Content creation at scale has always been messy. When I was creating content for multiple platforms — blog posts, social media updates, technical articles — I found myself wasting a huge amount of time switching between tools. I’d draft content in one place, copy it over to another platform, reformat it, and try to keep everything consistent across different channels.
It was fragmented and inefficient. I knew there had to be a better way.
That’s when I thought:
What if I could have everything in one place?
Not just a writing tool — a system that could generate AI-driven content, enable real-time team collaboration, manage versioning, and distribute content across platforms — all while maintaining consistency and state. That’s how SynaplyAI was born.
The Problem: Fragmented Content Creation at Scale
Content creation at the enterprise level isn’t just about writing — it’s about consistency and efficiency. The problem isn’t just the time it takes to create content — it’s the fragmentation of the process:
- Writing content → Platform A
- Repurposing content → Platform B
- Scheduling and tracking → Platform C
This creates two key issues:
- Messaging consistency – Each piece of content ends up being slightly different due to manual reformatting.
- Scalability – This approach doesn't scale for enterprise-level content creation with multiple stakeholders involved.
I realized that to solve this problem, I needed a platform that could:
- Centralize content creation, distribution, and tracking.
- Allow multiple users to work on the same content simultaneously without conflicts.
- Handle complex AI generation and token-based state tracking across a multi-tenant architecture.
The Technical Foundation: Multi-Tenant Architecture
SynaplyAI isn’t just another content creation tool — it’s built on a robust multi-tenant architecture designed for enterprise scalability and security.
1. Tenant Isolation
We implemented full tenant isolation using:
- AsyncLocalStorage for context propagation.
- Prisma middleware to enforce tenant-specific data boundaries at the database level.
- Redis for tenant-based rate limiting and usage tracking.
This means that every tenant operates in a fully isolated context, preventing data leakage and ensuring high performance even under heavy load.
2. Real-Time Conflict Resolution with Token-Level State Handling
Real-time collaboration introduces a major technical challenge: conflicting edits. If multiple users are editing the same document simultaneously, how do you maintain state consistency without creating versioning conflicts?
We solved this by implementing:
- Vector clock synchronization – Tracks causal relationships between edits, ensuring that order is preserved even when conflicts occur.
- Operational Transform (OT) – Resolves conflicts at the token level, allowing multiple users to edit simultaneously while maintaining logical order.
- Token-Based State Handling – Each token in the document is assigned a unique state, allowing the system to merge changes without data loss.
Example:
// Conflict resolution using vector clocks
if (localVersion < remoteVersion) {
// Apply remote changes first
merge(remoteChange);
} else {
// Keep local changes and queue remote changes
queue(remoteChange);
}This approach ensures that conflicts are detected and resolved in less than 5ms — even with multiple users editing simultaneously.
3. Event Sourcing and Snapshotting
To support real-time document editing and rollback, we built an event sourcing system with adaptive snapshotting.
- Event Store – Stores every document change as an immutable event.
- Snapshot Manager – Creates snapshots at dynamic intervals based on document size and activity.
- Command Handler – Executes state changes as a series of commands, allowing for consistent rollback and replay.
Example:
async executeCommand(command) {
return this.transactionManager.executeInTransaction(async (transaction) => {
const events = await this.commandHandler.handle(command);
for (const event of events) {
await this.eventStore.store(event, transaction);
}
return { success: true, events };
});
}This allows us to reconstruct the document state at any point in time with minimal performance overhead.
4. Usage Tracking and Token Management
AI processing is expensive, so efficient token management was essential.
We implemented:
- Redis-based token tracking – Tracks AI usage at the token level in real time.
- Tenant-specific token limits – Each subscription tier includes different token limits and overage fees.
- Dynamic Token Budgeting – Adjusts AI model allocation based on user behavior and load.
Example:
const dayKey = `usage:${tenantId}:${formatDate(new Date())}`;
pipeline.incrby(`${dayKey}:request`, requestTokens);
pipeline.incrby(`${dayKey}:response`, responseTokens);
await pipeline.exec();This ensures that token consumption is predictable and scalable without risking overuse or unexpected costs.
Challenges and Breakthroughs
The biggest technical challenge was conflict resolution. Real-time state management at scale with multiple users editing concurrently introduces edge cases that are hard to predict.
Some of the hardest problems we faced:
- Ordering conflicts – Multiple users editing the same token simultaneously.
- Sync delays – Network latency causing inconsistent state updates.
- Snapshot collisions – Conflicts between snapshot states and real-time edits.
The breakthrough came when we combined vector clocks with a state feedback loop. By treating conflicts as discrete state updates and allowing partial acceptance, we created a flexible conflict resolution system that operates with minimal latency.
Performance and Scalability
SynaplyAI can handle:
- 500+ concurrent users editing the same document without lag.
- Conflict resolution in <5ms using token-based state handling.
- AI-generated content at scale with processing time under 500ms.
- Multi-tenant usage tracking with Redis-based token budgeting.
- Adaptive snapshotting reduces state recovery time to <100ms.
What’s Next for SynaplyAI?
Right now, we’re fine-tuning the platform based on beta user feedback.
Next steps:
- Expand real-time editing capabilities with enhanced AI-assisted suggestions.
- Roll out additional enterprise-level features, including custom AI model integrations.
- Optimize AI processing costs by dynamically adjusting token handling.
The long-term vision is to make SynaplyAI the go-to AI platform for content creation — scalable, secure, and adaptable to the needs of enterprise clients and independent creators alike.
Join the Beta
If you want to experience real-time AI-driven content creation and conflict-free collaboration, sign up for the beta today.
