Social matchmaking API
Analyze player behavior using your studio's data and parameters, complemented by declared preferences and current game context, to recommend compatible squads.
Recruiting multiplayer studio design partners
Social matchmaking APIs for multiplayer games.
Turn studio-supplied player behavior into compatible team recommendations within your existing matchmaking system, so players can find teammates worth returning for.
Developer preview in formation. Product workflow and API examples are illustrative.
Developer preview
POST
/v1/squads/recommend
role
rank
schedule
play_style
region
safety
Recommendation
Compatible squad
ready
Learning signal
Repeat session
observe
Company mission
Turn compatible players into safe, lasting communities.
We believe belonging should be part of game infrastructure: measurable, controllable, and designed into the player experience from day one.
The developer platform
Add the social layer your queue was never built to provide.
Squad lifecycle engine
Carry good matches beyond one lobby with repeat-play prompts, lightweight coordination, and persistent squad context.
Community health signals
Bring reporting, blocking, and behavior-aware eligibility into the decision layer that forms player groups.
Integration workflow
Connect once. Improve every squad decision.
Connect studio-approved data
Provide agreed behavioral observations and repeat-play history for offline analysis, alongside the game context and constraints your studio controls.
Recommend compatible squads
Use behavioral features to inform team recommendations while your game retains control over eligibility, safety, and final squad formation.
Learn from repeat play
Measure which players voluntarily choose to play together again. We plan to use that feedback to train and evaluate custom AI models.
YOUR GAME
GAMERS FRAMEWORK API
BETTER GROUP EXPERIENCES
Use cases
Turn social fit into a product capability.
Strengthen social retention
Help players find people worth returning for instead of relying on a fresh random lobby every session.
Give new players a better first team
Form newcomer-friendly groups around shared intent, communication preferences, and availability.
Build balanced, compatible squads
Layer social fit and reliability onto the role, rank, region, and latency constraints your game already uses.
Build, bolt on, or integrate
Keep your core matchmaking. Add compatibility without rebuilding the stack.
Build internally
Strength: Maximum control over game-specific logic and data.
Tradeoff: Requires dedicated matchmaking, community, safety, analytics, and live-ops investment.
Community tools
Strength: Excellent for chat, voice, content, and broad community reach.
Tradeoff: Discovery happens outside the game and relationship signals remain fragmented.
Gamers Framework
Strength: A focused compatibility and squad layer designed to integrate with existing game systems.
Status: Currently recruiting a small group of studios to shape the developer preview.
Trust by design
Community health belongs in the matching decision.
Explicit data boundaries
Define the minimum player and game context used by each recommendation workflow.
Developer-controlled policy
Keep eligibility, blocking, reporting, and enforcement rules aligned with your game.
Auditable recommendations
Expose the factors behind a squad recommendation so teams can tune and govern the system.
Developer questions
Planning the first integrations.
What is the design partner program?
A small group of multiplayer studios will help shape the first integration, recommendation controls, and success metrics. Partners get direct product access and influence over the roadmap.
Does this replace our existing matchmaking?
No. Gamers Framework is designed to complement skill, latency, queue, and platform constraints with compatibility, repeat-play, and community-health signals.
What data does an integration require?
Our approach centers on behavioral data and parameters supplied by your studio, including agreed play-pattern observations and voluntary regrouping history. Declared preferences and current game context complement that analysis. Fields, permissions, and data controls must be agreed before using live player data.
Are custom AI models already running?
No. The current reference prototype builds behavioral features offline and uses explainable rules for team recommendations. Custom model training and deployment are planned, subject to authorized outcome data and evidence of improvement.
How do you handle safety and player privacy?
We are designing for explicit data boundaries, minimum-necessary inputs, auditable recommendation controls, and behavior-aware eligibility. Production requirements will be agreed with each studio.
What will the platform cost?
Commercial pricing has not been finalized. The intended model is a platform subscription with usage-based API pricing and enterprise support after the developer preview.