Documentation Platform Overview

The Future of Home Care Systems. AI-driven and connected in real-time.

Caire automates 50%+ of manual scheduling work, pushes staff efficiency to 75–80%, and protects continuity for tens of thousands of clients. Explore how the platform runs end-to-end—from data ingestion and optimization to enterprise-grade security.

CAIRE platform overview hero

Platform at a Glance

Proven production metrics from Swedish municipal deployments show how the Caire platform transforms operational, financial, and human outcomes simultaneously.

75–80%+
Staff efficiency (care time vs shift time)
50%+
Reduction in manual scheduling work
Up to 20%
Travel time reduction for field staff
+1–2%
Margin improvement without new hires
AI Flywheel

Compounding Value for Every Stakeholder

Each optimization run produces better schedules, happier staff, and more predictable outcomes. Data from every execution feeds the intelligence layer, strengthening the platform for all providers on the network.

  • Executives: Realized 75–80%+ care time and transparent ROI dashboards.
  • Schedulers: AI handles constraints; planners focus on strategic choices.
  • Caregivers: Shorter routes, protected breaks, and continuity for clients.
  • Municipalities: Lower emissions, better use of public budgets, higher trust.
Illustration of the CAIRE data flywheel
Caire’s free-tier analytics invites more providers to connect data, sharpening the AI for everyone.

Before vs After – Manual Chaos to AI Autopilot

The platform eliminates spreadsheet firefighting and unlocks strategic execution.

                        flowchart TB
                            subgraph BEFORE["❌ BEFORE: Manual Scheduling"]
                                B1["📊 Hours of spreadsheet work"]
                                B2["🚗 Excess travel & mileage"]
                                B3["😓 <75% efficiency"]
                                B4["📞 Phone trees & ad-hoc fixes"]
                                B5["🔄 High churn, poor continuity"]
                            end
                            subgraph BRIDGE["⚡ CAIRE PLATFORM"]
                                C1["AI ingestion + validation"]
                                C2["Scenario optimization"]
                                C3["Real-time replanning"]
                            end
                            subgraph AFTER["✅ AFTER: Automated Excellence"]
                                A1["⏱️ 50%+ scheduling time saved"]
                                A2["📉 Up to 20% less travel"]
                                A3["😊 75–80%+ efficiency"]
                                A4["🧠 Continuity-first assignments"]
                                A5["📈 +1–2% margin uplift"]
                            end
                            BEFORE --> BRIDGE --> AFTER
                            style BEFORE fill:#FCA5A5,stroke:#DC2626,stroke-width:3px
                            style BRIDGE fill:#2563EB,stroke:#059669,stroke-width:4px,color:#fff
                            style AFTER fill:#A7F3D0,stroke:#2563EB,stroke-width:3px
                        
Overwhelmed scheduler trapped in paperwork before CAIRE
Before Caire: manual workflows trap schedulers in administrative mazes.

Self-Driving Operations Levels

The evolution of home care operations from Level 0 to Level 4.

Self-Driving Operations Levels Chart
  • Level 0: Phone & Spreadsheets (Manual)
  • Level 1: Digital Planning – Static plans, manual adjustments, no optimization.
  • Level 2: Assisted Scheduling (Live today) – Optimization engine, daily adjustments. Advanced optimization (manual trigger). Produces ready-to-publish proposals.
  • Level 3: Self-Driving Scheduling (Roadmap) – Autonomous handling of disruptions. Real-time optimization (streaming GPS, cancellations, traffic). Planners only review exceptions.
  • Level 4: Predictive Operations (Roadmap) – Autonomous operations, forecasting using data and ML pipelines.

Enterprise Architecture & Data Flow

A unified architecture supporting all levels of automation—from assisted planning to predictive operations.

