AI
models
Artificial intelligence
Not as a buzzword. As a tool for analysis, automation, finding patterns, and working with internal knowledge.
technology consulting
m@ksim.pro
AI/
DE/
IT/
IS
I help owners and executives navigate technology decisions at the intersection of AI, data, IT, and information security.
This site is a continuation of a personal conversation after a meeting, business card, or referral.
AI / DE / IT / IS
Four areas
that rarely work alone
They feed each other. Data fuels AI. AI runs on infrastructure. Infrastructure has to be safe.
DE
data
Data engineering
Collecting, cleaning, integrating, and preparing data. Without this layer, analytics and AI become a polished demo with nothing underneath.
IT
systems
Information technology
Architecture, integrations, infrastructure, automation, and picking solutions you can actually maintain after launch.
IS
risk
Information security
Access, data, risk, security boundaries, and common sense when introducing new digital tools.
About
My name is Maxim Chernykh
For over 25 years I've worked with data, analytics, automation, and technology systems. My territory is the kind of problem where you can't just pick a stack and start coding. You have to understand the business case, the data, the architecture, the risks, and what can actually be delivered.
Background covers analytics, data science, integrations, ETL pipelines, industrial and operational data, IT infrastructure, and information security work.
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25+years in analytics and data science
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30+years in IT and business
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∞hands-on entrepreneurship
Principles
How I approach work
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Problem first, technology second.
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AI does not fix bad data.
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Simple architecture often beats trendy.
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Security has to be in the design from day one.
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A good system keeps running after the consultant leaves.
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Sometimes the best outcome is not starting a project that didn't need to start.
Common ground
What people bring me
01
Data and analytics
Reports don't reconcile. Data sits in different systems. Excel has quietly become part of the infrastructure. And leadership needs a single, sane picture.
02
AI and automation
There's an AI idea on the table. The real questions are where the value actually lives, what data the project needs, and how to keep it from becoming an expensive toy.
03
Integrations and pipelines
Pulling data from APIs, Excel, databases, ERP/CRM systems, websites, and external sources, and turning it into a process you can manage.
04
Industrial and operational analytics
Spotting deviations, losses, anomalies, and inefficiencies in operational processes before they turn into incidents.
05
Technology audit
The project is already running. The vendor speaks in jargon. Timelines slip. The owner or executive needs an independent read.
06
Information security
Looking at data, access, integrations, and AI initiatives not only through the lens of value, but also through the lens of risk.
Operator's lens
I look at technology through a business lens
I bring more than a technical background. I've also been on the business side. So I see a project as a system of decisions, money, timelines, risks, people, and accountability, not just a stack of tools.
What matters to me is that a solution isn't only technically correct. It has to be useful, deliverable, and durable.
Business case
What changes after rollout, and why it's worth doing.
Cost of ownership
What the solution costs not only on day one, but year after year.
Accountability
Who will use it, who will keep it running, and who answers for the outcome.
Blog
How I think about problems
Writing about data, AI, IT, security, and technology decisions, free of marketing fog.
Contact
If we've already met,
write to me
If you landed here on a referral, send me a short note: the problem, the context, and what you'd like to understand.
Email
m@ksim.pro
I reply personally. No bots, no drip sequences, no funnels. If the form below isn't your style, write to the email directly.