Purchase history and an onboarding questionnaire pre-populate preferences. Rufus learns and improves with every session.
Review Intelligence
Rufus synthesises user reviews into unbiased, use-case-specific summaries never influenced by sponsorship.
04 Scope Clarity
What we're building and what we're protecting from unintended scope creep.
✅ IN SCOPE
✓ Search UI redesign Rufus as a first-class feature
✓ AI-Guided Search with clarifying questions + curated shortlist
✓ Personalisation via purchase history & onboarding quiz
✓ Review synthesis engine (sponsorship-free)
✓ Multilingual support: English, German, Japanese
✓ Budget-aware recommendations for cost-sensitive users
🚫 OUT OF SCOPE
✗ Review system architecture or scoring logic
✗ Rufus on Amazon Seller Central or third-party apps
✗ Product catalogue or fulfilment infrastructure changes
✗ Alexa / voice-activated Rufus (future consideration)
✗ Cross-category preference learning (future consideration)
05 Launch Roadmap
Apr 15
Design
UI built & tested
internally
Exit Criteria
80% employee
approval
✅ Complete
Apr 30
Beta
50 customers get
early access
Exit Criteria
>25 confirm
improved UX
🔲 Upcoming
May 31
Limited Release
Live on site;
feedback loop open
Exit Criteria
24hr
bug-free window
🔲 Upcoming
Jun 30
Full Launch
All markets:
US, DE, JP
Exit Criteria
KPIs live;
no P0 issues
🔲 Target
06 Success Metrics
We're aligned when all three of these move in the right direction.
↑ Adoption
Rufus Active Users
Increase the percentage of customers who use Rufus at least once per shopping session. Baseline to be established at Beta.
Product + Analytics
< 5 min
Time-to-Purchase
Reduce average time from first search query to order confirmation to under 5 minutes per item for Rufus-assisted sessions.
Engineering + Design
↓ Drop-off
Decision Fatigue
Reduce session abandonment on search results pages. A curated shortlist of 5–10 replaces 1,000s measured against control group.
Marketing + Analytics
Risks & Mitigations
Risk | Severity | Mitigation | Owner |
AI output quality | High | Dedicated QA process in Beta. On-call engineers throughout rollout. Gate at each phase. | Engineering |
Legal / regulatory compliance | High | AI responses audited against national standards before each market launch (US, DE, JP). | Legal + Eng |
Partner integration delays | Med | OpenAI and Apple co-ordination must start 6 weeks before Limited Release. | PM + Partners |
Low adoption at launch | Med | Marketing campaign (Facebook + social) timed to Limited Release. In-app tooltips surfaced on first session. | Marketing |
Localisation errors (DE/JP) | Med | Native-speaker review of all Rufus outputs before each market goes live. | Globalization |
Next Steps
1
All Teams
Confirm scope and responsibilities in this deck. Flag any conflicts to PM by April 24.
2
Engineering
Complete AI-Guided Search MVP build. Deploy to staging environment by April 28.
3
Design
Finalise search UI integration and onboarding questionnaire screens for Beta cohort.
4
Marketing
Prepare Limited Release announcement assets and in-app tooltip copy for review.
5
PM
Recruit Beta cohort of 50 customers. Schedule Beta debrief for May 5.