DataRoot Labs vs Intellectsoft: full comparison for 2026
Quick verdict
DataRoot Labs (4.4/5) edges ahead of Intellectsoft (3.9/5) overall. DataRoot Labs is the better choice for data-heavy startups needing applied ML research capacity. Intellectsoft is the stronger option for enterprises wanting AI alongside blockchain or IoT work. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs Intellectsoft: head-to-head summary
| Criterion | DataRoot Labs | Intellectsoft |
|---|---|---|
| Founded | 2016 | 2007 |
| HQ | Kyiv, Ukraine | New York, United States |
| Team size | 11-50 | 150-300 |
| Rating | 4.4 / 5 | 3.9 / 5 |
| Primary differentiator | R&D-style engagement model built for startups, not enterprise procurement | Combines AI with blockchain and IoT engineering under one roof |
| Pricing model | Dedicated team or fixed project | Fixed project or dedicated team |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, PyTorch, scikit-learn | Python, AWS, Ethereum |
| Industries served | Healthtech, Fintech, Retail & e-commerce | Healthcare, Financial services, Manufacturing, Retail & e-commerce |
DataRoot Labs vs Intellectsoft: overview
DataRoot Labs
DataRoot Labs is a Kyiv-based data science and AI consulting company founded in 2016. Team size estimates vary by source, ranging from roughly 11 to 200 employees depending on whether contractors and R&D partners are counted, but the firm consistently positions itself around applied research and development for data science and AI-powered startups rather than broad enterprise IT outsourcing. Its focus stays narrow: machine learning models, computer vision pipelines, and AI R&D partnerships for companies that need a research-capable team without hiring one in-house.
Intellectsoft
Intellectsoft was founded in 2007 by Alexey Kharchykov and Dmitriy Kulikov in Kyiv, and public sources list headquarters variously in New York and Palo Alto today. Employee estimates range from about 51-200 on LinkedIn to 200-300 on other trackers, with the company describing 150-plus engineers across 10 offices. Its practice spans custom software development, AI, blockchain, and cloud computing for enterprise, SMB, and startup clients, giving it broad but not deeply specialized AI coverage.
Services and capabilities: DataRoot Labs vs Intellectsoft
| Capability | DataRoot Labs | Intellectsoft |
|---|---|---|
| Generative AI | ✗ | ✗ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| AI consulting | ✓ | ✗ |
| Fixed-price projects | ✓ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: DataRoot Labs vs Intellectsoft
| Framework / platform | DataRoot Labs | Intellectsoft |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: DataRoot Labs vs Intellectsoft
| Criterion | DataRoot Labs | Intellectsoft |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Fixed project | Fixed project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataRoot Labs vs Intellectsoft
| Dimension | DataRoot Labs | Intellectsoft |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthtech, Fintech, Retail & e-commerce | Healthcare, Financial services, Manufacturing |
| Best use cases | Standing up a machine learning proof of concept before a startup's seed round closes., Getting a second opinion or independent build on a computer vision pipeline. | Building an AI feature that also needs blockchain-based data verification., Running a mixed IoT and AI project under a single engineering team. |
| Typical project type | Dedicated team | Fixed project |
DataRoot Labs vs Intellectsoft: pros and cons
| DataRoot Labs | |
|---|---|
| + | Research-oriented culture suits startups that need genuine ML experimentation, not templated builds. |
| + | Small team keeps communication direct between founders and the engineers doing the work. |
| + | Kyiv talent pool gives strong ML fundamentals at lower rates than US or Western European firms. |
| + | Computer vision work is a genuine specialty backed by named client projects. |
| - | Reported employee counts vary widely by source, making true capacity hard to verify |
| - | Limited public information on enterprise-scale delivery experience |
| Intellectsoft | |
|---|---|
| + | Broad technology coverage means AI can be paired with blockchain or IoT work without a second vendor. |
| + | Nearly two decades of custom software delivery experience. |
| + | 150-plus engineers across 10 global offices support flexible staffing. |
| + | Enterprise, SMB, and startup client mix shows adaptability across budget levels. |
| - | Headquarters location and employee count are reported inconsistently across sources |
| - | AI is one of several core specialties rather than the firm's defining focus |
Who should choose DataRoot Labs?
A typical fit: standing up a machine learning proof of concept before a startup's seed round closes.
R&D-style engagement model built for startups, not enterprise procurement. Minimum engagement is not publicly disclosed. Works best with clients in Healthtech, Fintech, Retail & e-commerce.
Who should choose Intellectsoft?
A typical fit: building an AI feature that also needs blockchain-based data verification.
Combines AI with blockchain and IoT engineering under one roof. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Financial services, Manufacturing, Retail & e-commerce.
Decision matrix: DataRoot Labs vs Intellectsoft
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DataRoot Labs |
| You need a large dedicated team for an ongoing programme | DataRoot Labs |
| Your budget is at the lower end | Compare: DataRoot Labs (Not disclosed) vs Intellectsoft (Not disclosed) |
| You need specialist depth in a specific vertical | Intellectsoft |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | DataRoot Labs |
Use case fit: DataRoot Labs vs Intellectsoft
| Use case | DataRoot Labs fit | Intellectsoft fit | Winner |
|---|---|---|---|
| Standing up a machine learning proof of concept before a startup's seed round closes. | Strong | Limited | DataRoot Labs |
| Getting a second opinion or independent build on a computer vision pipeline. | Strong | Strong | Both equally |
| Building an AI feature that also needs blockchain-based data verification. | Limited | Strong | Intellectsoft |
| Running a mixed IoT and AI project under a single engineering team. | Limited | Strong | Intellectsoft |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs Intellectsoft
DataRoot Labs (4.4/5) is the stronger overall choice for most AI Development projects. R&D-style engagement model built for startups, not enterprise procurement.
Intellectsoft (3.9/5) is worth a look if you need running a mixed IoT and AI project under a single engineering team. If your situation matches that, Intellectsoft is a competitive option.
Related comparisons
DataRoot Labs vs Intellectsoft FAQ
Is DataRoot Labs better than Intellectsoft?
DataRoot Labs (4.4/5) scores higher overall, but "better" depends on your use case. DataRoot Labs's strongest advantage: research-oriented culture suits startups that need genuine ML experimentation, not templated builds. Intellectsoft's strongest advantage: broad technology coverage means AI can be paired with blockchain or IoT work without a second vendor.
How do DataRoot Labs and Intellectsoft differ in pricing?
DataRoot Labs uses dedicated team or fixed project pricing. Intellectsoft uses fixed project or dedicated team pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: DataRoot Labs or Intellectsoft?
Intellectsoft is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between DataRoot Labs and Intellectsoft?
DataRoot Labs's primary differentiator is: R&D-style engagement model built for startups, not enterprise procurement. Intellectsoft's primary differentiator is: combines AI with blockchain and IoT engineering under one roof. They also differ in team size (11-50 vs 150-300), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthtech, Fintech vs Healthcare, Financial services).
Verify all details directly with each company before making a decision.