System Architecture

flowchart LR %% ========= COLORS (Caire palette-inspired placeholders) ========= classDef source fill:#cce7ff,stroke:#2a7de1,color:#000; classDef storage fill:#e0ffe5,stroke:#31a353,color:#000; classDef process fill:#fff3c4,stroke:#c79b00,color:#000; classDef security fill:#ffe0e0,stroke:#d63a3a,color:#000; classDef output fill:#e4d7ff,stroke:#7a4ed8,color:#000; %% ========= DIAGRAM START ========= subgraph "Attendo Systems (eCare Welfare)" welfareAPI["Welfare API
(REST / Authentication)"]:::source welfareCSV["Welfare CSV Export
(optional)"]:::source welfarePDF["PDF: Care Decisions
(manual upload)"]:::source end subgraph "Caire Platform (AWS Stockholm - EU/EEA)" ingest["Data Ingestion Layer
(API Gateway / Import Services)"]:::process validate["Validation + Normalization
(Business Rules)"]:::process encrypt["Encryption & Access Controls
(Transport & At-Rest)"]:::security store["Datastore (High-Level)
Structured + Files
(e.g. relational + object storage)"]:::storage optimize["Optimization Engine
Batch Scheduling"]:::process audit["Logging, Audit & Monitoring
(Request logs, scheduling logs)"]:::security end subgraph Outputs ui["Web UI
(Planner, Schedule Views)"]:::output exportCSV["Export / Reports / CSV"]:::output apiOut["API Response
(Planned schedule)"]:::output end %% ========= FLOWS ========= welfareAPI --> ingest welfareCSV --> ingest welfarePDF --> ingest ingest --> validate --> encrypt --> store --> optimize --> audit optimize --> ui optimize --> exportCSV optimize --> apiOut
System Architecture

Care Provider Core System

  • Planning Data (Visits, Time Windows, Addresses, Skills/Tags)
  • Staff Data (Shifts, Availability, Skills, Employment Type, Geography)
  • Case/Welfare Data (Needs, Care Plans, Restrictions)
  • Location Data (Home addresses, Units, Zones)

Integration Layer

  • API ingest (preferred)
  • CSV import (secondary)
  • Secure PDF uploads (care plans)
  • Validation + Schema Mapping

Scheduling Engine (Core Logic)

  • Phase 1: Demand Aggregation – Merge visits by time/route/geo, Identify batchable windows
  • Phase 2: Candidate Assignment Generation – Match employees → visits by constraints, Filter by role, skills, continuity, travel feasibility
  • Phase 3: Optimization – Routing solver (minimize travel time), Compression objective (reduce idle time), Priority: continuity → employee preference → cost → travel
Scheduler Experience

From Firefighting to Strategic Command

Caire auto-resolves sick calls, route conflicts, and gaps in minutes. Planners start each morning with validated proposals and can launch what-if scenarios with one click.

  • Morning startup: 15–20 minutes to review AI proposals.
  • Intraday: real-time optimization flips reactive phone trees into proactive alerts.
  • Monthly: ghost “ideal” schedules highlight when it’s time to re-baseline the roster.
Screenshot of the Caire scheduling interface with AI proposals
Unified dashboards bring schedule data and decisions into a single view.
Team onboarding caregivers through a digital flow
AI workflows guide onboarding so every caregiver starts aligned.
AI Recommendation Loop

Visit Lifecycle – Onboarding to Continuous Learning

The recommendation engine guides planners from the first municipal decision to continuous optimization:

  • Onboard: AI reads care plans and suggests ideal visit patterns and competencies.
  • Approve: Clients and planners adjust preferences; the solver rebalances instantly.
  • Operate: Daily automation keeps schedules feasible as reality shifts.
  • Improve: Monthly baseline comparisons expose when efficiency drifts, prompting re-optimization.

Scenario Lab – Test Trade-offs Before Acting

Conservative continuity, balanced operations, aggressive savings—the Scenario Lab runs them all on the same data so leaders can select the path that matches policy and budgets.

Conservative

Protects continuity at all costs. Useful during change management and sensitive client cohorts.

Balanced

Recommended daily driver weighting travel, efficiency, and continuity for steady 75–80% performance.

Aggressive

Pushes costs and wait times down when staffing is tight—often used for special projects.

Custom

Fine-tune weights per municipality, area, or contract. Save templates for recurring scenarios.

Architecture Snapshot

Enterprise Stack Built for Swedish Healthcare

The platform blends modern web tech, optimization engines, and secure EU infrastructure.

