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 startups needing applied ML research on demand. 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-oriented engagement style built for startup pace, not enterprise procurement cycles | 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
Kyiv is home base for DataRoot Labs, founded in 2016 with a stated focus on applied data science research rather than broad IT outsourcing. Sources disagree on staff size, some citing as few as 11 employees and others closer to 200, likely reflecting how contractor networks get counted differently across platforms. What stays consistent across sources is the firm's specialization: machine learning models, computer vision pipelines, and hands-on AI R&D for startups that need research capability without building an internal team from scratch.
Intellectsoft
Intellectsoft was founded in 2007 by Alexey Kharchykov and Dmitriy Kulikov in Kyiv, though public sources now list headquarters in either New York or Palo Alto depending on the source. Employee estimates range from about 51-200 on LinkedIn to 200-300 elsewhere, 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 | Building an ML proof of concept ahead of a seed-stage fundraise., Getting an independent second opinion or 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 culture fits startups needing genuine experimentation over templated builds. |
| + | Small enough that founders talk directly to the engineers doing the work. |
| + | Kyiv-based ML talent typically comes at lower rates than US or Western European equivalents. |
| + | Named computer vision projects back up the specialization claim. |
| - | Employee counts vary widely across public sources, making capacity hard to pin down precisely |
| - | Limited public evidence of 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: building an ML proof of concept ahead of a seed-stage fundraise.
R&D-oriented engagement style built for startup pace, not enterprise procurement cycles. 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 |
|---|---|---|---|
| Building an ML proof of concept ahead of a seed-stage fundraise. | Strong | Strong | Both equally |
| Getting an independent second opinion or build on a computer vision pipeline. | Strong | Strong | Both equally |
| Building an AI feature that also needs blockchain-based data verification. | Strong | Strong | Both equally |
| 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-oriented engagement style built for startup pace, not enterprise procurement cycles.
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 culture fits startups needing genuine experimentation over 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 firm before shortlisting.
What are the main differences between DataRoot Labs and Intellectsoft?
DataRoot Labs's primary differentiator is: R&D-oriented engagement style built for startup pace, not enterprise procurement cycles. 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 firm before making a decision.