We engineer software platforms that solve complex problems across legal technology, digital media, and education. Founded in 2021, we've built systems serving 10M+ users with 99.9% uptime. Our first product, lawla—an AI legal assistant—launches January 2026.
End-to-end engineering of production systems. We own the full stack—from infrastructure and backend services to mobile apps and AI models. Every line of code, every architectural decision, built in-house.
Production-ready AI system that translates complex legislation into plain language. lawla combines fine-tuned LLMs, knowledge graphs, and retrieval-augmented generation to deliver accurate legal information with source citations.
Technical Stack: Custom fine-tuned Llama 3 70B model, vector databases for semantic search, automated knowledge graph updates from legislative sources, fact-verification pipeline achieving 96.3% accuracy in beta testing. Built for scale from day one with sub-3-second response times.
High-traffic news platform serving 10M+ monthly users across Nigeria and West Africa. Real-time content delivery system with intelligent caching, CDN optimization for low-bandwidth environments, and ML-powered content recommendations.
Engineering Highlights: Kubernetes-based microservices architecture, automated scaling handling 500K+ concurrent users, sub-100ms API response times, PostgreSQL + Redis data layer, React/Next.js frontend optimized for 3G networks.
Multi-tenant learning management system for vocational training institutions. Adaptive learning algorithms personalize content delivery, comprehensive analytics dashboard tracks student progress, automated certification workflows.
Technical Architecture: Secure multi-tenant data isolation, role-based access control, real-time progress tracking with WebSocket updates, mobile-first responsive design, offline-capable PWA architecture for low-connectivity scenarios.
Production-first mindset. We build for reliability, scalability, and maintainability from day one. Modern stack, rigorous testing, comprehensive monitoring.
Production ML systems including fine-tuned LLMs (Llama 3), custom NLP pipelines, RAG architectures, vector databases (Pinecone/Weaviate), and real-time inference optimization.
Cross-platform mobile apps with React Native, native modules for performance-critical features, offline-first architecture, optimized for emerging markets with limited connectivity.
AWS-based infrastructure with Kubernetes orchestration, automated CI/CD pipelines, infrastructure-as-code (Terraform), comprehensive monitoring (Prometheus/Grafana), and incident response automation.
High-performance microservices in Node.js/Python, GraphQL and REST APIs, event-driven architectures, message queues (RabbitMQ/Kafka), comprehensive API testing and documentation.
Modern web apps with React/Next.js, TypeScript for type safety, SSR/SSG for performance, sophisticated state management, automated testing with Jest/Cypress, accessibility compliance.
PostgreSQL primary databases with replication, Redis caching layers, data pipelines for analytics, real-time metrics tracking, automated backups and disaster recovery procedures.
Deep dives into our technical architecture, infrastructure decisions, and lessons learned building production systems at scale
Complete technical breakdown of building an AI legal assistant from scratch: LLM fine-tuning methodology, RAG implementation, knowledge graph architecture, fact-verification pipeline, and achieving 96.3% accuracy in production.
Read Technical Paper →How we architected Naija News to handle massive scale: Kubernetes autoscaling strategies, CDN optimization for emerging markets, database sharding, caching layers, and maintaining 99.9% uptime under traffic spikes.
Read Engineering Post →Technical architecture for secure multi-tenant learning platforms: data isolation strategies, row-level security implementation, tenant-aware caching, and scaling multi-tenancy to hundreds of concurrent organizations.
Read Architecture Guide →Interested in early access to lawla? Building something similar and want to discuss our technical approach? We're open to partnerships, technical collaborations, and conversations with fellow engineers solving hard problems.
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