Technical Architecture — Kairoo v2.0
Comprehensive full-stack architecture for a scalable, AI-powered career development platform. Built for enterprise performance with Next.js, Express.js, MongoDB, Redis, and advanced multimodal AI integration.
Read this as a blueprint.
This page is the V2 target architecture. Sections marked Blueprint · vision describe systems and scale we are building toward — not all of it is shipped today. The figures below are aspirational targets for the orchestration platform.
Architecture Story
From signal ingestion to action, the system is orchestrated like a narrative arc.
Follow the scroll to see how each subsystem hands context, intent, and execution to the next layer.
Chapter 1
Signal Ingestion Mesh
Streams telemetry from resumes, job boards, HRIS, and user interactions through an event backbone with schema evolution and PII classifiers.
Chapter 2
Reasoning Fabric
Orchestrates Gemini, GPT-4.1, and in-house models through a policy-aware router that selects optimal context, temperature, and cost envelope per task.
Chapter 3
Action Layer
Automates follow-ups, learning plan updates, and executive dashboards with approval checkpoints and human-in-the-loop overrides.
Chapter 4
Continuous Intelligence Loop
Feedback signals retrain ranking models nightly while anomaly detectors surface drift and bias directly into observability boards.
Operating loop
Blueprint · visionSense → Think → Act flow
Every surface follows the same arc: capture signal, reason over it, then act with guardrails and learn from the result.
System decomposition
Blueprint · visionLayered flowchart
Four cooperating layers — Experience, Intelligence, Infrastructure, and Trust — each with explicit responsibilities.
Experience Layer
Intelligence Layer
Infrastructure Layer
Trust Layer
High level
System architecture overview
The request path: a Next.js frontend talks to an Nginx API gateway, which routes into the Python/FastAPI AI service and supporting microservices.
Interactive system architecture
Frontend Architecture
Next.js 15: App Router with Server Components
TypeScript: Full type safety and IntelliSense
Tailwind CSS: Utility-first styling with custom design system
Zustand: Lightweight state management
React Query: Server state management and caching
Backend Architecture
Express.js: RESTful APIs with middleware architecture
Prisma ORM: Type-safe database access and migrations
JWT + OAuth2: Secure authentication and authorization
Redis: Session management and caching layer
Bull Queue: Background job processing
Toolchain
Blueprint · visionComplete technology stack
The full picture across frontend, backend, and AI/ML — the target blueprint stack for the V2 platform.
Frontend Stack
Backend Stack
AI/ML Stack
Contracts
Blueprint · visionData & API architecture
The persistence and interface contracts: a Prisma relational schema, MongoDB analytics collections, and both REST and GraphQL surfaces.
Prisma schema design
// User Management
model User {
id String @id @default(cuid())
email String @unique
passwordHash String
profile Profile?
subscriptions Subscription[]
sessions Session[]
createdAt DateTime @default(now())
updatedAt DateTime @updatedAt
}
// Learning System
model LearningPath {
id String @id @default(cuid())
title String
description String
modules Module[]
difficulty Difficulty
estimatedHours Int
tags Tag[]
createdAt DateTime @default(now())
}MongoDB collections
User Analytics Collection
{
"_id": ObjectId(),
"userId": "cuid_user_id",
"events": [{
"type": "tool_usage",
"toolId": "interview_coach",
"timestamp": ISODate(),
"duration": 1200
}]
}Latency strategy
Blueprint · visionMulti-layer caching strategy
Three cooperating cache tiers — browser, CDN edge, and Redis — keep responses fast and the origin cool.
L1 — Browser Cache
Service Worker caching
LocalStorage for user preferences
IndexedDB for offline data
HTTP cache headers (1-24h TTL)
L2 — CDN Cache
CloudFlare edge caching
Static asset optimization
Image transformations
Geographic distribution
L3 — Redis Cache
Session management
API response caching
User-specific data
Real-time analytics
SLOs
Blueprint · visionPerformance targets & monitoring
Hard latency budgets across the rendering and AI pipeline, backed by a full observability stack.
Performance targets
Monitoring & observability
Trust
Blueprint · visionSecurity & compliance framework
Defense in depth across network, application, and data layers — mapped to the compliance standards we are building toward.
Security layers
Network Security
WAF with DDoS protection
SSL/TLS 1.3 encryption
IP whitelisting for admin
Application Security
OAuth 2.0 + JWT authentication
Role-based access control (RBAC)
API rate limiting
Data Security
AES-256 encryption at rest
PII data anonymization
Secure key management (HSM)
Compliance standards
Elasticity
Blueprint · visionScalability & infrastructure plan
Horizontal auto-scaling via Kubernetes HPA and reproducible infrastructure-as-code on AWS EKS, deployed across multiple regions.
Auto-scaling strategy
# Kubernetes HPA Configuration
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: kairoo-api-hpa
spec:
minReplicas: 3
maxReplicas: 50
metrics:
- type: Resource
resource:
name: cpu
target:
averageUtilization: 70Infrastructure as code
# Terraform AWS Infrastructure
resource "aws_eks_cluster" "kairoo" {
name = "kairoo-cluster"
role_arn = aws_iam_role.cluster.arn
version = "1.28"
vpc_config {
subnet_ids = aws_subnet.private[*].id
}
}Multi-region deployment architecture
US-East-1
Primary orchestration
EU-West-1
Low-latency cohort
AP-Southeast-1
DR active-standby
Sequencing
Blueprint · visionTechnical roadmap & future enhancements
A phased path from foundation to scale to innovation — including custom model training, edge compute, blockchain, and VR/AR learning modules.
Foundation Phase
Scaling Phase
Innovation Phase
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