Scopic vs Modak: full comparison for 2026
Last updated: July 2026
Quick verdict
Scopic (4.2/5) edges ahead of Modak (3.7/5) overall. Scopic is the better choice for healthcare, fintech, and enterprise teams building genuinely custom ML systems without off-the-shelf shortcuts. Modak is the stronger option for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption. The right choice depends on your project size, budget, and required tech stack.
Scopic vs Modak: head-to-head summary
| Criterion | Scopic | Modak |
|---|---|---|
| Founded | 2006 | 2016 |
| HQ | Marlborough, MA | San Jose, CA |
| Team size | 250+ | 100–200 |
| Rating | 4.2 / 5 | 3.7 / 5 |
| Best for | Healthcare, fintech, and enterprise teams building genuinely custom ML systems without off-the-shelf shortcuts | Large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption |
| Pricing model | Fixed project, T&M | T&M, retainer |
| Min. engagement | $25K+ | $50K+ |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, Apache Spark, Databricks |
| Industries served | healthcare, fintech, manufacturing, transportation, retail | financial, healthcare, manufacturing, logistics, saas |
Scopic vs Modak: 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.)
Modak
Modak is an AI-native data engineering company headquartered in San Jose, California, founded in 2016. The company uses machine learning techniques to transform how structured and unstructured enterprise data is prepared, consumed, and shared — focusing on AI-driven data modernisation for large organisations. Global consulting services help enterprises modernise data infrastructure, accelerate AI readiness, and drive measurable business outcomes. (Founding year and approach per Modak official website and ZoomInfo.)
Services and capabilities: Scopic vs Modak
| Capability | Scopic | Modak |
|---|---|---|
| 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 Modak
| Framework / platform | Scopic | Modak |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
Pricing comparison: Scopic vs Modak
| Criterion | Scopic | Modak |
|---|---|---|
| Minimum engagement | $25K+ | $50K+ |
| Engagement models | Fixed project, T&M | T&M, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Scopic vs Modak
| Dimension | Scopic | Modak |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | healthcare, fintech, manufacturing | financial, healthcare, manufacturing |
| Best use cases | Computer vision quality inspection system, Medical imaging ML classification | Enterprise data modernisation for AI readiness, ML-powered ETL and data prep pipeline |
| Typical project type | Fixed project | T&M |
Scopic vs Modak: 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 |
| Modak | |
|---|---|
| + | ML applied to data engineering itself — accelerates data prep for ML programmes |
| + | AI-native from inception — not a repositioned data warehouse firm |
| + | Strong on unstructured data processing for AI readiness |
| + | San Jose HQ with enterprise client focus |
| - | Data engineering focus — not suited to custom ML model development or computer vision |
| - | Minimum engagement oriented toward large enterprise programmes |
| - | Less suited to companies without an existing large data estate |
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 Modak?
Modak is the right choice for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption.
ML-powered data engineering — uses ML itself to accelerate data prep and modernisation at enterprise scale. Minimum engagement starts at $50K+. Works best with clients in financial, healthcare, manufacturing, logistics, saas.
Decision matrix: Scopic vs Modak
| 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 | Check each company's engagement model |
| 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 Modak
| Use case | Scopic fit | Modak fit | Winner |
|---|---|---|---|
| Computer vision quality inspection system | Strong | Limited | Scopic |
| Medical imaging ML classification | Strong | Limited | Scopic |
| Enterprise data modernisation for AI readiness | Limited | Strong | Modak |
| ML-powered ETL and data prep pipeline | Limited | Strong | Modak |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Scopic vs Modak
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.
Modak (3.7/5) is the better choice when large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption. If your situation matches those criteria, Modak is a competitive option.
Related comparisons
Scopic vs Modak FAQ
Is Scopic better than Modak?
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. Modak is better for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption.
How do Scopic and Modak differ in pricing?
Scopic uses fixed project, t&m pricing with a minimum engagement of $25K+. Modak 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 Modak?
Modak 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 Modak?
Scopic's primary differentiator is: 20-year track record of custom ml engineering across 1,000+ projects — no api-wrapper shortcuts. Modak's primary differentiator is: ml-powered data engineering — uses ml itself to accelerate data prep and modernisation at enterprise scale. They also differ in team size (250+ vs 100–200), minimum engagement ($25K+ vs $50K+), and primary industries served (healthcare, fintech vs financial, healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.