Chernov

About

Anton Chernov

Founder & Operating Partner | INSEAD Global Executive MBA'27

AI and engineering executive who operates as a builder, P&L owner and value-creation partner. Two decades of turning technical depth into commercial outcomes — tripled revenue to $6M+ ARR at a 5M-user consumer platform through product-market repositioning and pricing redesign, then exited. Directed AI organisations at Amazon and Sony. INSEAD-trained in strategy, leadership, and private equity.

Chernov Consulting works with investors and growing companies as an operating partner — walking into a portfolio or growth-stage business, assessing the technology organisation, identifying where AI is the growth lever or the bottleneck, and driving the product and commercial decisions that move the revenue line.

Hands-on execution. From JTBD-driven product discovery and pricing model redesign to AI platform architecture and team rebuilds — we stay until the outcome is delivered.

Revenue & Product Value Creation

JTBD discovery and product-market repositioning
Pricing model design and A/B experimentation
New revenue stream identification and launch
Data-driven growth playbooks with measurable outcomes

AI Transformation & Platform

AI strategy and build-vs-buy decisions
LLM integration, RAG systems, and agentic workflows
Cloud-agnostic ML platform architecture
MLOps, CI/CD pipelines, and infrastructure at scale

Technology Assessment & Team Leadership

Technical due diligence for investors and acquirers
Engineering organisation assessment and restructure
Interim CTO / CPO / Head of AI mandate
Hiring, team culture, and engineering leadership development

Investor or portfolio company with a technology or AI challenge? Let's talk.

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Featured Work

Hands-on value creation across consumer platforms, enterprise AI, and frontier research — from tripling portfolio company revenue to production ML systems at Amazon scale

Operating Partner — Consumer Platform Revenue Transformation

Joined as Partner and CTO at a 5M-user consumer platform, co-owned the P&L, and tripled monthly recurring revenue from $150K to $530K+ ($6M+ ARR) in two years. Levers: JTBD discovery, pricing model redesign (30% MRR lift via A/B), new revenue streams, and data-driven experimentation. Full cycle — from engagement to equity to exit.

Product Strategy Revenue Growth Data Analytics A/B Testing P&L

AI for Professional Writers

Professional publishers needed AI-powered search across large document collections. A Retrieval Augmented Generation (RAG) system — complete data ingestion with cleaning, chunking, and embedding for vector-based similarity search — went from concept to production-ready deployment with full CI/CD within weeks.

Python OpenAI/GPT Elastic Search GCP RAG

The Learning Robot

A robotic arm that sees, tracks faces, and learns in real time — deep neural networks running on embedded devices, built on Apache MXNet in collaboration with The Qt Company. Edge computing meets computer vision. The demo generated 30+ enterprise leads at Embedded World and attracted new open-source contributors.

Deep Learning MXNet Computer Vision Robotics C++

Healthcare AI for Wearable Devices

Elderly care patients wearing sensor-equipped devices needed real-time fall detection — with models that adapted to each individual wearer over time. A healthcare AI platform streaming wearable sensor data, with self-retraining models running from embedded C++ on-device to cloud services. 0-to-1, from research to production.

Healthcare AI Wearables Embedded C++ Self-Retraining ML Edge AI

Financial Fraud Detection

Real-time transaction analysis to catch financial fraud before it causes damage. A machine learning system processing transaction streams, identifying anomalous patterns, and flagging suspicious activity — production AI protecting business-critical financial operations.

Fraud Detection Machine Learning Real-Time Analytics Financial AI

MLOps: Orchestration Tools on Kubernetes

Which orchestration tool should your team pick for ML pipelines on Kubernetes? A comparative analysis of Airflow, Prefect, Kubeflow and more — based on hands-on production experience across enterprise engagements. The article became a go-to reference for teams evaluating MLOps tooling.

MLOps Kubernetes Airflow Prefect

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Guiding Principles

Excellence in AI and engineering requires more than technical skills. These principles guide how I work and deliver exceptional results.

Be cool

Exceptional people are neither ordinary nor dull. They strive to find a niche for their unique abilities and work to inspire others.

If you've gotta think about being cool, you ain't cool.

Move fast

Exceptional people focus on progressing towards set business goals in small iterations. This allows them to be flexible and change direction where needed while keeping a sporty, enduring pace.

The first principle is that you must not fool yourself — and you are the easiest person to fool.

Balance

Exceptional people know that almost every hard decision is an act of balance. That's why we ride the wave.

Life is like riding a bicycle. To keep your balance, you must keep moving.

Elevate quality

Exceptional people have very high standards. They shape high-quality products, services and processes with exceptional, often unexpected quality.

My tastes are simple: I am easily satisfied with the best.

Always learn

Exceptional people are never done learning and always seek self-improvement.

There is only one good—knowledge, and one evil—ignorance.

Build trust

Exceptional people are honest with themselves and others. They value the strength of people's relationships over robustness of systems. They try to be helpful, instead of trying to be right.

People don't care how much you know until they know how much you care.

CHERNOV

AI VALUE CREATION