Tredence
VC-backed analytics and AI consulting company founded in 2013, headquartered in San Jose.
Founded 2013 | San Jose, USA | 1,001–5,000 employees | Last updated: July 2026
custom-model-trainingdata-engineering-mlmlops-pipelineml-consulting
What is Tredence?
Tredence is a data science and analytics consultancy founded in 2013 by Sumit Mehra, Shub Bhowmick, and Shashank Dubey, headquartered in San Jose, California, with additional offices in Chicago, Riyadh, London, Toronto, and Bengaluru. The company has raised a reported $205 million in Series B funding and reports more than 4,200 employees globally. Its practice spans AI consulting, supply chain analytics, and customer analytics, applying machine learning models to specific vertical business problems at enterprise scale.
Tredence was founded in 2013 and is headquartered in San Jose, USA. The firm employs 1,001–5,000 people and works primarily with clients in Retail/CPG, Supply chain, Financial services sectors. Its primary differentiator is: Venture-backed growth trajectory ($205M raised) with named specialization in supply chain and customer analytics rather than generic horizontal AI consulting..
Tredence tech stack and services
PythonCloud ML platforms (AWS/Azure/GCP)Data warehouse/pipeline tooling
| Service area | Details |
|---|---|
| Building demand forecasting or inventory optimization models for supply chain operations | Available for Retail/CPG, Supply chain, Financial services clients |
| Developing customer analytics and personalization models for retail or CPG brands | Available for Retail/CPG, Supply chain, Financial services clients |
| Running a large, multi-market enterprise AI consulting engagement | Available for Retail/CPG, Supply chain, Financial services clients |
| Combining data engineering and applied ML within one vendor for a Series B-funded scale-up client | Available for Retail/CPG, Supply chain, Financial services clients |
Tredence use cases
Short answer: Tredence is best suited for enterprises needing vertical-specific analytics and ML applied to supply chain or customer-analytics problems at scale..
| Use case | Industries | Approach |
|---|---|---|
| Building demand forecasting or inventory optimization models for supply chain operations | Retail/CPG, Supply chain | Python, Cloud ML platforms (AWS/Azure/GCP) |
| Developing customer analytics and personalization models for retail or CPG brands | Retail/CPG, Supply chain | Python, Cloud ML platforms (AWS/Azure/GCP) |
| Running a large, multi-market enterprise AI consulting engagement | Retail/CPG, Supply chain | Python, Cloud ML platforms (AWS/Azure/GCP) |
| Combining data engineering and applied ML within one vendor for a Series B-funded scale-up client | Retail/CPG, Supply chain | Python, Cloud ML platforms (AWS/Azure/GCP) |
Tredence pricing
Short answer: Tredence uses a not published; enterprise project engagements pricing approach. Minimum engagement starts at Not published.
| Engagement model | Typical range | Best for |
|---|---|---|
| Enterprise project engagement | Variable; depends on team size | Large programmes or team augmentation |
| Dedicated team | Variable; depends on team size | Large programmes or team augmentation |
Tredence does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
Tredence pros and cons
| Advantages | Things to consider |
|---|---|
| +Significant venture funding ($205M) provides financial stability and growth investment relative to bootstrapped peers. | -No clearly published aggregate Clutch/G2 rating found in available sources for this research pass. |
| +Vertical specialization in supply chain and customer analytics offers concrete domain expertise. | -Enterprise-scale focus may be less accessible or cost-effective for small or early-stage buyers. |
| +Global office footprint (US, Middle East, UK, Canada, India) supports multi-region enterprise clients. | -Pricing model and minimum engagement size are not published. |
| +Over 4,200 employees provides substantial delivery capacity for large programs. | -Named, quantified public case studies with client outcomes are limited in available search results. |
Tredence vs alternatives
How Tredence 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 |
| 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 |
| Modus Create | Distributed organizations wanting a remote-first partner that pairs... | Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development. | 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 |
Tredence FAQ
What is Tredence?
Tredence is a data science and analytics consultancy founded in 2013 by Sumit Mehra, Shub Bhowmick, and Shashank Dubey, headquartered in San Jose, California, with additional offices in Chicago, Riyadh, London, Toronto, and Bengaluru. The company has raised a reported $205 million in Series B funding and reports more than 4,200 employees globally. Its practice spans AI consulting, supply chain analytics, and customer analytics, applying machine learning models to specific vertical business problems at enterprise scale.
How much does Tredence charge?
Tredence uses not published; enterprise project engagements pricing. Minimum engagement starts at Not published. A discovery call is required to get project-specific quotes.
What tech stack does Tredence use?
Tredence works with Python, Cloud ML platforms (AWS/Azure/GCP), Data warehouse/pipeline tooling. Primary industries served include Retail/CPG, Supply chain, Financial services.
Is Tredence right for enterprise?
Enterprises needing vertical-specific analytics and ML applied to supply chain or customer-analytics problems at scale.. 1,001–5,000 team size. Key consideration: No clearly published aggregate Clutch/G2 rating found in available sources for this research pass..
What are the best Tredence alternatives?
The best alternatives to Tredence 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 Tredence with other ML Model Development companies
Tredence vs Tensorway Tredence vs Neurons Lab Tredence vs DataRoot Labs Tredence vs Miquido Tredence vs Provectus Tredence vs Neoteric Tredence vs Addepto Tredence vs N-iX Tredence vs InData Labs Tredence vs MobiDev Tredence vs Sciforce Tredence vs Sigmoid Tredence vs Quantiphi Tredence vs Sigma Software Group Tredence vs Intellectsoft Tredence vs ELEKS Tredence vs Fractal Analytics Tredence vs Xebia Tredence vs Grid Dynamics Tredence vs Iterate.ai Tredence vs Modus Create Tredence vs Aptus Data Labs Tredence vs SoftServe Tredence vs DataRobot Tredence vs Persistent Systems Tredence vs EPAM Systems Tredence vs Globant Tredence vs LTIMindtree Tredence vs Cognizant Tredence vs HCLTech Tredence vs Infosys Tredence vs Accenture Tredence vs Devbridge (a Cognizant company)
Last reviewed: July 2026. Verify all details directly with Tredence before making a decision.