Start with the problem.
Build only what solves it.
Mighty Data, Inc. works with founders and technical leaders who suspect AI could improve their business but aren’t yet sure where or how. We help identify the problem worth solving, define what success looks like, decide whether AI belongs, then engineer a guarded, reliable system you own outright.
Featured Media
Mikhail Voloshin on Why AI Needs Guardrails, Not Just Better Prompts
A practical look at separating the jobs AI does well from the parts conventional software should still control.
Define the job before choosing the tool.
Most AI projects that go sideways started with the technology and went looking for a problem. Every Mighty Data, Inc. engagement runs in the other direction, beginning with three questions.
01
What problem are we solving?
A specific roadblock, like a manual process that eats a team’s week or a data load your current software can’t handle.
02
What does success look like?
A measurable result, agreed on before any code gets written. If success can’t be described, it can’t be engineered.
03
Does AI actually belong here?
Sometimes. Often, conventional software does the job better and costs less. The tool gets chosen based on fit, and plenty of good systems use very little AI.
When AI does belong, the real engineering begins: the tests, limits, and guardrails that let it do its job… and nothing else.
Proof grounded in working systems
Mighty Data, Inc. has built working systems for apparel, biotech, and neuroscience research. Each one started as an operational problem somebody was tired of solving by hand.
Bézier makes leggings cut to the customer’s body from a few phone photos. Mighty Data, Inc. worked on every stage of the machine vision and CAD/CAM pipeline behind the app: photos become body measurements, and measurements become fabric cut patterns plotted for production. Made-to-measure clothing, with no fitting room involved.
Bézier New York
Alpenglow builds microscopes that capture 3D images of tissue at extraordinarily high resolution. The data came off the cameras faster than any existing software could handle. Mighty Data, Inc. engineered a parallelized pipeline that moves those massive image volumes into rendering software in workable time. No AI required, so none was used.
Alpenglow Biosciences
A neuroscientist was counting hundreds of dendritic spines per image by hand. The commercial alternative was a $200,000 software suite. Mighty Data, Inc. built a purpose-made ImageJ plugin instead. It flags likely spines using transparent statistical methods, and a researcher confirms each one, so every count can be documented in a published paper.
Dendritic Spine Counter
Completion, not dependency
Custom software should solve a problem, not create a new dependency. Every Mighty Data, Inc. engagement is built to leave the running system in your hands, along with its source code, documentation, and credentials. Clients often come back for extensions and new work. None of them have to.
You own the system and its source code
You hold every login and credential
You get documentation written for the people who run it
You decide whether Mighty Data, Inc. comes back
Experienced judgment across technology cycles
Mikhail Voloshin, CEO and principal engineer of Mighty Data, Inc., has spent more than 25 years building software, including work at Microsoft and Google, quantitative finance, and computational neuroscience.
He has watched technology cycles come and go, from the dot-com boom and broadband to social media, mobile, and AI. The tools change; the engineering discipline doesn’t. Today, he builds custom systems ranging from data pipelines and machine vision to scientific tools and production software. When AI belongs, his specialty is making it perform a defined job reliably, and stay within its boundaries.
Selected press and media
Good technology decisions start with understanding what the tools can and can’t do. Mikhail regularly speaks about AI, search, software, and the engineering discipline behind systems that have to work in the real world.
AI spending grows as businesses seek a clearer path to ROI ↗
DIGITAL JOURNAL
Why rising AI investment doesn’t automatically produce business value, and how clear requirements, measurable outcomes, safeguards, and human oversight turn experiments into durable systems.
The Wizard Academy
A practical introduction to what search engines are, how they evolved, and how the technology behind search interprets language.
Real Vision Presents
Conversations and demonstrations that unpack how GPT and large language models actually work, challenge common AI myths, and explore where the technology fits in practice.
Plugged In Podcast
How businesses can combine AI flexibility with conventional software guardrails to automate safely, reduce risk, and keep human authority where it matters.
Got a problem your team keeps solving by hand? Start here.
Tell Mikhail what’s stuck and what success would look like, and he’ll give you a straight answer on whether AI belongs in the fix.