Enterprise Multi-Model AI Chat Platform with Credit-Based Billing
An organization needed a unified platform for teams to access multiple AI models (GPT, Claude, Gemini, Grok, Perplexity) with enterprise-grade security, usage tracking, and cost management.
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El Desafío
Teams were using multiple AI tools with no centralization or cost control:
- Each team member had separate subscriptions to different AI providers
- No unified conversation history or knowledge sharing across the organization
- No visibility into AI usage costs or per-user consumption
- Enterprise security and GDPR compliance requirements couldn't be met with consumer tools
- Comparing model outputs required switching between multiple interfaces
Nuestra Solución
We built a production-grade multi-model AI chat platform with credit-based billing, role-based access control, and GDPR compliance.
Architecture
- Frontend: React 18 + TypeScript + Vite with Tailwind CSS
- Backend: Node.js/Express with TypeScript and Prisma ORM
- Database: PostgreSQL (60+ tables) with Redis caching
- Auth: AWS Cognito with JWT-based RBAC
- Billing: LemonSqueezy with credit-based consumption tracking
- Queue: BullMQ for background job processing
- Infrastructure: AWS (ECS/Fargate, RDS, ElastiCache, S3, KMS, SES)
AI Integrations
- OpenAI GPT models
- Anthropic Claude models
- Google Gemini models
- xAI Grok models
- Perplexity for web search
- Suno for AI music generation
Key Features
- Multi-Model Chat - Switch between AI providers per conversation
- Split-Screen Comparison - Side-by-side model output comparison
- Workflow Automation - LangGraph-powered step-by-step AI workflows
- GPT Marketplace - Discover, create, and share custom GPTs
- Artifacts - Sandboxed code/HTML preview within conversations
- Credit System - Pay-per-use with automatic refills and admin grants
- GDPR Compliance - Automated deletion, data export, AES-256-GCM encryption
- Content Moderation - Flagging system with auto-triage for inappropriate content
- Group Chat - Multiple AI participants in a single conversation
- Web Search - Perplexity integration for grounded, up-to-date responses
Resultados
Stack Tecnológico
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Preguntas Frecuentes
MicrocosmWorks engineered an intelligent routing layer that evaluates incoming prompts based on task type, complexity, and token requirements, then dispatches them to the most appropriate model whether that is GPT-4, Claude, Llama, or a specialized fine-tuned model. This approach optimizes both response quality and cost, since simpler queries can be handled by faster, cheaper models while complex reasoning tasks go to more capable ones.
MicrocosmWorks implemented a unified credit system that abstracts away the varying per-token costs of different AI providers into a single internal currency that enterprise customers purchase in bulk. Each model interaction deducts credits proportional to its actual API cost plus a configurable margin, giving administrators a single dashboard to track usage, set department-level budgets, and generate chargeback reports.
Yes, MicrocosmWorks built a centralized governance layer that enforces consistent data handling policies regardless of which underlying LLM processes the query. All conversations are encrypted at rest, role-based access controls determine which teams can access which models, and configurable retention policies automatically purge conversation history according to your compliance requirements.
MicrocosmWorks optimized the routing layer to add under 50 milliseconds of overhead per request, which is negligible compared to typical LLM response times of 1-10 seconds. The platform uses connection pooling, pre-authenticated sessions with each provider, and async streaming so that tokens begin appearing in the user interface as soon as the selected model starts generating them.
MicrocosmWorks builds enterprise multi-model chat platforms at development rates of $30-$50/hr, which is a fraction of what large consultancies charge for similar AI infrastructure projects. The total scope depends on the number of model integrations, authentication and SSO requirements, and whether you need features like conversation branching, prompt libraries, or fine-tuning pipelines.
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