# Artem Frolov

Source: https://artemf.dev/

Applied AI Engineer & Data Scientist

Location: London, UK

I build AI systems for real workflows.

I'm an applied AI engineer and data scientist in London. I turn complex operational work into useful AI tools, from complaint investigation to pricing. My focus is reliable delivery: clear evaluation, traceable outputs, and people in control.

## Focus

- Governed LLM applications
- Agentic workflow design
- Retrieval and context engineering
- Databricks-native AI apps
- LangGraph orchestration
- MLflow evaluation and logging
- MLOps for production decision systems
- Internal tools for operational teams

## Currently

Building AI assistants and decision tools at Domestic & General.

## Open to

Applied AI, AI product, infrastructure, and agentic systems conversations.

## Links

- [GitHub](https://github.com/artemf375)
- [LinkedIn](https://www.linkedin.com/in/artemfr)
- [Email](mailto:afrolov01@icloud.com)

## Impact

- Live pilot Complaints Sidekick: AI investigation assistant moved from proof of concept into a Phase 2 live trial with complaint handlers (Source: complaints-sidekick)
- ~80% Less manual analysis: Approximate reduction in manual analysis effort for internal pricing and analytics AI assistants (Source: experiences)
- < 1 week ML deployment timeline: Model deployment reduced from approximately one month to under one week after MLOps improvements (Source: experiences)
- ~4% First-year retention: Approximate improvement associated with a cancellation propensity model integrated into pricing decisions (Source: experiences)
- Daily Transcript processing: Governed batch pipeline turns customer call transcripts into structured data for operational analysis (Source: call-transcript-analysis)

## Experience

### Senior Data Scientist — Domestic & General

2023-08 to Present · London, UK

Building AI assistants, machine learning models, and pricing tools in a regulated insurance business. Leading work from business case and architecture through evaluation, governance, and rollout.

- Led Complaints Sidekick from business case and architecture to a Phase 2 live trial, with complaint handlers reviewing AI summaries, evidence, and recommendations.
- Built evaluation, monitoring, and feedback frameworks with usage logs, traceable outputs, quality assurance review, and human oversight.
- Delivered AI assistants for pricing and analytics teams to investigate model performance and verify deployments, reducing manual analysis effort by approximately 80%.
- Introduced CI/CD, automated testing, model versioning, and reproducible training pipelines, reducing deployment timelines from approximately one month to under one week.
- Developed the organisation's first production cancellation propensity model for pricing decisions, with an approximately 4% improvement in first-year customer retention.
- Delivered pricing optimisation using Earnix, simulations, elasticity modelling, and lifetime value analysis.
- Worked with senior stakeholders across Pricing, Decision Science, Complaints, Operations, Risk, and Assurance to define requirements and deliver governed systems.

Skills: Production AI, Agentic Workflows, Regulated AI, Data Science, Machine Learning, Pricing, Databricks, MLOps, LangGraph, MLflow

### Co-Founder / Builder — Paloma Labs

2025-11 to Present · Delaware, US

Building analytics and machine learning tools for mobile apps, with a focus on event tracking, on-device prediction, and developer experience.

- Built early Swift SDK prototypes for automatic and manual event tracking.
- Designed event ingestion, session processing, and analytics schemas with FastAPI, PostgreSQL, and Redis.
- Explored on-device inference for behaviour prediction and privacy-conscious product analytics.
- Built web tools for configuration and analytics exploration.

Skills: Swift, SwiftUI, FastAPI, PostgreSQL, Redis, React, Product Analytics, On-device ML

### Net Revenue Management - Commercial Excellence — Henkel Ltd

2022-08 to 2023-02 · UK

Developed pricing analysis, retail reporting, and commercial decision tools for a consumer goods business.

- Analysed Dunnhumby, IRI, and commercial data to identify pricing and revenue-growth opportunities.
- Built reporting and decision tools, including electronic point-of-sale tracking and stock optimisation, that generated approximately £500k in commercial value.
- Translated business questions into analysis of pricing, promotions, stock, and category performance.

Skills: Revenue Management, Pricing Analytics, Power BI, Excel, Commercial Analytics

## Education

### Durham University — BEng, Electronic Engineering

2019-09 to 2022-06 · Durham, UK

Studied electronics, communications, signal processing, control systems, mathematics, and applied statistics, with practical work in modelling and engineering design.

- Led a six-person project on the engineering and commercial feasibility of torsion bar suspension for armoured vehicles; the project received a first-class mark.
- Applied mathematical modelling and control theory to evaluate system performance and engineering trade-offs.

Subjects: Statistics, Further Mathematics, Control Systems, Signal Processing, Electronics, Communications, Engineering Design

## Capabilities

### AI workflow discovery

Work with operational teams to understand the task, map the current workflow, and decide where AI can make a useful contribution.

- Discovery with Pricing, Complaints, and Operations
- Business cases and feasibility assessments
- Clear scope and success criteria

### Architecture & orchestration

Connect retrieval, model calls, and structured outputs in a workflow with clear responsibilities for each step and the people who review it.

- LangGraph orchestration for multi-step agent flows
- Structured outputs and function calling
- Next.js and Databricks apps

### Retrieval & context design

Bring calls, notes, repair history, and operational data into a useful case record, with governed access and traceable sources.

- Structured retrieval and summarisation workflows
- Unity Catalog-governed data and transcripts
- Batch and real-time context assembly

### Evaluation & observability

Define quality criteria, log model runs, and review behaviour in use. Combine automated evaluation with user feedback and quality assurance.

