Aristek SystemsContact Us
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AI & DS-focused
research & development (R&D) services

Before you build, make sure it’s buildable.

23+

years of IT experience

5+

years of AI development expertise

160+

in-house employees

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Why invest in R&D at all?

R&D helps reduce uncertainty in tech initiatives. It brings clarity before development begins, especially for AI and data-driven products. R&D’s key benefits include:

  • Faster validation

    Research and prototyping shorten the path from concept to market.

  • Lower risk

    Minimize guesswork in architecture, tech stack, and product direction.

  • Smarter investment

    Focus resources on solutions that actually make sense to build.

  • Better positioning

    Stay prepared for shifting markets and evolving user needs.

  • Early feasibility check

    Test ideas and data before committing to full-scale engineering.

  • Waste reduction

    Avoid spending time and money on solutions that won’t work as planned.

Who benefits from external R&D and when to consider it

Not every company can maintain a dedicated R&D team. We can cover this gap, offering focused, practical research support exactly when and where it’s needed.

Here are the types of teams we support and the situations where focused R&D can help:

  • Startups exploring AI

    • Fast validation of early-stage ideas without building full solutions
    • Working prototypes for investor presentations and fundraising
    • No in-house AI or Data Science specialists, and no hiring plans yet
    • Uncertainty around MVP scope and technical feasibility

  • Enterprises in digital transformation

    • AI initiatives held back by lack of internal expertise
    • Limited capacity for in-depth research within existing teams
    • Misalignment between business goals and technical constraints

  • Software vendors adding AI features

    • Plans to embed AI into current products, but unclear architecture
    • Missing structured processes like hypothesis testing, prototyping, and ROI assessment
    • No team with AI/ML expertise for complex implementation challenges

  • Innovation teams in non-tech industries

    • Interest in testing AI use cases without long-term investment
    • Unclear potential of internal data for AI-driven solutions
    • Lack of technical capacity to explore ideas independently
    • Pressure to stay competitive and unlock new markets through innovation
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Is your team stuck between ideas and execution?

Our R&D support helps move things forward without adding full-time staff.

How we can help

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    Strategy & feasibility

    We help shape product ideas and validate early concepts through research, discovery sprints, and feasibility analysis.

    This helps avoid wasted time and ensures your team focuses on what can actually be built and delivered.

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    Prototyping & testing

    We develop fast, functional prototypes to test core assumptions.

    We build and test solutions, from ML model experiments to minimal AI-driven MVPs, in close collaboration with your product or engineering teams.

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    Consulting & guidance

    We support teams with decision-making: from selecting suitable AI models and frameworks to reviewing data readiness, compliance, and market trends.

    When needed, we step in as an external expert team to assess ideas or plans already on the table.

Meet our core R&D team

Our research and development consultants have strong technical backgrounds and hands-on experience in AI, product research, and solution delivery.

Depending on your case, we can bring in AI/ML engineers, frontend/backend developers, UI/UX designers, business analysts, and industry-specific experts.

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    Dmitri

    Head of R&D
    • Acts as the link between technical teams and your business stakeholders.
    • Defines strategy, milestones, and success metrics for the R&D process.
    • Coordinates specialists, manages timelines, and ensures the project stays aligned with
      both technical feasibility and business priorities.
    • Validates technical decisions for scalability, performance, and security.
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    Viktoryia

    Data Science Expert
    • Specializes in time series, computer vision, audio processing, LLMs, and transactional data analysis.
    • Brings domain knowledge in retail, healthcare, and banking.
    • Focuses on delivering accurate, production-ready models.
    • Drives R&D experimentation by testing multiple model approaches, assessing results, and recommending the most effective path forward.
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    Artem

    Data Science Expert
    • Holds an MS in Physics and deep expertise in ML frameworks and deployment tools.
    • Designs and leads AI projects in forecasting, NLP, computer vision, and recommendation systems.
    • Aligns technical solutions with measurable business improvements.
    • Shapes the technical foundation of R&D initiatives by selecting the right algorithms, tools, and architecture to match the project’s research goals.
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    Vadim

    Expert in the EdTech domain
    • Leverages expertise in EdTech and learning sciences to define features grounded in learner psychology and real education workflow
    • Provides educational logic and constraints for ML systems
    • Conducts research on learning effectiveness and define success metrics
    • Contributes to developing industry-specific IPs and frameworks (project accelerators)
    • Participates in customer discovery and pilot projects
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    Kate

