Companies are spending more on machine learning every year, but the results are still patchy. In a Gartner survey of 782 infrastructure and operations leaders published in April 2026, only 28% said their AI use cases fully succeeded and met ROI expectations.
When a project stalls, the model itself is rarely the problem. More often the data was never cleaned up properly, or no one was assigned to monitor the model once it went live.
That's where machine learning consulting companies earn their fee. A good partner helps you choose a use case with a clear payoff, then keeps the model accurate after launch.
We put this guide together for technology leaders and founders who are comparing ML consulting partners for 2027. It covers 10 top machine learning consulting companies, from offshore engineering teams to global consultancies. For each firm you'll see its machine learning consulting services, and best-fit use cases.
Top Machine Learning Consulting Companies at a Glance
The detailed profiles further down explain each firm's ML consulting services and where the evidence for each claim comes from.
The 10 Best Machine Learning Consulting Firms for 2027
Here’s a closer look at each firm. You’ll see what they offer, what they’ve built and who they suit best.
1. Your Team in India
Overview: Your Team in India has been building offshore engineering teams since 2009 and is powered by Signity Solutions. The company sets up dedicated ML engineers who work as part of the client’s team. Clients decide what the engineers work on each day, while the team handles hiring, HR and infrastructure. This makes it a practical choice among machine learning consulting companies in India for businesses that want long-term ML capacity.
ML consulting services:
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AI strategy and consulting, including use case selection
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Custom machine learning models for predictive analytics and forecasting
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NLP, including multilingual use cases
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Computer vision
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Generative AI and AI agent development
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MLOps and model deployment
Technical capabilities:
The company’s engineers build with TensorFlow, PyTorch, scikit-learn, XGBoost and Hugging Face. For deployment and MLOps, they use AWS SageMaker, Google Vertex AI, Azure AI, MLflow and Kubeflow. Retrieval projects run on vector databases like Pinecone, Weaviate and Qdrant.
Verified project examples:
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Plus Covr (insurance): A six-person AI team raised claims document extraction accuracy from 61% to 94%.
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GETTRX (fintech): The team helped cut fraud risk while keeping the platform PCI-DSS compliant.
How engagement works: Clients can choose dedicated (monthly), hourly or fixed-cost billing. Only 3% of applicants pass the company’s AI technical assessment. Engineers can start within 48 hours, and each engagement begins with a 7-day free trial. The company’s website shows a 4.9/5 Clutch rating.
Best fit: Startups and mid-size companies that need enterprise machine learning consulting skills at offshore cost and plan to keep the same team as the product grows.
2. ScienceSoft
Overview: It is one of the longest-running ML consulting companies on this list. It has delivered projects in more than 30 industries, with deep experience in healthcare, banking and manufacturing.
ML consulting services:
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Business analysis to define what ML should solve
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Technical design and data preparation
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Model development and implementation
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Reporting and model support after launch
You can hire ScienceSoft for consulting only, for full implementation, or to support models you already run.
Technical capabilities:
The team works in Python, R and Scala, using TensorFlow, scikit-learn, Keras and spaCy. For cloud ML it relies on Amazon SageMaker and Azure Machine Learning. ScienceSoft also holds ISO 9001 and ISO 27001 certifications, which matters when your data is sensitive.
Best fit:
Regulated mid-size and large companies that want predictive analytics or computer vision delivered through a documented, security-certified process.
3. DATAFOREST
Overview. DATAFOREST works where data engineering meets machine learning. Its Clutch profile shows a 5.0 average across 29 reviews.
ML consulting services
Data science as a service, predictive analytics, computer vision, NLP, recommendation systems and AI integration. Recommendation systems make up 70% of its AI focus on Clutch.
Technical capabilities
Snowflake data lakes, real-time ETL pipelines, AWS (it is an AWS partner) and Google Cloud Speech.
Best fit
Mid-size e-commerce and financial services firms where the hardest part of the ML project is getting the data ready.
4. Azumo
Overview. Azumo is a nearshore AI and software development company, headquartered in San Francisco, with engineers across Latin America.
ML consulting services
Model selection, data labeling, model training and optimization, production deployment, and ongoing performance monitoring.
Technical capabilities
Python-based ML stacks, large language models and generative AI frameworks. Azumo is SOC 2 certified.
