Industry: Logistics & TSL

Real-time tracking, AI optimization and dashboards for logistics and TSL.

Q: How long does a pilot implementation take?

Pilot/MVP takes 4-6 weeks: GPS tracking for core fleet, basic ETA prediction, KPI dashboard, and 1 TMS/WMS integration. Full production build: 8-12 weeks. Discovery (2 weeks) precedes both options.

Q: Do you integrate with our existing telematics providers?

Yes. We integrate via telematics provider APIs (Samsara, Geotab, Trimble, Webfleet) or directly from OBD-II/CAN bus. During Discovery we map your existing telemetry sources and build unified data pipelines.

Q: How does the AI route optimization work?

We use CVRPTW (Capacitated Vehicle Routing Problem with Time Windows) solvers combined with ML models. The system considers: traffic patterns, delivery time windows, vehicle capacity, driver hours, fuel costs. Recalculates in real-time when conditions change.

Q: Can we start with tracking only and add AI later?

Absolutely. That's exactly the Pilot → Production Build path. Start with GPS tracking and dashboards (Pilot/MVP), collect data, then add AI route optimization and demand forecasting (Production Build). The architecture is modular from day one.

Q: How do you handle data security and GDPR?

RBAC (Role-Based Access Control) with row-level security. Encryption at rest and in transit. Driver PII anonymized in analytics. GDPR-compliant data retention policies. Infrastructure-as-code with security scanning. Penetration testing before production deploy.

Q: What metrics will we track after deployment?

Primary: OTIF, ETA accuracy, dwell time, empty run rate. Business: fuel costs, SLA penalty reduction, dispatcher efficiency. Technical: API latency, WebSocket uptime, queue depth. All in a shared Grafana dashboard with real-time alerts.

We integrate data from TMS/WMS/ERP/telematics into one system — so you see every shipment, every truck, every delay in real time.

CUSTOM DEV Custom FMS/WMS panels, GPS tracking, ETA prediction
NEURAL AI route optimization, demand forecasting, load planning
SIGNAL Real-time KPI dashboards: OTIF, SLA, dwell time

−15% empty runs · 98% OTIF · ROI in 4 months

Book a 15-min operational-technical consultation Get a quote in 24h

No obligations. NDA on request.

−15% empty runs → See Case 98% OTIF → See Case ROI 4 months → See Case

THE COST OF INACTION

Lack of visibility in logistics costs every day — in fuel, time and claims.

No ETA = phones ringing, clients waiting, dispatchers guessing
No dwell time data = you're burning resources at loading docks
Manual reports = delayed decisions, invisible trends
Empty runs at 25-30% = margin burned on diesel and driver time

Show me where costs are leaking →

Loading dock — current state

WHAT WE DELIVER

What we deliver in practice: FMS, AI and Reporting.

CUSTOM DEV

Real-time fleet tracking

Custom FMS panels with GPS, Google Maps API, WebSocket for live position updates. ETA prediction based on traffic, weather, and historical data. ePOD and driver mobile app.

GPS tracking + Google Maps API
WebSocket real-time updates
ETA prediction (ML model)
ePOD + driver mobile app

ETA accuracy 94%

See case study: fleet tracking →

NEURAL

AI route & load optimization

ML models for route optimization considering traffic, time windows, and vehicle capacity. Demand forecasting for fleet planning. Automatic load consolidation to minimize empty runs.

Route optimization (CVRPTW)
Demand forecasting
Load consolidation
What-if scenario simulation

−15% empty runs

See case study: AI optimization →

SIGNAL

KPI dashboards & client visibility

Real-time OTIF, SLA, and dwell time dashboards. Client-facing tracking portals. Automated reports replacing manual Excel work. Alerts for SLA breaches and anomalies.

OTIF / SLA / dwell time dashboards
Client tracking portal
Automated KPI reports
SLA breach alerts

98% OTIF

See case study: KPI dashboards →

HARD PROOF

Results in numbers. Process in artifacts.

3PL Operator • CUSTOM DEV

Problem

No real-time fleet visibility. Dispatchers relied on phone calls. ETA accuracy below 60%. Client complaints rising 20% QoQ.

