Data everywhere, decisions nowhere
You pay hundreds a month for tools and see endless charts and numbers. But not a single tool tells you: should you enter this category or not?
The First AI Decision Agent for Amazon Sellers
Satlis AI: The Growth Decision Engine for Amazon. Category, competitor, Search Terms, ads — every step delivers a decision.
Other tools give you charts. Satlis gives you a complete set of decisions you can act on — from judgment to execution.
Go / Wait / Skip category scoringBeatable competitors + outranking planDual-tested precision Search Terms8-layer Sponsored Products architectureAI visual assets
Every module outputs an actionable decision, not just reports and suggestions.
AI-driven results from real operations
Efficiency changes from real sellers using Satlis (ratio metrics only).
96.9%
AI-driven order share
8.9x
AI ROAS vs. manual ads
72.1%
Organic order share
The uncomfortable truth about Amazon seller tools
Search Terms coverage below 1/16
Brand Analytics gives you 42 terms. Your competitors use 700+. In the COSMO and Rufus era, traditional tools miss 94% of real buyer intent.
Ad spend keeps rising, organic orders stay flat
Manual PPC runs at 56.5% ACoS — budget burned on the wrong traffic entries. You cannot predict which entry will convert and which will just drain spend.
Why Amazon sellers are switching to AI Agents
Comparison
Traditional tools
Satlis AI Agent
Output format
Data & charts
Decisions & action plans
Search Terms coverage
Brand Analytics level (1x)
16.7x Brand Analytics coverage
Precision filtering
Manual review
AI dual test: 20K candidates → precision terms
Category analysis
BSR tables
15-dimension AI scoring + go/wait/skip decision
Competitor intelligence
Sales estimates
"Beatable" grading + outranking plan
PPC optimization
Manual bid tuning
Neural network prediction + bid engine
Campaign architecture
Flat campaign structure
8-layer structured architecture
Decision quality
"Here is the data, figure it out"
"Here is what to do, and why"
Decision-led, not data-led
"The real value of AI is not more data — it is fewer decisions you have to make yourself."
Satlis is not a data tool. It is a decision engine. Every module outputs a decision — go / wait / skip — backed by an evidence chain.
Traditional tools
- Hand you 15 metrics
- List 10,000 keywords
- Show competitor dashboards
- Export ad reports
Satlis AI Agent
- Tells you "Category A — recommended to enter"
- Filters down to precision actionable terms
- Tells you "This competitor is beatable — here is the plan"
- Predicts which entries will convert before you spend
Five Decision Engines: every module gives a judgment, not just charts
Decision first, evidence second, then execute immediately.
Should we enter this category or not?
Category ResearchSatlis decision
Outputs A-to-E category scoring with a go / wait / skip decision. 15-dimension AI scoring: market demand, competition density, new product success rate, review barrier, ad competition pressure, and more.
Traditional tool output
A pile of BSR data in a spreadsheet. Draw your own conclusions.
Which competitors can we actually beat?
Competitor ResearchSatlis decision
Outputs a beatable competitor pool with S/A/B grading. Flags weaknesses one by one: low ratings, no A+ Content, no video, weak brand presence, inflated pricing. Blue ocean — lock on the benchmark. Red ocean — pinpoint the entry gap.
Traditional tool output
A competitor list sorted by sales volume. No idea who is beatable.
How do we outrank this specific competitor?
Outranking PlanSatlis decision
Dual-perspective attack plan — Traffic side: CTR gaps, Frequently Bought Together interception, ad slot gaps, Search Terms blind spots. Operations side: review vulnerabilities, pricing windows, fulfillment weaknesses, content quality gaps. Outputs an actionable first move.
Traditional tool output
This feature does not exist.
Are these Search Terms worth the spend?
Keyword RefinerSatlis decision
From 20,000 candidate terms, dual testing filters down to precision actionable terms. Test 1: Indexing qualification — is Amazon indexing your ASIN for this term? Test 2: Conversion opportunity — does this term have real conversion potential for your ASIN? Precision is 16.7x that of Brand Analytics.
Traditional tool output
A keyword list sorted by search volume. No verification, no filtering, no precision.
What should we fix first in Sponsored Products?
Campaign Architecture EngineSatlis decision
8-layer campaign architecture — not a flat campaign. Neural network predicts which entry "will convert" vs. "will just burn budget." Bid engine outputs predicted optimal bids (not Amazon's inflated "suggested bid"). From 51,309 raw entries, filters to 3,273 super Search Terms contributing 72% of orders.
Traditional tool output
"Here is your ACoS. Good luck."
One complete loop from market entry to profit growth
Each step feeds into the next. Skip any link and the chain breaks.
01
Search Terms expansion
AI intent mining to expand the entry pool.
02
Indexing qualification
Verify whether Amazon indexes your ASIN for each term.
