Modus Create
Fully remote product engineering company founded in 2011, with AI/ML and data engineering practices.
Founded 2011 | Reston, USA | 501–1,000 employees | Last updated: July 2026
ml-consultingdata-engineering-mlcustom-model-training
What is Modus Create?
Modus Create is a fully remote, distributed product engineering company founded in 2011 and headquartered in Reston, Virginia, with team members spread across more than 55 countries. The company's AI/ML and data engineering practice includes AI Strategy Roadmap assessments and AI Data Foundation assessments intended to ensure underlying data is reliable and properly governed before or alongside model development work. Modus Create has partnered with technology providers including Atlassian, GitHub, and AWS, and has been recognized on the Inc. 5000 list for nine consecutive years.
Modus Create was founded in 2011 and is headquartered in Reston, USA. The firm employs 501–1,000 people and works primarily with clients in Technology/SaaS, Retail, Healthcare sectors. Its primary differentiator is: Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development..
Modus Create tech stack and services
PythonAWSData governance toolingCloud ML platforms
| Service area | Details |
|---|---|
| Running an AI Data Foundation assessment before committing to a full model-development engagement | Available for Technology/SaaS, Retail, Healthcare clients |
| Building an AI strategy roadmap for an organization new to machine learning adoption | Available for Technology/SaaS, Retail, Healthcare clients |
| Combining broader remote product engineering with an embedded ML workstream | Available for Technology/SaaS, Retail, Healthcare clients |
| Engaging a distributed team for a globally dispersed organization | Available for Technology/SaaS, Retail, Healthcare clients |
Modus Create use cases
Short answer: Modus Create is best suited for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery..
| Use case | Industries | Approach |
|---|---|---|
| Running an AI Data Foundation assessment before committing to a full model-development engagement | Technology/SaaS, Retail | Python, AWS |
| Building an AI strategy roadmap for an organization new to machine learning adoption | Technology/SaaS, Retail | Python, AWS |
| Combining broader remote product engineering with an embedded ML workstream | Technology/SaaS, Retail | Python, AWS |
| Engaging a distributed team for a globally dispersed organization | Technology/SaaS, Retail | Python, AWS |
Modus Create pricing
Short answer: Modus Create uses a not published; project and dedicated team pricing approach. Minimum engagement starts at Not published.
| Engagement model | Typical range | Best for |
|---|---|---|
| Fixed project | From Not published | Well-defined scope |
| Dedicated team | Variable; depends on team size | Large programmes or team augmentation |
| Assessment/audit engagement | Variable; depends on team size | Large programmes or team augmentation |
Modus Create does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
Modus Create pros and cons
| Advantages | Things to consider |
|---|---|
| +Structured AI Data Foundation assessment reduces risk of building models on ungoverned or unreliable data. | -AI/ML is one of several product engineering service lines rather than the company's sole specialization. |
| +Fully remote, globally distributed team (55+ countries) offers broad timezone coverage. | -No clearly located aggregate Clutch/G2 star rating in available public sources. |
| +Nine consecutive years on the Inc. 5000 list signals sustained growth. | -Pricing model and minimum engagement are not published. |
| +Technology partnerships with Atlassian, GitHub, and AWS support integrated delivery tooling. | -Fully remote delivery model may not suit buyers who prefer localized or on-site teams. |
Modus Create vs alternatives
How Modus Create compares to the other top ML Model Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market fintech, supply chain, and SaaS companies that... | Combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in. | 4.8 | Full comparison |
| Neurons Lab | Financial services firms wanting a boutique, engineering-led partner... | End-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services. | 4.6 | Full comparison |
| DataRoot Labs | Startups and mid-market companies wanting a senior, AI-only... | Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University). | 4.6 | Full comparison |
| Miquido | Companies that need ML/computer-vision capability bundled with broader... | Combines a large, review-verified product engineering practice with a dedicated AI/ML/CV specialization, useful for teams needing both app and model work from one vendor. | 4.6 | Full comparison |
| Provectus | Mid-market companies that need cloud data infrastructure and... | Grew out of cloud and big-data engineering roots, giving it particular strength in the data infrastructure layer underneath ML models, not just the models themselves. | 4.5 | Full comparison |
