A Deep Dive Into Machine Learning Consulting: Case Studies and FAQs


Machine learning consulting services are becoming increasingly popular as organizations across industries seek to innovate their operations with the help of artificial intelligence. But the field is still relatively new, and many stakeholders are unsure whether and how, exactly, ML consulting could address their specific business needs—and apprehensive about the cost of implementing AI solutions.

In this blog post, we tackle these doubts by explaining the most popular machine learning consulting services, answering frequently asked questions, and going over real-world case studies that illustrate the value of ML consulting across a variety of industries.

If you're excited about the idea of leveraging AI to give your business a competitive edge, read on to learn more about what machine learning consulting can do for you.

What is machine learning?

Machine learning is a category of artificial intelligence that involves developing machines (e.g., computers or software) that can “learn” from data by using artificial neural networks that mimic the structure and workings of the human brain. 

Machine learning algorithms can recognize patterns in datasets (consisting of text, images, video footage, and sound) and form predictions or decisions based on that information. Common uses of machine learning include predictive analytics, natural language processing (NLP), and computer vision

Unlike traditional systems that only do what they are specifically programmed to, ML algorithms can produce an infinite number of outcomes with minimal human intervention.

What is machine learning consulting?

Machine learning consulting is a service that helps businesses automate processes, improve work efficiency and accuracy, and better address their core business needs using bespoke ML solutions. It involves specialized teams of data scientists, software development engineers, and AI experts working with internal stakeholders to develop and implement ML tools aligned with the client’s budget and objectives. 

Depending on the client’s needs, the machine learning consulting process can also include:

  • Auditing business needs and advising the client on which challenges are a good fit for ML solutions
  • Building a proof of content (PoC) that uses ML and data science to solve an existing business challenge
  • Productionizing ML systems and helping the client optimize existing ML technologies
  • Guidance on data collection, data management, and data preparation to ensure that the implemented machine learning models have the input necessary to function
  • Training in-house tech teams on using the newly implemented AI and machine learning technologies
  • Advising and supporting on hiring strategy or staff augmentation to build out in-house teams

The ultimate goal of machine learning consulting is to help clients improve their business operations and create new opportunities for growth and innovation.

Understanding the different types of ML consulting services

On a high level, ML consulting services can be broken down into two categories: staff augmentation and managed projects. 

Staff augmentation

Staff augmentation projects rely on ML consulting firms working with companies to place highly-specialized experts (commonly machine learning engineers or data scientists) directly on their team in order to bring in additional expertise, increase the team’s capacity, or meet specific project requirements or deadlines. 

This type of ML consulting is a good fit for: 

  • Organizations that have existing technical teams in need of temporary support to expand their skillset and supercharge a specific machine learning project
  • Clients who need a machine learning expert in a part-time advisory capacity 
  • Companies that need to get started on ML projects fast, using the contract-to-hire model to avoid lengthy and expensive hiring processes

At Tribe AI, staff augmentation projects typically involve the following steps:

  1. A 30-minute discovery phone call, during which consultants assess the client’s technical and staffing needs based on current business processes and goals. 
  2. Curating a staffing list. The team at Tribe AI put together a custom talent list based on the necessary experience, areas of expertise, and timelines. This is followed by a detailed walkthrough of the project roadmap. If necessary, we coordinate interviews with the project candidates. On average, this step takes 3 days
  3. Getting started. Most of our talent is available to jump on projects immediately upon approval. The average time from kickoff to staffing is 10 days.

Managed projects

During managed projects, the ML consulting firm handles the whole project from start to finish. This could include building a custom machine learning model from scratch or optimizing an existing model for better results. 

Managed ML projects vary in scope and budget—they can benefit startups, Fortune 500 companies, and anyone in between. 