Was wir getan haben

GPS integration + WebSocket live tracking panel
ML-based ETA prediction model
Client-facing tracking portal

Ergebnisse

58% → 94%

ETA accuracy

−40%

Client complaints

−65%

Dispatcher calls

Tech

Node.js WebSocket PostgreSQL Google Maps API React

Distribution Company • NEURAL

Problem

25-30% empty runs. Routes planned manually in spreadsheets. No demand forecasting — over-provisioning fleet by 15%.

Was wir getan haben

CVRPTW route optimization engine
Demand forecasting ML pipeline
Load consolidation module

Ergebnisse

28% → 13%

Empty runs

−18%

Fuel costs

4 months

ROI

Tech

Python OR-Tools Redis Kafka React

Logistics Network • SIGNAL

Problem

OTIF below 85%. No SLA monitoring — breaches discovered post-factum. Weekly manual Excel reports taking 2 days to compile.

Was wir getan haben

Real-time OTIF/SLA dashboard
Automated KPI report pipeline
Anomaly detection & SLA alerts

Ergebnisse

83% → 98%

OTIF

2 days → real-time

Report time

−70%

SLA penalties

Tech

Grafana PostgreSQL Node.js SSE Sentry

ETA Accuracy — Before vs After

Route Optimization — Before vs After

IRON PROCESS

Implementation process that doesn't wreck operations.

Each step has a measurable deliverable. You know what you're getting before we start.

1 Day 1-3

Discovery Sprint

Fleet audit, TMS/WMS integration mapping, KPI baseline, data quality assessment. Output: Integration Architecture Document.

2 Day 4-7

Architecture & API Contracts

System design, API contracts with TMS/WMS, offline strategy (queue + retry), idempotency guarantees. Go/No-Go with client.

3 Week 2-6

Build Sprint

Implementation in 2-week sprints. Daily standups, weekly demos. Staging environment mirroring production topology.

4 Week 7-8

QA on Real Operations

Testing with real GPS data, exception simulation (GPS gaps, API timeouts), offline mode verification, load testing at 3× expected volume.

5 Week 9

Deploy & Handover

Canary rollout (10% → 50% → 100%), knowledge transfer session with your team, runbook documentation, SLA activation.

Release Checklist

Idempotency verified
Offline mode tested
Exception simulation passed
Load test 3× volume
Rollback plan ready
Runbook documented

PACKAGES

Packages for logistics operations.

Discovery

Audit & roadmap

2 weeks

Fleet & operations audit
TMS/WMS integration mapping
KPI baseline & target setting
Data quality assessment
Architecture recommendation

Start Discovery

Pilot / MVP

Core tracking + dashboards

4-6 weeks

GPS tracking for core fleet
ETA prediction (v1)
Basic KPI dashboard
1 TMS/WMS integration
Staging environment

Start Pilot

Production Build

Full system + AI modules

8-12 weeks

Full FMS with all integrations
AI route optimization
Client tracking portal
Automated KPI reports
Offline mode + queue
Load testing & canary deploy

Plan Production Build

Maintenance

Monitoring + SLA

Monthly

24/7 uptime monitoring
Performance regression alerts
Security patch management
Monthly KPI reports
Priority support (SLA)

Discuss SLA

Final price depends on fleet size, number of integrations and data volume. Free estimate after consultation.

What's included in the price

Full source code ownership
Git repo with complete history
CI/CD pipeline setup
Staging environment
Knowledge transfer session
Post-launch support (30 days)

What we DON'T do

Miracles without your data — we need TMS/WMS access
"Quick fixes" without measuring results
Vendor lock-in — everything is yours
Promises without data and POC

COMMON CONCERNS

Most common concerns — straight answers.

How do you integrate with our TMS/WMS?

We support REST API, SOAP, EDI, and direct database connectors. During Discovery we map every integration point, document API contracts, and build adapters. We've integrated with SAP TM, Oracle TMS, Transporeon, TimoCom, and dozens of custom WMS systems.

What happens when GPS or API goes down?

Offline-first architecture: message queue (Kafka/RabbitMQ) with automatic retry and exponential backoff. Idempotent operations ensure no duplicate processing. GPS gaps are interpolated. System auto-recovers without manual intervention. SLA includes uptime guarantees.

