Shakudo

Use Case

Detect and Mitigate Toxic Behavior in Online Gaming

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TABLE OF CONTENTS

Toxic behavior is the reason players leave. A single bad experience in chat can end a subscription before the game itself is ever tested, and the cost compounds: churn, moderation backlogs, brand risk, and for operators in the EU and UK, compliance expectations under the Digital Services Act and the Online Safety Act. A detection pipeline that runs inside the customer's environment addresses the problem where the player data lives.

The cost of toxicity in online games

Every toxic message is a moderation decision someone has to make, and the volume of in-game chat makes manual review impossible at scale. Unchecked, toxic behavior shapes the community: new players see it first, and the window after a player joins is exactly when toxic behavior compounds into churn. For a live-service game, that window is also a compliance window, since the EU and UK regulations place active moderation duties on operators of large platforms. The brand risk is the same in every case: a community that feels unsafe is a community that leaves, and the exit happens before a player ever judges the game.

What Shakudo delivers

Shakudo deploys the detection pipeline inside the customer's environment. Player data never leaves the environment, which matters for a business that treats conversation data as a regulated asset. The result is a real platform: detection runs at the scale of peak gaming hours, community-health dashboards make the trend visible to the whole team, and the platform keeps running after the engagement ends. The team owns the pipeline, the models, and the data.

How it works

  • Streaming ingestion. In-game text, voice transcripts, and behavioral signals stream in as they happen, so detection covers the conversation while it is still ongoing.
  • Models tuned to the community's language. The models are trained on gaming-specific language, including slang, coded insults, and multilingual chat, and scored in real time rather than run against a static keyword list.
  • Low-latency inference at peak. Inference runs in the data platform where the data already lives, so a flag arrives within the match, at peak-hour scale, without a round trip to an external service.
  • Flag, escalate, act. A toxic message triggers a flag that feeds the moderation workflow the team already runs, and player-level signals, such as report history and repeat-offender patterns, surface the accounts that need a human review.
  • Production monitoring. Model drift, throughput, and community-health trends are monitored continuously, so the pipeline stays reliable as the community's language evolves.

The same in-environment pipeline pattern applies to other high-volume decision problems, such as sales call transcript analysis. In both cases, the pipeline runs where the data lives, so the data never leaves the environment.

Who it is for

The pipeline fits the operators where player volume makes manual moderation impossible: a live-service publisher with in-game text or voice chat, a game studio shipping a community title, an esports or platform operator running player behavior monitoring as part of trust and safety, and any platform operator that needs moderation at the scale of peak gaming hours.

Frequently asked questions

How can AI detect toxic chat in online games?

Models trained on gaming-specific language score each message in real time, reading context rather than matching keywords. A coded insult or a multilingual message that a keyword list misses is still flagged, and the flag reaches the moderation workflow while the conversation is still ongoing, before the message shapes the match.

How do gaming companies choose a toxicity detection vendor?

The decision usually comes down to four questions: where does the player data live after ingestion, does the detection hold up at peak-hour throughput, are the models tuned to the community's actual language, and what happens when the vendor leaves, does the studio keep a working platform or lose access to its own moderation? An in-environment pipeline answers the first and last of those in the studio's favor.

How do you detect toxic players automatically?

Detection works at two levels. Message-level flags catch individual toxic messages in real time, while player-level signals, such as report history, repeat-offender patterns, and behavior across sessions, identify the accounts that need a human review before escalation, mute, or ban.

How do you solve toxicity in gaming?

Detection is one layer of the solution. Pair it with response playbooks so moderators act consistently, community-health dashboards so the trend is visible to the whole team, and design that rewards positive behavior, since punishment-only approaches leave the root cause untouched. The detection pipeline provides the signal that every one of those layers runs on.

When the goal is a moderation system that keeps player data in the studio's own environment, a conversation with Shakudo is the fastest way to see what the pipeline would flag on the game's own chat data.

AI-Powered Toxicity Management: Enhancing Player Experience and Retention in Gaming

Shakudo empowers gaming companies to swiftly implement and manage a cutting-edge toxicity detection system using best-in-class AI and data tools.

This solution directly addresses the critical challenge of maintaining a positive player experience, which is key to increasing user engagement and monetization.

  • Real-time toxicity detection and moderation using advanced natural language processing, reducing response time from hours to seconds
  • Customizable dashboards for monitoring community health metrics, enabling data-driven decisions to improve player satisfaction
  • Scalable architecture that seamlessly handles millions of daily player interactions, ensuring consistent performance during peak gaming hours

Shakudo Drives Innovation Across Industries

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Retail | largest food retailer in Canada

"Shakudo cut our AI tool deployment from 6-month procurement cycles to same-day delivery. Without that speed, we wouldn't meet production timelines."
Charu Pujari
Senior Vice President, AI & Engineering
@ Loblaw Digital
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real estate | $77.6 Billion AUM

"We chose Shakudo over alternatives because it gave us the flexibility to use the data stack components that fit our needs knowing that we can evolve the stack to keep up with the industry."
Neal Gilmore
Senior Vice President, Enterprise Data & Analytics
@ QuadReal Property Group
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Healthcare | #1 Software for Autism and IDD Care

"We use Shakudo to shorten development time and time to impact. The platform provides us with a value-added shortcut to get from Point A to Point Z much faster. It’s now weeks or months vs months and years."
Chris Sullens
CEO @ CentralReach
GALLO

Beverage | 70+ million cases shipped annually

"What drew me in is simple. When developers ship production-ready code this quickly, how can I have environments spun up fast enough? Shakudo is how we close that gap."

Robert Barrios
Chief Information Officer @ GALLO
FlexiVan

Logistics | 120,000+ intermodal chassis

"Shakudo does not just provide the platform. It is a real partnership. They are always there to help and execute our vision faster and the right way. It is like a co-team working together to achieve our goals."

Sagar Chikkala
Chief Information Officer @ FlexiVan
Whitecap Resources

Oil & Gas | 375,000 boe/d across Western Canada

"We started out with Shakudo about a year and a half ago as a way to build a foundational data layer for our analytics. … What started out as the foundational layer, which we needed, will turn into really an advanced AI tool for our business."
James Wakelin
Director of Business Intelligence @ Whitecap Resources