03
Opportunity filtering
Keep only the entries worth spending on.
04
Listing handoff
AI Listing ensures Amazon's algorithm recognizes and captures demand.
05
PPC amplification
8-layer structured Sponsored Products for efficiency.
06
Iteration loop
Cycle-based review, continuously tightening costs.
This is why sellers using fragmented tools keep running in circles — they are optimizing a broken chain.
The data speaks for itself
Same period, AI-driven vs. manual operations — efficiency comparison (ratio metrics only).
Metric
Manual operations
Satlis AI Agent
ACoS
56.5%
27.6%↓ 51%
ROAS
1.77x
3.63x↑ 105%
CVR
10.7%
15.4%↑ 44%
CPA
Baseline
↓ 43%↓ 43%
Organic order share
Low
72.1%70% of total orders
AI-driven order share
—
96.9%Nearly all AI-driven
Within 30 days, organic daily orders grew 6.4x while ad efficiency doubled — simultaneously.
Real sellers, real decisions, real growth
"Used dual-tested precision terms for ad launch. ROAS hit 5.94 in two weeks. Organic ranking started climbing in week two."
"Switched from manual PPC to the 8-layer campaign architecture. Organic order share reached 72.1%, ACoS dropped from 56% to 27%."
"Category decisions told us to skip 3 categories we were about to enter (all D/E rated). The A-rated category is now our best-performing product line."
Four workspaces — everything you need
Everything needed to run category, listing, ads, and creative in one loop.
Product Research Workspace
From market-entry judgment to competitor outranking strategy.
Learn more
Search Terms & Listing Workspace
From 20,000 candidates to precision terms to a launch-ready listing.
Learn more
Advertising Workspace
From ad chaos to systematic, high-efficiency Sponsored Products.
Learn more
Visual Lab Workspace
Listing-grade creative assets, produced in minutes.
Learn more
Choose by seller stage, not by feature pile-up
Buy decision accuracy first, then buy execution scale — matches the real growth path of Amazon sellers.
New Product Validation Stage
Goal: validate the category and Search Terms opportunity first, avoid discovering the wrong direction after heavy investment.
- Priority: Category decision + Competitor decision + dual Search Terms testing
- Budget approach: small steps first, validate then scale
- Suggested cadence: 1–2 operating cycles
Scale and Efficiency Stage
Goal: on a stable order base, grow organic order share while reducing ACoS.
- Priority: Campaign Architecture Engine + bid engine + budget engine
- Budget approach: concentrate on high-return layers, continuously cut low-efficiency layers
- Suggested cadence: 2–3 operating cycles
Multi-account Collaboration Stage
Goal: standardize decision playbooks across stores and teams, reduce efficiency variance.
- Priority: standardized decision templates + account collaboration
- Budget approach: layered allocation by account maturity
- Suggested cadence: continuous optimization
Frequently asked questions
Focused on what sellers care about most: profit, conversion, organic orders, and sustainable growth.
How is Satlis different from traditional tools?
Traditional tools output data and charts — you have to do the analysis and judgment yourself. Satlis outputs decisions: enter or skip a category, which competitor to target, which Search Terms to invest in, what to fix first in Sponsored Products. Every module delivers an actionable go / wait / skip call, not a pile of numbers to interpret.
What does "delivering decisions" mean — does AI make my decisions for me?
Satlis does not make decisions for you. It delivers judgment recommendations backed by an evidence chain. For example, category analysis outputs a blue ocean / red ocean rating with 15-dimension scoring evidence; competitor analysis tells you which competitors are beatable and exactly where their weaknesses are. The final call is still yours, but you no longer spend hours analyzing data to reach a conclusion.
How does the dual Search Terms test (indexing qualification + ranking opportunity) work?
The first test verifies whether Amazon indexes your ASIN under that Search Term (indexing qualification). The second test determines whether that term has real conversion opportunity for your ASIN (conversion judgment). Only terms that pass both tests enter the final precision library, preventing you from wasting ad budget on dead-end entries.
Is this system only for new products, or does it work for mature ASINs too?
Both. The new product launch path starts from category judgment, through competitor positioning and precision term filtering, to ad launch — helping you lock strategy before listing. The mature ASIN optimization path uses 8-layer campaign architecture and neural network prediction to restructure existing Sponsored Products, lowering ACoS while growing organic order share.
Which Amazon marketplaces does Satlis support?
Primarily the US marketplace, with more marketplaces launching continuously. Check the marketplace selector inside the product for current availability.
How do I get started?
Sign up and enter the system. Start with a category analysis or competitor analysis to get your first decision. We recommend running the full loop once, then iterating based on results.
Stop optimizing blindly. Start getting decisions.
Your competitors are still guessing. You already have the decision. Get your first category decision in 5 minutes.