Peter Gerhat

Peter Gerhat

Mobile AI Engineer

Building Reliable Data Foundations at Scale

Your app collects data.
But are you actually getting insights?
Or just dashboards no one uses?
  • 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 test and iterate on

What I Do (Services)

DATA & ANALYTICS AUDIT

You suspect your app data is hiding something valuable. In 1–2 weeks, I’ll pinpoint what you’re capturing, what’s slipping through, and where to make the highest-value fixes. This way, you won’t waste budget on the wrong things.

OFFLINE-CAPABLE FIELD APPS

Field teams work where connectivity dies. I create React Native apps that work offline. They validate data on the device, ensuring accurate capture, even without a signal. This means no server reliance and no lost submissions.

MOBILE ANALYTICS SETUP

Your app fires events, but nobody trusts what lands in BigQuery. I begin at the source. I ensure the instrumentation is set up right and design the architecture. Finally, I give you dashboards that 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

From finding the gaps to running a reliable analytics operation.

A simple, structured engagement model focused on clean instrumentation, trustworthy data flow, and analytics that actually supports product decisions.

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 of 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, transport, and delivery.
  • You receive a prioritised report with the concrete fixes that will have the biggest impact on your data quality.
Step 3

Custom Analytics Setup

  • I redesign your analytics architecture from scratch — from app instrumentation to pipeline structure and event schema.
  • You end up with custom schemas, offline capture, and conditional validation for what standard tools don’t cover.
Step 4

Analytics Operations Retainer

  • I manage your analytics infrastructure on an ongoing basis: new events for releases, schema changes, and data quality monitoring.
  • Your setup stays reliable as the product evolves, instead of degrading with every new feature shipped.
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.
GET YOUR FREE
MOBILE DATA AUDIT
No agency overhead · 30-minute call

Portfolio

Selected work and case studies

Examples of mobile analytics, data infrastructure, field operations, and product work. Click any project to view the full case study.

No portfolio items found.

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

Start with the 30-minute audit call.

One broken event schema can mean months of wrong product decisions. Let's find yours before it costs more.
GET YOUR FREE
MOBILE DATA AUDIT
Specific to your stack · 30-minute call

About Me

Data Science Go 2019 Conference

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 across mobile engineering, data pipelines, and AI integration, at Vodafone building large-scale telemetry infrastructure, and at Sprengnetter connecting field operations data to business decisions – I’m the single person who can both instrument your app and design the pipeline behind it.

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

Experience

Technical Product Owner

Technical Product Owner

Software Developer

Product Planner

Application Developer

Software Engineer

Education

MSc Information Systems

MBA Business Analytics

BSc Software Engineering

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