Best fit
US companies that want ML engineers working in or close to their own time zone.
5. Forte Group
Overview. Forte Group is a Chicago-based product engineering firm with a strong quality engineering background and a dedicated Chief Quality Officer.
ML consulting services
End-to-end model development, MLOps pipeline automation, ML infrastructure and platform engineering, AI strategy and governance.
Technical capabilities
Deep learning, NLP, computer vision and reinforcement learning. CI/CD for models, automated training, validation and monitoring. It is a Databricks solutions partner.
Best fit
Companies with a model that works in a notebook and needs reliable enterprise MLOps to reach production.
Recommended Read: Top Companies To Hire Dedicated Developers Globally
6. IBM Consulting
Overview. IBM Consulting runs a global AI and automation practice that IBM said in 2023 included 21,000 data and AI consultants with over 40,000 enterprise client engagements.
ML consulting services
Use case prioritization, model selection and fine-tuning, AI governance, and a dedicated practice for IBM's watsonx platform. It also works with AWS, Microsoft, Salesforce and SAP technology.
Technical capabilities
Watsonx.ai, watsonx.data and watsonx.governance, plus more than 50 domain-specific classical machine learning accelerators. IBM was named a Leader in the IDC MarketScape for Worldwide AI Services in 2023.
Best fit
Large enterprises running hybrid cloud, especially those that need strong AI governance across many business units.
7. Deloitte
Overview. Deloitte's AI & Insights practice designs, builds and scales AI solutions powered by agentic AI, machine learning and edge AI. Deloitte says it was named a Leader in the IDC MarketScape: Worldwide AI Services 2025 assessment.
ML consulting services
AI strategy, model build and deployment, change management, governance and managed services. Its ReadyAI offer provides AI and ML practitioners on demand who work as an extension of client teams.
Technical capabilities
The SmartApps framework covers suitability analysis, proof of concept, model and app building, and MLOps setup, along with a Trustworthy AI approach to safety and compliance.
Best fit
Fortune 1000 programs where enterprise machine learning consulting has to come with change management.
One caution: independence rules may limit which services Deloitte can offer to its audit clients.
8. Booz Allen Hamilton
Overview. Booz Allen is the largest provider of AI services to the US federal government, based on Deltek GovWin's analysis of prime contract obligations from fiscal years 2021 to 2023. Its AI network includes more than 2,200 practitioners, about 200 of them with PhDs.
ML consulting services
AI strategy, AI-ready data and DataOps, AI engineering, responsible and explainable AI, and AI risk management.
Technical capabilities
The aiSSEMBLE AI factory for federal environments, plus purpose-built stacks for computer vision, cyber AI and responsible AI. Model development covers LLMs, computer vision, MLOps and edge AI.
Best fit
Federal agencies, defense programs and contractors that need cleared teams and high-security deployment.
9. H2O.ai
Overview. H2O.ai is a platform company that pairs its AI software with enterprise support and professional services. About 40% of its revenue came from financial services, according to Sacra's research.
ML consulting services
Enterprise support and co-built solutions around its products, including training, dedicated account managers and deployment help. An AWS Marketplace listing bundles its enterprise platform with professional services.
Technical capabilities
Open-source H2O-3, H2O Driverless AI for automated machine learning with feature engineering and explainability, and h2oGPTe for generative AI. It supports air-gapped, on-premises and cloud VPC deployments.
Best fit
Banks, insurers and telecom firms with in-house data scientists who want AutoML and vendor support while keeping models in their own environment.
10. SAS
Overview. SAS is an analytics software company with a professional services arm. It says 90% of Fortune 100 companies or their affiliates are SAS customers.
ML consulting services
SAS Global Consulting offers advisory, enablement and implementation services around the SAS Viya platform, including deep experience in regulated public sector work.
Technical capabilities
SAS Viya for data management, model development and deployment, with visual and code interfaces. SAS Viya Workbench lets developers build models in SAS, Python or R.
Best fit
Banks, insurers and government bodies that already run SAS and want predictive analytics and fraud models built on that investment.
How to Choose a Machine Learning Consulting Company
Start with the business problem. A churn model, a product recommendation engine and a defect-detection camera need very different skills, and a partner strong in one may be average in another.
