Bernt
58ca4e68db
- Go backend API with full CRUD for all modules (CRM, Sales, Finance, HR, Legal, Marketing, Support, Purchase, Inventory, Projects, Automation, Analytics) - Rust analytics service with parallel report generation - C runtime with POSIX shared memory IPC - PostgreSQL schema with 30+ tables, full migrations - Redis cache, sessions, pub/sub - Kafka event streaming with Zookeeper - WebSocket hub for real-time updates - Automation engine with cron jobs, workflows, event triggers - JWT authentication, multi-tenant from start - Docker Compose with all services - Nginx reverse proxy with rate limiting - Integration tests passing - Feature gap analysis against Fortnox/Odoo/Visma Refs: BOC-001
2.4 KiB
2.4 KiB
Landvex Datamodell — De Tre Looparna
1. Obesvarade Frågor-loggen (Efterfrågeloopen)
CREATE TABLE unanswered_questions (
id UUID PRIMARY KEY,
question_text TEXT NOT NULL,
customer_id UUID,
vertical VARCHAR(50),
geography VARCHAR(100),
willingness_to_pay DECIMAL(10,2),
confidence_required DECIMAL(3,2),
timestamp TIMESTAMPTZ DEFAULT NOW(),
resolved BOOLEAN DEFAULT FALSE,
resolution_method VARCHAR(20), -- 'model', 'human', 'partner'
resolution_cost DECIMAL(10,2),
-- KPI-derivat
days_to_resolve INT,
revenue_potential DECIMAL(10,2)
);
2. Sensor-nätverk (Utbudsloopen)
CREATE TABLE sensor_network (
id UUID PRIMARY KEY,
zoomer_id UUID,
geography VARCHAR(100),
acceptance_rate DECIMAL(5,2),
avg_completion_time_hours DECIMAL(6,2),
churn_30d BOOLEAN,
credit_earnings_30d DECIMAL(10,2),
-- Dynamisk prissättning
current_credit_multiplier DECIMAL(3,2) DEFAULT 1.0,
supply_tension_score DECIMAL(5,2)
);
3. Modell-träning (Modellloopen)
CREATE TABLE model_training (
id UUID PRIMARY KEY,
mission_id UUID,
uncertainty_score DECIMAL(5,4),
demand_score DECIMAL(5,4),
allocation_score DECIMAL(5,4), -- uncertainty * demand
source_type VARCHAR(20), -- 'satellite', 'partner', 'human'
cost_per_example DECIMAL(10,2),
model_version VARCHAR(20),
confidence_improvement DECIMAL(5,4),
timestamp TIMESTAMPTZ DEFAULT NOW()
);
4. Answer Rate-baslinje
CREATE TABLE answer_rate_metrics (
date DATE PRIMARY KEY,
total_questions INT,
answered_by_model INT,
answered_by_human INT,
unanswered INT,
answer_rate DECIMAL(5,2),
median_time_to_truth_hours DECIMAL(6,2),
cost_per_answer DECIMAL(10,2)
);
Dashboard Queries
Answer Rate Trend
SELECT
date_trunc('week', date) as week,
avg(answer_rate) as avg_answer_rate,
avg(median_time_to_truth_hours) as avg_ttt
FROM answer_rate_metrics
WHERE date >= NOW() - INTERVAL '12 weeks'
GROUP BY 1
ORDER BY 1;
Top Obesvarade (Roadmap)
SELECT
vertical,
geography,
COUNT(*) as count,
AVG(willingness_to_pay) as avg_wtp
FROM unanswered_questions
WHERE resolved = FALSE
AND timestamp >= NOW() - INTERVAL '30 days'
GROUP BY 1, 2
ORDER BY count DESC
LIMIT 20;
Supply Tension Heatmap
SELECT
geography,
AVG(supply_tension_score) as tension,
AVG(current_credit_multiplier) as multiplier
FROM sensor_network
WHERE churn_30d = FALSE
GROUP BY 1
ORDER BY tension DESC;