- MLflow evaluation and logging
- LLM-based evaluation and user feedback
- Usage logging, traceability, and QA review

### Governance & human oversight

Give reviewers the evidence and controls they need. Build review steps and audit trails into the workflow with Risk and Assurance teams.

- Human-in-the-loop controls and review-and-approve flows
- Audit trails, usage logging, and traceability
- Workflow design with Risk and Assurance

### Delivery & adoption

Take a system from prototype to live use. Establish repeatable releases, deployment checks, and the stakeholder support needed for adoption.

- CI/CD, model versioning, and reproducible training pipelines
- Release governance and deployment verification
- Stakeholder engagement and adoption

## Skills

### AI & Applied ML

- Agentic AI Systems
- LLM Workflow Design
- LangGraph Orchestration
- Retrieval-Augmented Generation (RAG)
- AI Evaluation Frameworks
- LLM-based Evaluation
- Structured Outputs
- Function Calling
- Prompt Engineering
- Feedback Loops
- GBM / Propensity Modelling
- Experiment Design & A/B Testing

### Business & Leadership

- Enterprise AI Governance
- Stakeholder Management
- Executive Communication
- Product Delivery
- Regulated Workflow Design
- Business Case Development

## Technology stack

### Languages & Data

- Python
- SQL
- Spark
- TypeScript
- Swift
- Power BI

### AI & ML

- OpenAI API
- Databricks
- MLflow
- LangGraph
- LangChain
- Scikit-Learn

### Web & Backend

- FastAPI
- PostgreSQL
- React
- Next.js
- Tailwind CSS

### Infra & Tools

- Docker
- Nginx
- Cloudflare
- Tailscale
- Git

## Projects

### Paloma

ML analytics SDK

An experimental Swift analytics SDK for app behaviour, with on-device prediction to explore the timing of prompts, offers, and paywalls.

Technology: Swift, React, FastAPI, PostgreSQL

### Nightime Doodles

iOS app

SwiftUI app experiments with AI, document workflows, push notifications, and lightweight backend services.

Technology: SwiftUI, Google Vertex AI, AWS Lambda, S3, APNs

### Raspberry Pi Homelab

Self-hosting / infrastructure

A Raspberry Pi setup for self-hosted services, private networking, reverse proxies, and deployment experiments.

Technology: Docker, Nginx, Tailscale, Cloudflare, Hermes, OpenClaw

## Experiments

### [AIrcade](https://artemf.dev/blog/aircade)

In-browser games and interactive experiences. Start with a journey from Earth to the cosmic web.

### [Model Creativity](https://artemf.dev/blog/model-creativity)

One prompt, different models. An ongoing collection of creative experiments, starting with Astra.

## Certifications

### Databricks Certified Generative AI Engineer Associate

Databricks · 2026-06

Credential ID: 184791628

Verification: https://credentials.databricks.com/aac117df-d06a-43ab-940d-1a9b528e6c4a

Certificate: https://artemf.dev/content/certs/nwxzwpua_1780992461967.png

### AI Agents Fundamentals

Databricks Academy · 2025-10

Certificate: https://artemf.dev/content/certs/aieng_fundamentals.png

## How production AI ships

From source data to reviewed, monitored decision support

- Ingest: Calls, notes, history, operational data
- Retrieve: Relevant context, governed access
- Reason: Model workflows, structured outputs
- Evaluate: MLflow evaluation, logs, feedback
- Human review: Evidence, approval, audit trail
- Deploy & monitor: Live rollout, monitoring, governance

## Case studies and blog

- [Blog](https://artemf.dev/blog.md): Markdown; canonical page https://artemf.dev/blog
- [Experiments](https://artemf.dev/experiments.md): Markdown; canonical page https://artemf.dev/experiments
- [Complaints Sidekick](https://artemf.dev/work/complaints-sidekick.md): Markdown; canonical page https://artemf.dev/work/complaints-sidekick
- [Customer Call Transcript Analysis](https://artemf.dev/work/call-transcript-analysis.md): Markdown; canonical page https://artemf.dev/work/call-transcript-analysis
- [The Age of One-User Software](https://artemf.dev/blog/the-age-of-one-user-software.md): Markdown; canonical page https://artemf.dev/blog/the-age-of-one-user-software
- [Second Self](https://artemf.dev/blog/second-self.md): Markdown; canonical page https://artemf.dev/blog/second-self
- [Slipstream](https://artemf.dev/blog/slipstream.md): Markdown; canonical page https://artemf.dev/blog/slipstream
- [AIrcade](https://artemf.dev/blog/aircade.md): Markdown; canonical page https://artemf.dev/blog/aircade
- [A little further](https://artemf.dev/blog/a-little-further.md): Markdown; canonical page https://artemf.dev/blog/a-little-further
- [Star Circuit](https://artemf.dev/blog/star-circuit.md): Markdown; canonical page https://artemf.dev/blog/star-circuit
- [Model Creativity](https://artemf.dev/blog/model-creativity.md): Markdown; canonical page https://artemf.dev/blog/model-creativity
- [Different worlds. The same direction.](https://artemf.dev/blog/sane-story.md): Markdown; canonical page https://artemf.dev/blog/sane-story
- [A mind made of maybes](https://artemf.dev/blog/insane-story.md): Markdown; canonical page https://artemf.dev/blog/insane-story
- [A mind made of maybes — Human edition](https://artemf.dev/blog/insane-story-human.md): Markdown; canonical page https://artemf.dev/blog/insane-story-human