    Expert in Healthcare & Veterinary
    • Ensures solutions products designed around real clinical needs
    • Provides domain logic for healthcare AI models
    • Verifies solutions are regulatory-safe and enterprise-ready
    • Contributes to developing industry-specific IPs and frameworks (project accelerators)
    • Participates in customer discovery and pilot projects
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    Eugene

    Expert in Logistics
    • Conducts regular industry research, identifies common pain points and market gaps.
    • Collaborates with data scientists to contribute domain logic for ML models
    • Contributes to developing industry-specific IPs and frameworks (project accelerators)
    • Ensures solutions are regulatory-safe and enterprise-ready
    • Participates in customer discovery and pilot projects
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    Olga

    Market & Product Researcher
    • Identifies market gaps, competitor activity, and industry benchmarks.
    • Validates concepts and supports value proposition design.
    • Ensures solutions meet real user needs and market demand.
    • Guides R&D direction by grounding technical exploration in verified market opportunities and user expectations.

R&D that turned into real solutions

Here are a few examples of what started as research and are now solving our customers’ real problems.

  • AI co-pilot for legal teams

    We worked with a logistics company’s legal department to automate contract review and risk flagging. The tool handles tasks like vendor questionnaires, clause checks, and NDA screening, cutting review time in half and improving detection of non-standard terms.

    • 50% time saved across legal operations
    • 90% accuracy in identifying risky clauses
    • Up to 60% less time spent on manual checks
    Explore project
    Data science & AILegalLogistics
    Slide 0: Preview of project 1
    Slide 0: Image of project 2

  • Custom AI certification coach

    We helped a global certification provider integrate AI into their learning platform. The assistant offers personalized study plans and instant support, improving both learner engagement
    and platform performance.

    • 89% increase in learner satisfaction
    • 27% fewer support tickets
    • 32% more completed study plans
    Explore project
    Data science & AIEdTech
    Slide 1: Preview of project 1
    Slide 1: Image of project 2

  • AI assistant for analytical dashboards

    A smart AI tool that integrates with corporate dashboards and provides analytical insights.

    • 85% of employee queries can now be solved
      without a data analyst
    • 50% faster insight generation
    • Efficiency in handling many users at once
    Explore project
    Data science & AILogistics
    Slide 2: Preview of project 1
    Slide 2: Image of project 2

  • AI-based sales forecast solution for a retail company

    We developed an AI solution that helps our client, a giant retailer, optimize sales. The tool does behavior analysis and forecasts sales. Key results:

    • 7% visitors-to-buyers conversion rate increase
    • 15% volume of data collected increase
    • 35% monthly infrastructure costs decrease
    Explore project
    Data science & AIRetail
    Slide 3: Preview of project 1
    Slide 3: Image of project 2

  • Consulting and R&D for Text-to-Speech in Education

    We helped an LMS platform provider find the best tool for enhancing the learning experience for their students. Our AI team assisted the client with researching the market, evaluating and analyzing existing solutions, and recommending the best strategies for integration.

    Explore project
    Data science & AIEdTech
    Slide 4: Preview of project 1
    Slide 4: Image of project 2

If this looks like something your team could use, let's talk.

Why choose Aristek

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    Deep industry experience

    We’ve worked with over 15 industries and know how to make AI work in real-world settings.

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    Expert team

    95% of our team holds BS, MSc, or PhDs.
    88% are Middle or Senior developers.

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    Partnerships with AWS and Microsoft

    As partners of AWS and Microsoft, we use trusted tools and cloud services that match your project needs.

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    Data privacy you can count on

    We follow strict security practices and industry regulations to keep your data safe.

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    Clear pricing and timelines

    We provide clear estimates, timelines, and next steps; no vague promises or hidden costs.

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    Ongoing team development

    We invest in regular training and growth to keep our specialists sharp and up to date.

How we approach R&D projects

1

Challenge intake (1–2 days)

After the contract and NDA are signed, we will discuss your idea and define the scope within 1-2 days

2

Discovery & ideation (2–4 days)

We explore possible directions, review similar solutions, and identify promising approaches through short internal research.

3

Feasibility check (3–5 days)

We evaluate the data, risks, and technical complexity to assess whether the idea can be built and how.

4

Prototyping (1–3 weeks)

We build a working proof-of-concept or MVP that shows how the solution performs in real conditions.

5

Iteration & validation (3–5 days)

We test the prototype, collect feedback, and assess how well the solution fits your broader goals.

6

Handoff or scale-up (2–3 days)

We deliver documentation, code, and transition plans or continue building if the project moves forward.

We have everything to scale your AI solution beyond R&D

Our broader expertise covers the services you need to move from a proven concept to a fully working product.

Have an idea worth testing?

Let’s see if it holds up.

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