What does the cost depend on?

Three factors: fleet size (determines infrastructure scale), number of integrations (TMS, WMS, ERP, telematics), and data volume (GPS pings/minute, report complexity). We provide a fixed-price estimate after Discovery. No hidden costs.

Do we get the full source code and repository?

Yes. Your Git repo, full commit history, infrastructure-as-code (Terraform/Docker), CI/CD configs. We run a knowledge transfer session with your team. Zero vendor lock-in — you own everything from day one.

We have an internal team — why external?

Specialization. Your team knows the business — we know WebSocket at scale, GPS data pipelines, and ML route optimization at a depth that comes from building it across dozens of logistics companies. We accelerate, transfer knowledge, and leave.

TECH STACK

Technology stack for high-volume operations.

WebSocket / SSE Node.js / TypeScript React Google Maps Platform PostgreSQL Redis Kafka / RabbitMQ Python (ML) OR-Tools / CVRPTW Docker / Kubernetes Terraform RBAC Sentry Grafana Prometheus

Full code ownership. Your repo, your infra — zero vendor lock-in.

FAQ

FAQ: Visibility, optimization and telematics.

OTIF (On-Time In-Full): A supply chain KPI measuring the percentage of deliveries completed on time and in full. Industry benchmark: 95%+. Leading logistics operators target 98%+ with real-time monitoring.
Dwell Time: The time a vehicle spends at a facility (loading dock, warehouse, depot) between arrival and departure. Excessive dwell time indicates operational bottlenecks. Industry average: 2-4 hours; optimized operations: <1 hour.
Telematics: Technology combining telecommunications and informatics to monitor vehicles remotely. Includes GPS tracking, engine diagnostics (OBD-II), driver behavior analysis, fuel consumption, and temperature monitoring for cold chain.
FMS (Fleet Management System): A software platform for managing commercial vehicle fleets. Core functions: GPS tracking, route optimization, maintenance scheduling, driver management, fuel monitoring, and compliance reporting.
ePOD (Electronic Proof of Delivery): Digital confirmation of goods delivery replacing paper-based proof. Captures signature, photo, timestamp, and GPS location. Accelerates invoicing, reduces disputes, and enables real-time delivery status updates.
WebSocket: A communication protocol providing full-duplex communication channels over a single TCP connection. In logistics, used for real-time position updates, live tracking dashboards, and instant alert delivery without polling overhead.

How long does a pilot implementation take?

Pilot/MVP takes 4-6 weeks: GPS tracking for core fleet, basic ETA prediction, KPI dashboard, and 1 TMS/WMS integration. Full production build: 8-12 weeks. Discovery (2 weeks) precedes both options.

Do you integrate with our existing telematics providers?

Yes. We integrate via telematics provider APIs (Samsara, Geotab, Trimble, Webfleet) or directly from OBD-II/CAN bus. During Discovery we map your existing telemetry sources and build unified data pipelines.

How does the AI route optimization work?

We use CVRPTW (Capacitated Vehicle Routing Problem with Time Windows) solvers combined with ML models. The system considers: traffic patterns, delivery time windows, vehicle capacity, driver hours, fuel costs. Recalculates in real-time when conditions change.

Can we start with tracking only and add AI later?

Absolutely. That's exactly the Pilot → Production Build path. Start with GPS tracking and dashboards (Pilot/MVP), collect data, then add AI route optimization and demand forecasting (Production Build). The architecture is modular from day one.

How do you handle data security and GDPR?

RBAC (Role-Based Access Control) with row-level security. Encryption at rest and in transit. Driver PII anonymized in analytics. GDPR-compliant data retention policies. Infrastructure-as-code with security scanning. Penetration testing before production deploy.

What metrics will we track after deployment?

Primary: OTIF, ETA accuracy, dwell time, empty run rate. Business: fuel costs, SLA penalty reduction, dispatcher efficiency. Technical: API latency, WebSocket uptime, queue depth. All in a shared Grafana dashboard with real-time alerts.

GET STARTED

If you want to see logistics in real time — let's start with 15 minutes.

You'll get: operations audit, optimization plan, quote. NDA on request. Zero spam.

Kalender wird geladen...