  • Frontend: Next.js 15, React 19, Tailwind, shadcn/ui, professional calendar components.
  • Backend: Next.js API routes, Drizzle ORM, Redis cache, AWS EC2 (EU).
  • Auth & Security: Clerk v6 SSO/MFA, organization-scoped RBAC, HTTPS/HSTS, audit trails.
  • DevOps: GitHub Actions CI/CD, PM2 clustering, CloudWatch, Sentry, automated DR.
AI Compliance & Trust

Built for the EU AI Act

Caire uses a deterministic "White Box" approach, ensuring full transparency and control.

  • No "Black Box": Our optimization engine is rule-based and deterministic. No hallucinations or hidden training biases.
  • Human in the Loop: The system is a decision-support tool. Planners always retain final approval.
  • Data Privacy: No customer data is used to train external models. Your data stays yours.
Read full compliance statement →

Technical Architecture & AI Compliance

A transparent, secure, and compliant foundation for modern home care. We ensure your data remains yours—never used to train public models.

Data Lifecycle

From Input to Insight (see diagram)

  • Input: Data is ingested via secure APIs (e.g., Carefox), encrypted CSV imports, or direct input. (See nightly process)
  • Storage: All data resides in EU-hosted PostgreSQL databases with strict tenant isolation (Row Level Security).
  • Processing: The AI engine optimizes schedules in memory using mathematical rules. No customer data is used for training. (See system overview)
  • Output: Optimized schedules are presented for human review and can be exported back to source systems.
EU AI Act

Compliance & Safety

  • Low Risk Classification: CAIRE is an operational optimization tool, not a "High Risk" system under the EU AI Act.
  • Explainable by Design: No "black box" neural networks. Every decision is traceable to configured rules and weights.
  • Data Sovereignty: You own your data. We do not use it to train generative models or share it with third parties.
  • Human-in-the-Loop: The system is designed to support human planners, not replace their final judgment.

Security & Compliance Checklist

Designed for Swedish municipalities with EU residency, GDPR controls, and healthcare data governance from day one.

Data Protection

  • All data hosted in EU via Amazon RDS (Stockholm region).
  • Organization ID enforced on every query and cache lookup.
  • Nightly backups with point-in-time recovery and retention policies.
  • Full audit trails for schedule changes and optimization runs.

Access & Monitoring

  • Clerk MFA, SSO, and per-role permission enforcement.
  • HttpOnly, SameSite, and Secure cookies; CSRF protection.
  • CloudWatch + Sentry alerts for anomalies and degradation.
  • Pen-test ready: layered defenses and sanitized inputs.
Implementation Model

Automation with Human Control

Caire hides the complexity but never removes oversight. Teams approve changes, run what-if experiments, and export back to source systems on their terms.

  • Nightly automation prepares fresh schedules before the workday starts.
  • Schedulers review, adjust, and publish via unified dashboards in minutes.
  • Leadership monitors KPIs and ROI in real time to steer investments.
  • Level 3 Roadmap: Continuous learning improves route heuristics, continuity weights, and forecasts.
Digital stethoscope with an AI icon
AI supports care delivery while keeping humans firmly in control.

Slinga Technical Architecture

Technical details on how recurring patterns (slingor) and visit pinning enable stable yet flexible scheduling.

Database Schema

Core Tables

  • slinga: Stores recurring weekly patterns per caregiver, weekday, and shift. Includes version tracking for pattern evolution.
  • slinga_visits: Individual visits within a slinga pattern, including planned times, sequence, and pinning status.
  • visits: Extended with slingaId, pinned (boolean), and assignment fields (assignedEmployeeId, assignedStartTime, assignedEndTime).
  • solution_visits: Candidate assignments from optimization jobs, allowing comparison before acceptance.
Pinning Mechanics

Fixed vs Flexible Visits

  • Pinned visits: Marked with pinned=true, these visits cannot be moved by the optimization engine. They maintain continuity and stability.
  • Unpinned visits: Marked with pinned=false, these visits can be optimized by AI to improve efficiency, travel time, and workload balance.
  • Pinning rules: When a visit is pinned, earlier conflicting visits in the same shift must also be pinned to satisfy optimization constraints.
  • Assignment flow: Visits start unpinned, get optimized, then become pinned when accepted by planners.
From-Patch API