| Neoteric | Organizations wanting a structured feasibility/strategy phase before committing... | Two-decade operating history combined with a formal upfront feasibility-assessment stage before any model-building work begins. | 4.5 | Full comparison |
| Addepto | Cost-conscious teams that specifically need MLOps consulting or... | Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option. | 4.4 | Full comparison |
| N-iX | Enterprise buyers wanting a large, heavily certified engineering... | Broadest cloud certification footprint in this comparison (350+ across five major platforms), backed by a 200+ person dedicated data practice. | 4.4 | Full comparison |
| InData Labs | Companies needing a focused predictive-analytics or computer-vision model... | Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims. | 4.3 | Full comparison |
| MobiDev | Small and mid-sized companies wanting a dedicated ML/data-science... | Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model. | 4.3 | Full comparison |
| Sciforce | Companies needing a research-oriented boutique for NLP, digital... | R&D-first culture with named specializations in digital signal processing and NLP that are less commonly offered as distinct practice areas by peers. | 4.2 | Full comparison |
| Sigmoid | Enterprises whose primary bottleneck is data infrastructure and... | Data-engineering-first approach with 950+ multi-cloud certified engineers, positioning it as an infrastructure specialist that also delivers ML rather than the reverse. | 4.2 | Full comparison |
| Tredence | Enterprises needing vertical-specific analytics and ML applied to... | Venture-backed growth trajectory ($205M raised) with named specialization in supply chain and customer analytics rather than generic horizontal AI consulting. | 4.2 | Full comparison |
| Quantiphi | Enterprises standardized on AWS wanting a partner with... | Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status. | 4.2 | Full comparison |
| Sigma Software Group | Companies wanting a large, diversified engineering group with... | Snowflake AI Data Cloud partnership combined with unusually broad industry diversification (AdTech through aviation to gaming). | 4.1 | Full comparison |
| Intellectsoft | Companies wanting an enterprise-name client roster and a... | Unusually strong roster of large, publicly named enterprise clients (EY, Qualcomm, London Stock Exchange) for a company of its relatively modest team size. | 4.1 | Full comparison |
| ELEKS | Enterprises wanting a long-established European software engineering partner... | One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades. | 4.1 | Full comparison |
| Fractal Analytics | Large enterprises wanting a scaled analytics and AI... | Maintains a dedicated internal foundational AI research team alongside client delivery work, and is now a publicly listed company (NSE/BSE) rather than privately held like most peers of similar size. | 4.1 | Full comparison |
| Xebia | Enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has... | Quarter-century software craftsmanship and technical training heritage now applied specifically to production AI/ML delivery rather than AI strategy alone. | 4.0 | Full comparison |
| Grid Dynamics | Fortune 1000 companies wanting the financial transparency and... | The only publicly traded company (NASDAQ: GDYN) in this comparison among the mid-to-large tier, giving buyers audited financial transparency unavailable from private peers. | 4.0 | Full comparison |
| Iterate.ai | Data-sensitive enterprises (e.g., regulated industries) that require AI... | Purpose-built for on-premise/private-infrastructure AI deployment, so client data and proprietary code never leave the client's own environment. | 4.0 | Full comparison |
| Aptus Data Labs | Companies wanting a boutique, India-based data engineering and... | Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team. | 4.0 | Full comparison |
| SoftServe | Enterprises needing edge computer vision or asset-monitoring ML... | Only company in this list simultaneously holding AWS Premier, Google Cloud AI/ML Specialization, and NVIDIA Elite Consulting Partner status, reflecting particular strength in edge and GPU-accelerated computer vision. | 4.0 | Full comparison |
| DataRobot | Enterprises that want to standardize on a single... | The only platform-first vendor in this comparison, meaning model development work happens on and around DataRobot's own automated ML software rather than being platform-agnostic. | 3.9 | Full comparison |
| Persistent Systems | Mid-market and enterprise buyers wanting a publicly traded,... | Purpose-built DxH accelerator suite for MLOps and bias detection, plus a specific Everest Group Leader ranking in the mid-market Data & AI segment rather than only the largest enterprise tier. | 3.9 | Full comparison |
| EPAM Systems | Very large enterprises wanting a publicly traded, AWS... | Proprietary EPAM DIAL platform for enterprise AI orchestration, combined with the 2025 AWS Global Innovation Partner of the Year distinction, an award-level differentiator not held by most peers. | 3.9 | Full comparison |
