Micro-design frontend, event-driven lambda backend, and a multi-modal data layer with graph-native AI-first schema design.
Independently deployable micro-frontends with shared design tokens, ensuring visual consistency and development velocity.
Each platform layer (Lighthouses, BPM, Hub) is an independently deployable micro-frontend with shared design tokens.
Shared UI primitives (GlassCard, SectionHeading, AnimatedCounter) ensure visual consistency across all modules.
Type-safe component architecture with Framer Motion animations, Tailwind CSS 4, and shadcn/ui primitives.
Mobile-first approach with fluid typography, adaptive layouts, and touch-optimized interactions.
Graph-native AI-first schema with knowledge graph as the primary model, supported by document, vector, and relational stores.
Neo4j-based knowledge graph as the primary data model. Entities (Agents, Signals, Processes) are nodes; relationships encode causal and temporal links.
MongoDB for semi-structured data — agent configurations, workflow templates, feedback payloads, and audit logs.
Pinecone/Weaviate for embedding-based retrieval — semantic search across signals, documents, and knowledge artifacts.
PostgreSQL for transactional data — user accounts, billing, SLA metrics, and compliance records with ACID guarantees.
Python and Rust microservices triggered by events, each handling a single domain concern with containerized deployment.
Python/Rust microservices triggered by events from message queues (Kafka/NATS). Each service handles a single domain concern.
Docker + Kubernetes orchestration with auto-scaling based on signal volume and processing load.
Kong/Envoy gateway with rate limiting, auth, and routing to microservices. GraphQL federation for cross-service queries.
Multi-region deployment on AWS/GCP with Terraform IaC. CDN for static assets, Redis for caching, S3 for object storage.
Embedded human supervision and rigorous controls directly into the AI lifecycle for multi-org, multi-industry environments.
Automated bias checks and ethical-by-design frameworks embedded in every data pipeline.
High-impact decisions require mandatory Human-in-the-Loop approvals with full audit trails.
Continuous monitoring of production models for performance degradation or behavioral shifts.
Every AI agent action is documented and traceable, ensuring GDPR and sector-specific compliance.
A comprehensive technology foundation designed for AI-first enterprise operations.
| Category | Technologies |
|---|---|
| Frontend | React 19, TypeScript, Tailwind CSS 4, Framer Motion, shadcn/ui, Vite |
| Backend | Python (FastAPI), Rust (Actix-web), Event-driven Lambda architecture |
| Data Layer | Neo4j (Graph), MongoDB (Document), PostgreSQL (Relational), Pinecone (Vector) |
| Message Queue | Apache Kafka, NATS, Redis Streams |
| Infrastructure | Docker, Kubernetes, Terraform, AWS/GCP multi-region |
| AI/ML | LLM orchestration, Multi-Agent Systems, LLM-as-Judge evaluation |
| Observability | OpenTelemetry, Prometheus, Grafana, ELK Stack |
| Security | OAuth 2.0, RBAC, mTLS, encryption at rest/transit, GDPR compliance |