AI Chat interface

Chat interface where AI agent is responding to the user request to create a game build. User is creating guardrails and constraints. Agent has been trained with the database, use cases, tailored response for service specific capabilities. Agent automates the workflows, create resources ready and waits for human to provide action commands, promots to complete a job. Agent retains and has the context of the information provided from previous sessions to provide a personalized experience.

AI optimization

Option B

The optimization opportunities are listed for each session. It shows root cause, signals from data, comparison of recommended instance along with cost. The AI would access the RAG files that has the ruleset, algorithm to analyze on why a RAM and vCPU combination will work best for a given game session. The algorithm has many variables to consider, such as session packing on an instance, game rendering, number of concurrent players, latency, CPU and memory consumption and many more. The user has authority to approve, modify or defer it. The modification data keeps improving the AI agent over time.

Conversation AI Interface

Conversational AI that lets users to provide context, guardrails for their content to be launched. The AI would provision compute resources with smart packages of instances, vCPUs and RAM with Autoscaling features, cost optimization. This feature saved game developers approximately 85% of development work each week.

AI Compute Framework

From session to scale, intelligently

Five pillars that eliminate cloud complexity, surface optimization signals, and put developers back in control — with humans in the loop at every cost-critical decision.

Framework overview

Smart defaults

Sandbox in seconds

Optimize

Learn every session

Scale

Proactive, not reactive

Anomaly detection

Threats surface instantly

Human in loop

You decide what matters

Five pillars

Smart defaults

From zero to sandbox — without the decisions

Developers are great at building games, not configuring cloud infrastructure. The agent removes every infrastructure decision — server type, region, instance sizing. Provide a game name and genre, get a live environment.

Auto server selection

Region, instance type, and OS chosen by workload profile — not the developer.

Pre-wired environments

Networking, storage, and security groups configured out of the box.

One-click sandbox

Input: game genre + expected CCU. Output: running environment with sensible baselines.

Override when ready

Defaults are a starting point. Every parameter stays editable.

Optimize

Every session makes the next one better

Each cloud run generates telemetry — latency, resource utilisation, player counts, tick rates. The framework turns that into actionable recommendations so each deployment is measurably more efficient than the last.

Memory utilisation

78% +12%

CPU efficiency

63% +8%

Network cost

41% −19%

Idle instance time

14% −31%

Scale

Proactive scaling, driven by player-server data

Reactive approaches consistently fail at launch. Player-server data that sat in a dashboard becomes an intelligent scaling signal — the framework proposes action before the bottleneck forms, with pre-launch forecasting and spike prediction built in.

Player telemetry

CCU, session length, matchmaking queue

Server metrics

Tick rate, latency, packet loss

Scaling package

Action + cost delta + confidence

Human approval

Reviewed, modified, or deferred

Anomaly detection

The AI sees what the dashboard can't

Long event lists require a trained eye — and time. The anomaly layer correlates runtime signals with security posture, resource performance, and live game state simultaneously, surfacing what matters before it becomes a player-facing incident.

Critical

API key exposure detected in environment variable — eu-west-1 instance group

0 min ago · Security · instance-grp-445

Warning

Non-optimized pathfinding asset consuming 14% excess CPU across 3 game servers

4 min ago · Performance · game-srv-07, 08, 11

Info

2 idle compute instances not serving sessions in 47 min — review for termination

47 min ago · Cost · compute-idle-grp-3

Human in loop

AI proposes. You decide.

Every action with a cost implication requires human approval. The agent surfaces the decision, the rationale, and the cost delta — then waits. Scale packages and budget overrides are never applied automatically.

Scaling proposal — Game launch: Ironfield Awaiting approval

+18

Instances

us-east-1

$1,240

Cost / day

+est. 48h

94%

Confidence

high signal