1. Match the use case to proven work: Ask for case studies in your exact area. Predictive analytics, recommendation systems, computer vision and NLP each have their own data and evaluation needs.
2. Check data engineering depth: Gartner expects organizations to abandon 60% of AI projects that lack AI-ready data. If your data is scattered, pick machine learning consultants who will fix the pipelines first.
3. Look hard at MLOps: Ask how they monitor drift, retrain models and roll back a bad release. A model with no owner after launch slowly gets worse.
4. Decide on the engagement model: A fixed-scope project suits a well-defined proof of concept. Ongoing products usually do better with a dedicated team that keeps context over time.
5. Verify the claims yourself: Read reviews on Clutch or G2, call two references, and run a short paid pilot before you commit to a long contract.
If cost and long-term capacity both matter, machine learning consulting companies in India are worth a serious look. You can build a full ML team for less than one senior hire costs in many US cities, provided the partner gives you direct access to the engineers.
What Affects the Cost of Machine Learning Consulting Services
Most ML consulting companies don’t list their prices online. Your final cost depends on a few things.
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Your data. Messy data needs cleaning and labeling first. This can take longer than building the model.
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The type of model. A simple sales forecast costs much less than a computer vision system that needs thousands of labeled images.
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How you hire. Fixed-price projects often cost more, because the vendor takes on the risk. A dedicated team billed monthly spreads the cost out.
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Where the team is. Rates vary a lot between the US, Europe, Latin America and India. This is a big reason many companies look at machine learning consulting companies in India.
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Rules you must follow. HIPAA, PCI-DSS or SOC 2 requirements add extra security and review work.
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Support after launch. Models need monitoring and retraining, and cloud costs keep running. Plan for these from day one.
Questions to Ask Before You Hire ML Consultants
A short call can tell you a lot about a partner. Ask these questions before you sign anything.
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Have you built something like this before? Ask for a past project close to yours, and ask to speak with that client.
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Who will actually work on our project? Get names and their ML experience. The people on the sales call aren’t always the ones who do the work.
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How will we measure success? Accuracy alone isn’t enough. Agree on a business number, like fewer fraud cases or faster claims.
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Who owns the code, data and models? Make sure everything stays with you if the contract ends.
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What happens after launch? Ask howl they track model performance and how often they retrain.
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Can we start with a small pilot? Two to four weeks is usually enough to test the fit.
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Where would you not use ML? A good partner will tell you when a simpler fix works better.
Build Your ML Team With Your Team in India
The right machine learning consulting company depends on your project and your budget. Big consultancies suit large, multi-year programs. Specialist firms and offshore teams work well when you want hands-on engineers and a faster start.
If you’re planning an ML project for 2027, like a demand forecast or a computer vision system, Your Team in India can help. We’ll set up a dedicated team of vetted ML engineers who work as part of your company. Start with a 7-day free trial and grow the team as your project grows.
Book a free consultation to talk through your use case with our AI and machine learning consulting experts.
Frequently Asked Questions
It helps you find ML use cases worth funding and prepares your data. It then builds and deploys the models and keeps them accurate after launch. Some firms only advise. Others, like Your Team in India, also supply the engineers who do the build.
It depends on your situation. For dedicated offshore ML teams, Your Team in India is a strong option. ScienceSoft and DATAFOREST suit specialist project work, and Azumo suits nearshore needs. IBM Consulting and Deloitte lead large enterprise programs, and Booz Allen leads US federal AI work.
They can be, if the partner shows production ML case studies, offers MLOps support and gives you direct access to the engineers. India also has one of the world's larger pools of ML talent, so scaling a team is usually faster.
A focused proof of concept often runs a few weeks to a few months. Getting to stable production takes longer, mostly because of data preparation, integration and monitoring setup. Ask any partner for a phased plan with clear exit points.
AI consulting is the broader term. It covers generative AI, automation and AI strategy. Machine learning consulting focuses on models that learn from your data, such as forecasting, recommendation systems, computer vision and NLP.
A clear business goal, a rough idea of what data you have and where it lives, and the metric you'll use to judge success. That alone makes the first conversation far more useful.
By Mangesh Gothankar
By Ashwani Sharma
By Achin Verma
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4.8 out of 5 by
