Peter
Gerhat
Turn appdata into decisions —
not ignored dashboards.
30-min call · No agency overhead · Clear gaps
Outages caught in minutes, not reported by users. Your ops team stops flying blind.
Field teams save 1–2 hours a day vs. paper or Excel workarounds. Every shift.
No more "I don't trust the data" in product meetings. Ship with confidence.
Built for teams that need one person
to own the gap.
From offline-first field workflows to telemetry pipelines, dashboards, and AI features in production.
Data & Analytics Audit
In 1–2 weeks, pinpoint what you're capturing, what's slipping through, and where to make the highest-value fixes.
Offline-Capable Field Apps
React Native apps that work offline, validate data on-device, ensuring accurate capture even without a signal.
Mobile Analytics Setup
Instrumentation set up right, architecture designed, and dashboards your CTO actually opens on Monday morning.
LLM / AI Integration
Native AI integration into your app, plus the instrumentation that tells you if it's actually moving the numbers.
IoT / Telemetry Pipeline
Clean pipeline with SLOs, cost monitoring, and alerting that catches failures before your customers do.
On-Device / Edge ML
Get a model out of its notebook and into production — offline-capable, latency-optimized, running on-device.
Not sure where your biggest data gap is?
Start with the Tracking Audit · No agency overhead · 30-minute call
From finding the gaps
to running clean analytics.
A structured engagement model focused on clean instrumentation, trustworthy data flow, and analytics that actually supports product decisions.
Tracking Gap Audit
Review existing mobile tracking and identify the most critical points where events are lost or faulty.
Event Pipeline Audit
Analyse complete data flow from device to dashboard: instrumentation, validation, transport, delivery.
Custom Analytics Setup
Redesign architecture from scratch — app instrumentation to pipeline structure and event schema.
Analytics Operations Retainer
Manage analytics infrastructure ongoing: new events, schema changes, and data quality monitoring.
Case studies & real outcomes.
Analytics, mobile product, and operational systems work across PropTech, FinTech, and more.
AI-Enabled Real Estate Valuation App
On-device AI model that estimates property value in real time — just by facing the building.
View case study →
Mobile Solution for Property Inspections
Offline-first inspection app for field teams, with on-device validation and zero lost submissions.
View case study →
Cross-Platform App for the Intelligent Household
Cross-platform mobile client for controlling an intelligent household, built for an international research group.
View case study →
Mobile Wallet App for NFC Payments
From early prototype to initial release — implementing design and iterating UX for NFC payment flows.
View case study →
Information System for City Infrastructure Planning
Software platform for cyclists and cities in Sweden, designed to increase cycling rates through data.
View case study →
Localization of an Airport AI-Assistant
Localizing an intelligent airport assistant for deployment in Sweden and Japan — culture-first AI design.
View case study →
I close the gap between instrumentation and decisions.
Companies with mobile apps or connected products often split ownership across mobile engineering, analytics, and reporting. That is where trust in the data starts to break.
Events get tracked inconsistently. Pipelines become fragile. Dashboards arrive too late to shape real product decisions.
My role is to remove that handoff risk. I work across instrumentation, data flow, telemetry reliability, and the decision layer — so your team gets a system that is usable, trusted, and built for scale.
Let's talk solutions.
I'll look at what you're currently tracking, where the gaps are, and what a complete pipeline would look like for your setup.