Scopic vs Keyrus: full comparison for 2026
Last updated: July 2026
Quick verdict
Scopic (4.2/5) edges ahead of Keyrus (3.8/5) overall. Scopic is the better choice for healthcare, fintech, and enterprise teams building genuinely custom ML systems without off-the-shelf shortcuts. Keyrus is the stronger option for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience. The right choice depends on your project size, budget, and required tech stack.
Scopic vs Keyrus: head-to-head summary
| Criterion | Scopic | Keyrus |
|---|---|---|
| Founded | 2006 | 2000 |
| HQ | Marlborough, MA | Paris, France |
| Team size | 250+ | 3,500+ |
| Rating | 4.2 / 5 | 3.8 / 5 |
| Best for | Healthcare, fintech, and enterprise teams building genuinely custom ML systems without off-the-shelf shortcuts | International enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience |
| Pricing model | Fixed project, T&M | T&M, retainer |
| Min. engagement | $25K+ | $50K+ |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, Tableau, Power BI |
| Industries served | healthcare, fintech, manufacturing, transportation, retail | financial, retail, healthcare, manufacturing, media |
Scopic vs Keyrus: overview
Scopic
Scopic was founded in 2006 and is headquartered in Marlborough, Massachusetts. The company has 250+ specialists distributed across six continents and has completed 1,000+ projects for healthcare, fintech, and enterprise clients, including machine learning, natural language processing, computer vision, and predictive analytics systems. Scopic distinguishes itself with a track record of engineering genuinely custom ML systems — not API wrappers — using TensorFlow, PyTorch, and computer vision pipelines. (Project count and founding year per Scopic official website.)
Keyrus
Keyrus is an international consulting group founded in 2000, headquartered in Paris, France, and operating in over 20 countries with 3,500+ professionals. The company positions itself at the intersection of business, data, and AI — helping clients move from experimental AI to industrial-grade ML systems in production. Services span data strategy, BI, analytics, AI testing, and ML deployment. (Employee count and global footprint per Keyrus official website.)
Services and capabilities: Scopic vs Keyrus
| Capability | Scopic | Keyrus |
|---|---|---|
| Custom ML build | ✓ | ✗ |
| ML consulting | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP / LLM | ✓ | ✗ |
| Predictive analytics | ✓ | ✓ |
| MLOps | ✗ | ✓ |
| Data engineering | ✗ | ✓ |
| Generative AI | ✗ | ✗ |
| Staff augmentation | ✗ | ✗ |
| Fixed-price projects | ✓ | ✗ |
| Dedicated team model | ✗ | ✓ |
Tech stack comparison: Scopic vs Keyrus
| Framework / platform | Scopic | Keyrus |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
Pricing comparison: Scopic vs Keyrus
| Criterion | Scopic | Keyrus |
|---|---|---|
| Minimum engagement | $25K+ | $50K+ |
| Engagement models | Fixed project, T&M | T&M, Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Scopic vs Keyrus
| Dimension | Scopic | Keyrus |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | healthcare, fintech, manufacturing | financial, retail, healthcare |
| Best use cases | Computer vision quality inspection system, Medical imaging ML classification | Industrial AI deployment at enterprise scale, Analytics and ML platform for financial services |
| Typical project type | Fixed project | T&M |
Scopic vs Keyrus: pros and cons
| Scopic | |
|---|---|
| + | 1,000+ delivered projects with verifiable case studies |
| + | Covers full ML spectrum: NLP, computer vision, predictive analytics |
| + | Custom ML engineering only — no API-wrapper work |
| + | 20-year delivery history reduces engagement risk |
| + | Distributed team across 6 continents provides broad timezone coverage |
| - | US headquarters with offshore delivery — requires clear async communication process |
| - | Large project portfolio means higher selectivity on smaller or shorter engagements |
| Keyrus | |
|---|---|
| + | Global footprint: 20+ countries, 3,500+ professionals |
| + | Industrial-AI focus — moves clients from PoC to production scale |
| + | Strong analytics and BI alongside ML for full data stack coverage |
| + | AI testing and validation capability |
| - | Large-firm pricing not suited to startup or SMB budgets |
| - | AI is one offering within broader data consulting — not ML-first |
Who should choose Scopic?
Scopic is the right choice for healthcare, fintech, and enterprise teams building genuinely custom ML systems without off-the-shelf shortcuts.
20-year track record of custom ML engineering across 1,000+ projects — no API-wrapper shortcuts. Minimum engagement starts at $25K+. Works best with clients in healthcare, fintech, manufacturing, transportation, retail.
Who should choose Keyrus?
Keyrus is the right choice for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience.
From experimental AI to industrial AI — consulting group specialising in productionising ML for large organisations. Minimum engagement starts at $50K+. Works best with clients in financial, retail, healthcare, manufacturing, media.
Decision matrix: Scopic vs Keyrus
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Scopic |
| You need a large dedicated team for an ongoing programme | Keyrus |
| Your budget is at the lower end | Scopic |
| You need specialist depth in a specific vertical | Scopic |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Scopic |
Use case fit: Scopic vs Keyrus
| Use case | Scopic fit | Keyrus fit | Winner |
|---|---|---|---|
| Computer vision quality inspection system | Strong | Limited | Scopic |
| Medical imaging ML classification | Strong | Limited | Scopic |
| Industrial AI deployment at enterprise scale | Limited | Strong | Keyrus |
| Analytics and ML platform for financial services | Limited | Strong | Keyrus |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Scopic vs Keyrus
Scopic (4.2/5) is the stronger overall choice for most Machine Learning projects. 20-year track record of custom ML engineering across 1,000+ projects — no API-wrapper shortcuts. It is best for healthcare, fintech, and enterprise teams building genuinely custom ML systems without off-the-shelf shortcuts.
Keyrus (3.8/5) is the better choice when international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience. If your situation matches those criteria, Keyrus is a competitive option.
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Scopic vs Keyrus FAQ
Is Scopic better than Keyrus?
Scopic (4.2/5) scores higher overall, but "better" depends on your use case. Scopic is better for healthcare, fintech, and enterprise teams building genuinely custom ML systems without off-the-shelf shortcuts. Keyrus is better for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience.
How do Scopic and Keyrus differ in pricing?
Scopic uses fixed project, t&m pricing with a minimum engagement of $25K+. Keyrus uses t&m, retainer pricing with a minimum engagement of $50K+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Scopic or Keyrus?
Keyrus is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.
What are the main differences between Scopic and Keyrus?
Scopic's primary differentiator is: 20-year track record of custom ml engineering across 1,000+ projects — no api-wrapper shortcuts. Keyrus's primary differentiator is: from experimental ai to industrial ai — consulting group specialising in productionising ml for large organisations. They also differ in team size (250+ vs 3,500+), minimum engagement ($25K+ vs $50K+), and primary industries served (healthcare, fintech vs financial, retail).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.