| Globant | Large enterprises wanting industry-specific pre-packaged AI solutions ("AI... | Only company in this list organized around a formal "studio + AI Pods" delivery model, and the only one with an IDC MarketScape Worldwide Leader in AI Services designation. | 3.9 | Full comparison |
| LTIMindtree | Large enterprises, particularly in BFSI and technology/media sectors,... | Explicit ModelOps templates and model-governance/responsible-AI tooling as named, productized capabilities rather than only bespoke consulting delivery, backed by an IBM watsonx Center of Excellence. | 3.9 | Full comparison |
| Cognizant | Large enterprises, especially in healthcare, wanting a very... | Dedicated, named MLOps platform specifically built for healthcare, combined with one of the largest disclosed data/AI consultant headcounts (23,000+) in this comparison. | 3.9 | Full comparison |
| HCLTech | Very large enterprises wanting a full-stack AI vendor... | Unusually broad "chip-to-cloud" AI stack claim backed by two named proprietary platforms (Graviton for ML development, AION for AI lifecycle management), a combination not matched by most peers in this list. | 3.9 | Full comparison |
| Infosys | Very large global enterprises wanting a substantial library... | Largest disclosed library of reusable, pre-trained AI assets in this comparison (12,000+ assets, 150+ pre-trained models), positioned to accelerate delivery versus fully bespoke builds. | 3.9 | Full comparison |
| Accenture | The largest global enterprises needing AI model development... | By far the largest scale of any company in this comparison (approximately 779,000 employees, $69.67B FY2025 revenue), trading breadth and compliance maturity for less niche, hands-on model-engineering depth than boutique specialists. | 3.9 | Full comparison |
| Devbridge (a Cognizant company) | Clients who want Devbridge's original product-engineering delivery model... | The clearest ownership-change disclosure in this comparison: a formerly independent boutique now operating explicitly as a Cognizant subsidiary, combining boutique delivery heritage with large-parent-company backing. | 3.8 | Full comparison |
Modus Create FAQ
What is Modus Create?
Modus Create is a fully remote, distributed product engineering company founded in 2011 and headquartered in Reston, Virginia, with team members spread across more than 55 countries. The company's AI/ML and data engineering practice includes AI Strategy Roadmap assessments and AI Data Foundation assessments intended to ensure underlying data is reliable and properly governed before or alongside model development work. Modus Create has partnered with technology providers including Atlassian, GitHub, and AWS, and has been recognized on the Inc. 5000 list for nine consecutive years.
How much does Modus Create charge?
Modus Create uses not published; project and dedicated team pricing. Minimum engagement starts at Not published. A discovery call is required to get project-specific quotes.
What tech stack does Modus Create use?
Modus Create works with Python, AWS, Data governance tooling, Cloud ML platforms. Primary industries served include Technology/SaaS, Retail, Healthcare.
Is Modus Create right for enterprise?
Distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery.. 501–1,000 team size. Key consideration: AI/ML is one of several product engineering service lines rather than the company's sole specialization..
What are the best Modus Create alternatives?
The best alternatives to Modus Create depend on your use case. Top options are:
- Tensorway: combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in.
- Neurons Lab: end-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services.
- DataRoot Labs: has never diversified beyond ai/ml services, and backs its delivery bench with an in-house ml training program (dataroot university).
Compare Modus Create with other ML Model Development companies
Modus Create vs Tensorway Modus Create vs Neurons Lab Modus Create vs DataRoot Labs Modus Create vs Miquido Modus Create vs Provectus Modus Create vs Neoteric Modus Create vs Addepto Modus Create vs N-iX Modus Create vs InData Labs Modus Create vs MobiDev Modus Create vs Sciforce Modus Create vs Sigmoid Modus Create vs Tredence Modus Create vs Quantiphi Modus Create vs Sigma Software Group Modus Create vs Intellectsoft Modus Create vs ELEKS Modus Create vs Fractal Analytics Modus Create vs Xebia Modus Create vs Grid Dynamics Modus Create vs Iterate.ai Modus Create vs Aptus Data Labs Modus Create vs SoftServe Modus Create vs DataRobot Modus Create vs Persistent Systems Modus Create vs EPAM Systems Modus Create vs Globant Modus Create vs LTIMindtree Modus Create vs Cognizant Modus Create vs HCLTech Modus Create vs Infosys Modus Create vs Accenture Modus Create vs Devbridge (a Cognizant company)
Last reviewed: July 2026. Verify all details directly with Modus Create before making a decision.