Cloudwalker Solutions

Every player sees
their game.

Cloudwalker Personalizer is an AI-powered casino recommendation engine that delivers hyper-personalised game suggestions — increasing engagement, session length, and revenue for iGaming operators.

Personalizer — live session Active
Just for you — Player #A7429
Top pick
Book of Ra
Match score 97%
Recommended
Sweet Bonanza
Match score 91%
Trending for you
Gates of Olympus
Match score 88%
New arrival
Big Bass Bonanza
Match score 84%

The problem

A lobby of thousands.
Every player sees the same thing.

The average online casino hosts 2,000+ games. The average player finds a handful they like — usually by accident. Meanwhile, operators invest in content and promotions that never reach the right audience.

It's not a content problem. It's a relevance problem. Cloudwalker Personalizer fixes that by building a unique, ranked game catalogue for every player — refreshed every day from their actual behaviour.

Generic lobby With Personalizer
Same games shown to every player Ranked lobby unique to each individual
New titles buried under popular games New launches reach players most likely to love them
Bonus campaigns sent to all segments equally Promotions matched to player preference profile
Players churn after hitting their regular games Discovery drives longer sessions and return visits
Cold-start players see irrelevant content immediately First session already personalised from day one

Recommendation engine

Four ways to put the right game in front of the right player

01
Personal Game Ranking

The engine reads every click, session, and stake across a player's full history and produces a ranked game list that belongs to that person alone. Not a segment. Not a demographic. One player, one ranking — refreshed daily as their behaviour evolves.

02
Game-to-Game Discovery

A player finishes a session on Book of Ra. Instead of sending them back to the generic lobby, the engine surfaces the three games most like the one they just played — same volatility, same feel, same rhythm. The moment they're most likely to keep playing is exactly when you show them what to play next.

03
CRM Precision Targeting

Preference clusters replace broad demographic segments. Instead of sending the same free-spin offer to 50,000 players, your CRM team can reach the 4,200 who actually play high-volatility slots on weekends — with the exact game their data says they'll open.

04
Cold-Start — Day One Personalisation

A brand-new player has no history, but they're not a blank slate. Cold-start models infer preference signals from registration data and the first few interactions — so the lobby they see on their very first visit is already working to keep them there.

iGaming coverage

Casino
Every slot, table, and jackpot ranked by that player's affinity — not global popularity
Live Casino
Surfaces the live tables that match each player's pace, stake level, and preferred game type
Virtual Casino
RNG and virtual sports content matched to frequency, session length, and historical stake patterns
Sportsbook Cross-sell
Identifies sports bettors whose profile predicts casino affinity — and shows them exactly the right first game

Hyper-personalization engine

The model trains overnight.
By morning, every player has a new list.

Amazon Personalize is the recommendation core — a fully managed ML service that ingests fresh interaction data each night, retrains, and produces a new ranked game list for every active player. No model tuning, no infrastructure ops, no drift.

Collaborative filtering at scale
Learns from the collective behaviour of all players to surface games that similar users loved — even before a player has tried them.
Daily model retraining
New game launches and changing player behaviour are reflected within 24 hours — recommendations never go stale.
"Just for you" homepage
The player's first screen is a ranked, personalised lobby — not a generic banner carousel. Highest-affinity games appear first, every time.
Promotion matching
Daily recommendations are cross-referenced with active casino promotions — so each player gets the offer most relevant to their current game preferences.
Daily recommendation flow
1
ML trains on player interactions
Amazon Personalize ingests latest session, game, and click data from S3 via AWS Glue
2
Per-user recommendations generated
Ranked game list created for each active player and written to DynamoDB via Lambda
3
Matched with Casino Promotions
Step Functions orchestrate cross-referencing against active bonus and free-spin campaigns
4
Delivered via API
Client platform calls the CW API endpoint (or receives push); results stored and shown as personalised homepage
41%
Games received first-time player interactions through recommendations
76%
Active users engaged with recommended games for the first time
+15%
Longer average session duration after personalisation
+53%
Games played per user (3.4 → 5.2), measured in production

Integration

Three paths to personalisation.
Pick the one your team can ship fastest.

Option 01 — Zero frontend work
We push.
You receive.
Each morning, Cloudwalker pushes ranked game payloads to your platform's existing API endpoint. Your frontend reads from your own database — nothing changes on your side except the data that's in it.
Option 02 — On-demand control
You call.
We answer.
Your platform calls the Cloudwalker API at the moment it needs recommendations — on player login, lobby load, or game exit. Real-time responses, your call cadence, your caching strategy. Full engineering control.
Option 03 — Fastest time to live
Drop in a widget.
Done.
Cloudwalker-hosted widgets embed into your lobby with a single script tag. "Just for you", "Because you played…", "Players like you also love…" — live in days, not sprints. Cloudwalker manages the rendering, data, and updates.

Built on AWS

Amazon Personalize Amazon Bedrock Claude Sonnet AWS Lambda Amazon DynamoDB AWS Glue Amazon S3 Amazon Redshift AWS Step Functions Amazon Cognito AWS IAM Amazon CloudWatch Amazon SNS AWS CloudFormation

Your players are telling you what they want.
Are you listening?

We'll run a live demo using your own game catalogue and player data — so you see real recommendations, not a sandbox. From first conversation to live personalisation in under two weeks.