Incremental Optimization

  • Full solve endpoint: Used for cold starts and major redesigns. Sends complete schedule with all visits and pinning flags.
  • From-patch endpoint: Used for incremental updates. Sends only changed visits, inherits previous solution and pinned assignments.
  • Use cases: Real-time disruptions (Scenario C), adding new clients (Scenario B), fine-tuning daily schedules.
  • Performance: From-patch is faster because it reuses previous solution as starting point, typically completing in seconds vs minutes for full solve.
Data Flow

Schedule Generation Process

  • Import: External schedules converted to slingor with visits marked pinned=true.
  • Daily expansion: Published slingor expanded into concrete visits for each date, all marked pinned.
  • Optimization: Full schedule sent to optimization engine. Pinned visits stay fixed, unpinned visits can move.
  • Solution storage: Candidate assignments stored in solution_visits table for comparison.
  • Acceptance: When planner accepts, assignment fields updated on visits. Slinga template remains unchanged.
Technical Constraints

Pinning Rules & Validation

  • Hard constraints: Skills matching, no overlaps, time windows, and pinned flags are always respected by the optimization engine.
  • Soft constraints: Continuity, travel time, workload balance, and unused hours recapture are optimized but can be overridden when necessary.
  • Pinning validation: System ensures that when a visit is pinned, all earlier visits in the same shift that would conflict are also pinned.
  • Freeze time: For real-time disruptions, visits that have started or are imminent remain pinned automatically (freeze time concept).
Coming Early 2026

CAIRE 2.0: Next-Generation Platform

A complete platform rebuild with a new scalable AI stack, modern data model, and redesigned user experience. Built for enterprise scale while maintaining the simplicity that makes CAIRE powerful.

Scalable AI Stack

Next-Generation Optimization

  • Enhanced Optimization Engine: Improved solver performance with better constraint handling and faster optimization cycles
  • GraphQL API: Unified API layer replacing REST endpoints for better performance and developer experience
  • Microservices Architecture: Modular design enabling independent scaling of optimization, analytics, and data processing
  • Real-time Processing: Support for live schedule updates and instant optimization feedback
  • Advanced Caching: Intelligent caching layer reducing optimization job times by up to 40%
New Data Model

v2.0 Schema Architecture

  • Normalized Schema: Complete redesign for better data integrity and query performance
  • Slingor Support: Native support for recurring weekly patterns (slingor) as first-class entities
  • Revision System: Built-in versioning for schedules, solutions, and optimization scenarios
  • Flexible Constraints: Enhanced constraint model supporting complex organizational rules
  • Audit Trail: Comprehensive logging of all schedule changes and optimization decisions
  • Multi-tenancy: Improved tenant isolation and data security at the database level
Redesigned UX

Professional Calendar Interface

  • Professional Calendar View: Industry-leading calendar component for intuitive drag-and-drop scheduling
  • Status-Based Visual System: Color coding by scheduling status (optional, mandatory, priority) rather than care type
  • Real-time Validation: Instant feedback on constraint violations during drag operations
  • Optimization Scenarios: Pre-configured optimization presets (Daily Plan, New Clients, Disruption Management) with customizable weights
  • Comparison Mode: Side-by-side comparison of schedule revisions with delta metrics
  • Mobile Responsive: Optimized experience across desktop, tablet, and mobile devices

Benefits

Performance & Scale

  • Handle 10x more schedules simultaneously
  • 50% faster optimization job completion
  • Support for organizations with thousands and tens of thousands of employees
  • Sub-second response times for common operations

User Experience

  • Intuitive drag-and-drop scheduling interface
  • Reduced learning curve for new schedulers
  • Better visual feedback and constraint validation
  • Streamlined workflows for common tasks

Migration & Timeline

CAIRE 2.0 is planned for release in early 2026. The migration will be seamless for existing customers:

  • Zero Downtime Migration: Data migration happens in the background with no service interruption
  • Backward Compatibility: Existing schedules and data remain fully accessible during transition
  • Training & Support: Comprehensive training materials and dedicated support during rollout
  • Gradual Rollout: Phased deployment starting with pilot organizations

For Current Customers: All existing functionality will be preserved and enhanced. The new platform builds upon the proven CAIRE foundation while adding enterprise-grade scalability and modern UX.