CEO
Eugene
Leads business discovery, prioritization, solution direction, and client outcomes across the engagement.
ABOUT / PRACTICAL BY DESIGN
Useful change, not AI theatre.
AIpractics is a founder-led AI and Data Science practice. We help companies understand where technology can remove inefficiency, then shape and build a solution around the way the business really works.
01 / Mission
Why AIpractics exists
Many companies lose time and capacity not because their people lack effort, but because information, decisions, and systems do not work together. AI can help, but only when it is applied to the right constraint and designed around real operations.
Our goal is to turn that inefficiency into mutual value: better operations and stronger capabilities for the client, and a long-term business built by delivering improvements that matter.
02 / Team
A focused team around the whole delivery path.
Meet the people and roles behind discovery, engineering, testing and delivery.
AI Architect
Dmitry
Shapes AI system architecture, provider boundaries, data flows, evaluation, security, and production controls.
AI Engineer
Alex
Builds and integrates language, automation, and model-powered product capabilities with reliable fallbacks.
Data Scientist
Anna
Frames measurable questions, prepares datasets, evaluates models, and makes uncertainty visible in decisions.
Tester
Julia
Tests critical journeys, edge cases, accessibility, and failure behavior before a solution reaches real operations.
Project Manager
Simon
Keeps scope, decisions, dependencies, communication, and delivery aligned with the client’s working reality.
03 / Principles
How we choose and build solutions
Flexibility does not mean improvising without structure. It means adapting the technology, scope, and delivery model while keeping the decision criteria clear.
Business first
We begin with the workflow, constraint, and desired decision—not with a preferred model or vendor.
Tailored by default
The useful answer may combine AI, conventional software, integrations, and process design around the way your company actually works.
Focused, measurable steps
We prefer a critical-path experiment with explicit success criteria before expanding the scope.
Human control where it matters
Uncertainty, permissions, approvals, and exceptions are designed into the solution rather than added after a failure.
Honest engineering
We make dependencies and limitations visible and do not promise certainty, savings, or AI capability that the evidence cannot support.
Available worldwide
We work across borders and time zones, setting clear communication windows and adapting collaboration to the client’s location.
Maximum practical flexibility
We can begin with a small diagnostic, prototype, focused integration, or a larger delivery and adjust scope as evidence improves.
Built around the client
The solution follows the client’s workflow, constraints, risk tolerance, systems, and team capability rather than forcing a standard package.
04 / Technologies
Technology selected for the job.
We combine proven tools across AI, data, product engineering, integrations, delivery, and operations. The stack follows the constraints and remains replaceable where provider lock-in matters.
- Language AI
- LLM APIs
- retrieval
- evaluation
- prompt and output controls
- provider adapters
- Data Science
- Python
- forecasting
- ranking
- recommendation
- optimization
- experiment design
- Vision & documents
- Computer Vision
- OCR
- extraction
- validation
- confidence thresholds
- human review
- Product engineering
- TypeScript
- React
- Next.js
- APIs
- PostgreSQL
- integrations
- background workflows
- Delivery & operations
- Docker
- Linux/VPS
- CI
- observability
- access controls
- backups
- performance testing
Start with one real source of inefficiency.
We will examine the process, identify plausible improvements, and recommend a focused first step without asking you to choose the technology in advance.