Peter Gerhat

Mobile Data Architect

Building Reliable Data Foundations at Scale

Your app collects data. But is your team
actually making decisions with it?

→

30-minute call

→ You leave knowing exactly where bad data is costing you decisions

→ No more “I don’t trust the data” in product meetings

→ Outages caught in minutes, not reported by users

→ Field teams save 1–2 hours a day vs. paper or Excel workarounds

→ ML features that are live, not stuck in a Jupyter notebook

→ Ship AI features you can actually evaluate and iterate on

What I Do (Services)

DATA & ANALYTICS AUDIT

You suspect your app data is hiding something valuable. In 1–2 weeks, I map exactly what you're capturing, what's falling through the cracks, and where the highest-value fix is - before you spend budget on the wrong thing.

OFFLINE-CAPABLE FIELD APPS

Field teams work where connectivity dies. I build React Native apps with full offline capability, and on-device data validation, so data is captured accurately whether or not there's a signal - no server dependency and no lost submissions.

MOBILE ANALYTICS SETUP

Your app fires events. Nobody trusts what lands in BigQuery. I start at the source, instrument correctly from day one, build the delivery architecture, and hand you dashboards your CTO actually opens on Monday morning.

LLM/AI INTEGRATION IN MOBILE APPS

LLM features are landing in every roadmap. Almost no one can build the feature and measure whether it works. I do both: native integration into your app, plus the instrumentation that tells you if it's actually moving the numbers.

IoT / TELEMETRY PIPELINE & OBSERVABILITY

Device data lands somewhere. Nobody's watching it. I build a clean pipeline with SLOs, cost monitoring, and alerting that catches failures before your customers do - from fragmented telemetry to an observable, production-grade setup.

ON-DEVICE / EDGE ML INTEGRATION

Your data team has a model. It's been sitting in a notebook for six months. I get it into production - offline-capable, latency-optimized, running on the device where it needs to be. No more "we'll deploy it next quarter."

How We Engage

Step 1

Tracking Gap Audit

I review your existing mobile tracking and identify the most critical points where you’re losing events or capturing faulty data.

You get a clear overview: what’s coming through cleanly, what isn’t, and where the biggest risk of bad decisions lies.

Step 2

Event Pipeline Audit

I analyse your complete data flow from device to dashboard: instrumentation, validation, delivery.

You get a prioritised report with the concrete fixes that will have the biggest impact on your data quality.

Step 3

Custom Analytics Setup

I rebuild your analytics setup from scratch – from app instrumentation to data pipeline.

You end up with custom schemas, offline capture, conditional validation, everything Firebase and Mixpanel don’t cover.

Step 4

Analytics Operations Retainer

I manage your analytics infrastructure on an ongoing basis:

New events at feature releases, schema updates, data quality monitoring.

Not sure where your biggest data gap is?

Start with the Tracking Audit.

I'll tell you exactly where your event schema breaks down - and what it's costing you.

No agency overhead · 30-minute call

Cross-Platform App for the Intelligent Household

AIESEC University of Oslo Website

Mobile Wallet App

Information System for Understanding the Bicyclists

Localization of an Intelligent Assistant

Startup Cities Around the World Visualized

Not sure if your app data is telling you anything useful?

Start with a free 30-minute audit call.

One broken event schema can mean months of wrong product decisions. Let's find yours before it costs more.

Specific to your stack · 30-minute call

About Me

Most companies with a mobile app or connected devices collect data, but still make product decisions based on gut feeling.

The reason is almost always the same: mobile engineering and analytics work in silos. Events are tracked inconsistently, pipelines are fragile, and dashboards arrive too late to matter.

I fix exactly that gap.

With 10+ years spanning mobile engineering, data pipelines, and AI integration, at Vodafone building telemetry infrastructure at scale, and at Sprengnetter connecting field operations data to business decisions – I’m the single person who can both instrument your app and build the pipeline behind it.

No coordinating two freelancers. No handoff risk. Decisions from real data, in 4-6 weeks.

Peter Gerhat

Mobile Data Architect

Building Reliable Data Foundations at Scale

Your app collects data. But is your team
actually making decisions with it?

→