Cleveroad vs Intuz: full comparison for 2026
Quick verdict
Cleveroad (4.0/5) edges ahead of Intuz (3.9/5) overall. Cleveroad is the better choice for startups needing AI features inside a mobile or web product. Intuz is the stronger option for IoT-heavy products needing AI layered on top of device data. The right choice depends on your project size, budget, and required tech stack.
Cleveroad vs Intuz: head-to-head summary
| Criterion | Cleveroad | Intuz |
|---|---|---|
| Founded | 2011 | 2008 |
| HQ | Krakow, Poland | San Francisco, United States |
| Team size | 113-200 | 51-200 |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Production-deployment discipline carried over from a decade of mobile and web delivery | AI paired specifically with IoT delivery experience, not offered separately |
| Pricing model | Fixed project or dedicated team | Fixed project or dedicated team |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, React Native, AWS | Python, AWS IoT, TensorFlow |
| Industries served | Retail & e-commerce, Healthcare, Logistics | Manufacturing, Logistics, Healthcare |
Cleveroad vs Intuz: overview
Cleveroad
Cleveroad was founded in 2011, and public sources disagree on headquarters, with LinkedIn listing Claymont, Delaware and other trackers pointing to Krakow, Poland as the operational base. Employee estimates likewise vary, from roughly 113 up to a LinkedIn-reported 201-500 range. The company's roots are in mobile and web development for startups and enterprise clients, with safe, production-grade AI deployment positioned as a newer strength built on top of that existing delivery discipline.
Intuz
Intuz was founded in 2008 and lists headquarters in San Francisco, with additional operations in Ahmedabad, Gujarat. Employee estimates range from roughly 51-200 on LinkedIn down to about 55 in more recent tracking, again reflecting the common split between core staff and broader contractor networks. The firm positions itself as a digital transformation company spanning AI, IoT, mobile, and web applications, making AI one of several connected service lines rather than a standalone specialty.
Services and capabilities: Cleveroad vs Intuz
| Capability | Cleveroad | Intuz |
|---|---|---|
| Generative AI | ✓ | ✗ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| AI consulting | ✗ | ✗ |
| Fixed-price projects | ✓ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: Cleveroad vs Intuz
| Framework / platform | Cleveroad | Intuz |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | N/A | N/A |
| TensorFlow | ✓ | ✓ |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Cleveroad vs Intuz
| Criterion | Cleveroad | Intuz |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Fixed project, Dedicated team | Fixed project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Cleveroad vs Intuz
| Dimension | Cleveroad | Intuz |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Retail & e-commerce, Healthcare, Logistics | Manufacturing, Logistics, Healthcare |
| Best use cases | Adding AI features to a mobile app already in production., Getting a startup MVP built with AI as one feature among several, not the entire product. | Adding predictive AI models on top of an existing IoT device data stream., Running a combined IoT and AI pilot for a manufacturing or logistics client. |
| Typical project type | Fixed project | Fixed project |
Cleveroad vs Intuz: pros and cons
| Cleveroad | |
|---|---|
| + | Mobile and web development roots translate into disciplined production deployment practices. |
| + | Over a decade of delivery history across startup and enterprise clients. |
| + | Operates across four continents, giving flexible timezone coverage. |
| + | AI positioned as an addition to, not a replacement for, established product delivery skills. |
| - | Headquarters and employee count are reported inconsistently across public sources |
| - | AI-specific case studies are less prominent than the firm's mobile and web development portfolio |
| Intuz | |
|---|---|
| + | IoT and AI combined expertise suits connected-device products specifically. |
| + | US headquarters with over 15 years of digital transformation delivery. |
| + | Ahmedabad delivery center keeps project costs competitive. |
| + | Broad service coverage across mobile, web, IoT, and AI reduces the need for multiple vendors. |
| - | Reported headcount has dropped notably in recent tracking compared to earlier LinkedIn figures |
| - | AI is one of several service lines, not the firm's primary specialty |
Who should choose Cleveroad?
A typical fit: adding AI features to a mobile app already in production.
Production-deployment discipline carried over from a decade of mobile and web delivery. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Healthcare, Logistics.
Who should choose Intuz?
A typical fit: adding predictive AI models on top of an existing IoT device data stream.
AI paired specifically with IoT delivery experience, not offered separately. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Logistics, Healthcare.
Decision matrix: Cleveroad vs Intuz
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Cleveroad |
| You need a large dedicated team for an ongoing programme | Cleveroad |
| Your budget is at the lower end | Compare: Cleveroad (Not disclosed) vs Intuz (Not disclosed) |
| You need specialist depth in a specific vertical | Cleveroad |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: Cleveroad vs Intuz
| Use case | Cleveroad fit | Intuz fit | Winner |
|---|---|---|---|
| Adding AI features to a mobile app already in production. | Strong | Strong | Both equally |
| Getting a startup MVP built with AI as one feature among several, not the entire product. | Strong | Strong | Both equally |
| Adding predictive AI models on top of an existing IoT device data stream. | Strong | Strong | Both equally |
| Running a combined IoT and AI pilot for a manufacturing or logistics client. | Limited | Strong | Intuz |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Cleveroad vs Intuz
Cleveroad (4.0/5) is the stronger overall choice for most AI Development projects. Production-deployment discipline carried over from a decade of mobile and web delivery.
Intuz (3.9/5) is worth a look if you need running a combined IoT and AI pilot for a manufacturing or logistics client. If your situation matches that, Intuz is a competitive option.
Related comparisons
Cleveroad vs Intuz FAQ
Is Cleveroad better than Intuz?
Cleveroad (4.0/5) scores higher overall, but "better" depends on your use case. Cleveroad's strongest advantage: mobile and web development roots translate into disciplined production deployment practices. Intuz's strongest advantage: IoT and AI combined expertise suits connected-device products specifically.
How do Cleveroad and Intuz differ in pricing?
Cleveroad uses fixed project or dedicated team pricing. Intuz 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: Cleveroad or Intuz?
Cleveroad 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 Cleveroad and Intuz?
Cleveroad's primary differentiator is: production-deployment discipline carried over from a decade of mobile and web delivery. Intuz's primary differentiator is: AI paired specifically with IoT delivery experience, not offered separately. They also differ in team size (113-200 vs 51-200), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Retail & e-commerce, Healthcare vs Manufacturing, Logistics).
Verify all details directly with each company before making a decision.