Written by Chris King (CTO & Co-founder, GRID) and Guy Kroupp (CEO & Founder, GetGud)
Cheating and toxic behavior have become some of the most persistent threats to competitive and casual multiplayer games. Studios are under constant pressure to safeguard player experience, protect competitive integrity, and moderate community behavior. But deploying systems that actually work — at scale and with precision — often runs into the same obstacles: lacking detection pipelines, integration complexity and data gaps.
That’s where the partnership between Getgud.io and GRID changes everything.
GRID is a data platform specialized in in-game data — from sourcing to distributing and monetizing assets. GRID provides structured, high-fidelity in-game data for some of the world’s most played and watched games, including League of Legends or CS2. With standardized formats and a reliable data delivery infrastructure, studios that integrate with GRID gain control over their assets and access to granular in-game telemetry: movement, shots, kills, assists, player states, team events, and more.
Getgud.io is an observability platform, specialized in detecting cheaters, reducing toxicity, enhancing gameplay, driving retention, and helping esports teams optimize performance — all from a single, powerful platform.
GRID and Getgud.io partnered earlier this year to integrate their solutions. Now, any game developer that integrates the GRID as its in-game data infrastructure can send data to Getgud with no additional engineering effort from the studio.
If the game is already sending data to GRID, Getgud.io can immediately begin analyzing it to detect a wide spectrum of cheating, griefing, and toxicity. This eliminates the integration hurdle that has blocked many studios from deploying behavioral systems effectively.
Precision Detection, With Zero Friction
Once connected, Getgud.io analyzes each match using a layered approach — combining statistical analysis, pattern detection, AI models, and rule-based systems to flag suspicious player behavior.
GetGud’s solution can be universal for any game, genre, and platform. Structured data is massively helpful in unlocking its potential. GRID’s standardized data format and game agnostic technology enable us to provide the same experience and analysis quality to any game.
On the cheat detection front, we’re identifying behaviors that traditionally were attempted to be detected by expensive client-side solutions (that are hackable by design and lacking in detection) or manual review (in case the studio even has replays to view). Some of the cheating behaviors we are finding are:
- Aimbots — Detectable through non-human level accuracy, reaction time, player movements, weapon physics, and shot behavior over time.
- Wallhacks — We’re the first in the world to detect this with high confidence using player vision cones and map data to identify unnatural pre-aiming or tracking behavior.
- Speedhacks — Caught through movement pattern anomalies and velocity spikes.
On the griefing side, we’re identifying the kinds of repeat offenders that quietly ruin games:
- Intentional Friendly Fire — Repeated team damage across rounds or matches, particularly when there’s no gameplay justification.
- AFK behavior — Players idle for abnormal durations or consistently inactive during high engagement windows.
- Feeding — Players repeatedly dying with no effort to survive or contribute to objectives.
AI-Powered Chat Moderation That Understands Behavior Over Time
Player toxicity doesn’t stop at gameplay. It’s often most visible in chat, where frustration, insults, and targeted abuse can destroy the player experience. Getgud.io’s Chat Guard system brings modern AI to this challenge.
GRID rich in-game telemetry can be easily cross-compared with in-game chat logs, and you can send your chat log data to Getgud via the same, GRID integration.
Using a fine-tuned large language model (LLM), Getgud evaluates chat on a match-wide basis — not just isolated messages — and assigns every player a Toxicity Score based on their overall tone, escalation, and consistency.
This approach captures nuance. Not all toxicity is explicit or immediate. A player might start passive-aggressive, escalate over time, or show consistent patterns across matches — sometimes the actions speak louder than words, so it’s good to have access to data on both. GetGud scoring model reflects this, allowing studios to distinguish between occasional frustration and sustained abusive behavior.
The results are displayed directly in the Getgud modeler — with toxic chat segments or behaviour highlighted and timestamped, making it easy to understand what happened, when, and why. No manual review, no filtering — just instant, actionable insight.
Toxicity Scores can be connected to automated enforcement rules, moderation workflows, or internal reporting tools — all configurable through Getgud.io’s platform.
It works out of the box and supports all languages. Best of all, if your game is already integrated with GRID, there’s nothing more you need to do.
Why This Partnership Matters
GRID gives Getgud.io access to clean, standardized match data — live or post-game. Getgud.io transforms that data into insights — identifying the most damaging behaviors in your game. Together, the integration is fast, seamless, and powerful.
- No engineering lift required
- Compatible with any title sending data to GRID
- Scalable detection across cheats, griefing, and toxicity
- Automatically actionable — use Rules to automatically take any action on problematic players
If you want to deliver a fairer, more competitive experience — this is the fastest way to get there.
Meet Us at Gamescom
GRID + Getgud.io will be at Devcom and Gamescom 2025. If you’re building multiplayer games and care about access to in-game data, fair play, community health and engagement, and smart enforcement — let’s meet.
Drop us a message or stop by our Gamescom event (you can register here) — we’d love to talk!
Connect with the authors — Chris King (CTO & Co-founder, GRID) and Guy Kroupp (CEO & Founder, GetGud)