This type of consulting project is a good fit for:

  • Companies that face a specific business challenge or are unsure how to get value from the data they have access to, and want to build a PoC to test a hypothesis about how ML could address their specific needs
  • Organizations that already use AI/ML solutions that need to be optimized for scale, speed, or ROI
  • Businesses looking to automate anything from daily operations to strategic data-informed decision-making processes

At Tribe AI, managed ML projects typically involve the following steps:

  1. Discovery. Our consultants work with your internal team to understand your business, complete with its goals and challenges. Then, we start mapping out recommended solutions, scoping the project, and assembling a dedicated team of experts.
  2. Development. The team works in sprints to develop a proof of concept for a custom ML solution. The PoC model helps both the client and the dedicated team understand where in the business ML and data science can drive the most value. Throughout this process, we schedule regular check-ins between a dedicated product manager, your staff, and other stakeholders. 
  3. Launch and testing. Once a PoC is finalized, we work with the client to launch it in a live business environment. We then educate stakeholders on how to continuously drive value from the solution and make our team available for regular maintenance work. 

After the work is done, we can continue the partnership through monitoring and maintenance services to make sure our clients are set up for success. 

The benefits of working with machine learning consultants: 7 case studies

1. An insurance company optimized pricing

With the help of Tribe AI, a leading insurance MGA successfully implemented a custom ML-powered pricing algorithm. The technology optimized premiums based on available customer data, resulting in a 12% premium lift across policies. The company saw an ROI in the first week of testing the algorithm. Read the full case study.

Source: Tribe insurance case study

2. A private equity firm built a smart investment model

Tribe AI helped a leading PE firm solve a specific challenge: driving unique insights into one particular vertical of interest using only publicly available data.

In six weeks, a team of experts built an ML-powered toolkit that now helps the client:

  • Automatically extract deep market insights from publicly available data
  • Evaluate potential investments at increased speed and confidence ‍
  • Offer a new value prop of proprietary data assets to prospective portfolio companies
  • Build a uniquely data-informed investment framework
  • Win more competitive deals

Read the full case study.

3. A compliance startup reduced training time and increased accuracy

A software leader that provides end-to-end compliance and audit management for modern companies worked with Tribe AI’s experts to optimize an existing AI-driven security product. 

In just 2 months, the team increased the accuracy of the company’s existing AI solution by 15%—which, in turn, improved the audit team’s workflows, with overall team efficiency increasing by 55%.

Read the full case study.

4. Wingspan built a complete AI roadmap 

Wingspan is a cloud-based financial platform for freelancers. Knowing that AI would give them a competitive edge (saving their users time and money), the company teamed up with Tribe AI for a two-tier project.

In the first phase, the Tribe team built a proof of concept predictive model that used ML to classify users’ expenses. The model was completed in just 8 weeks, and became the starting point of part two: building a comprehensive, multi-year AI roadmap for implementing artificial intelligence technologies across the Wingspan platform. Read the full case study.

Source: Tribe Wingspan case study

5. Kettle built a model that predicts predict the occurrence, spread, and severity of wildfires

Kettle operates in the $10 billion wildfire reinsurance market. With climate change affecting wildfire patterns and severity, existing pricing models based on historical data have become obsolete, making it extremely difficult for reinsurers to underwrite the risk. As a result, most reinsurers have increased either premiums astronomically or attempted to back out of markets altogether, creating coverage vacuums. 

Kettle teamed up with Tribe AI to build a proprietary ML-powered model that accurately predicts the occurrence, spread, and severity of wildfires. The model ensures adequate policy pricing in at-risk areas and helps both insurance providers and homeowners better balance risk in a changing climate. Read the full case study.

Source: Tribe Kettle case study

6. Togal.AI built the world’s fastest estimation software for construction

Togal.AI partnered with Tribe to build an AI-powered construction estimation software that automatically and accurately detects, labels, and measures project spaces, effectively cutting construction project takeoff times down from weeks to minutes.

The custom solution uses machine learning to perform otherwise complicated and time-consuming manual work—but the advanced technology is packaged into a user-friendly interface that doesn’t create more work (learning a complicated software) for construction professionals. Read full case study.

7. Fantasmo built real-time 3D maps to track the location of e-scooters

Fantasmo (acquired by Tier) worked with Tribe AI to embed an advanced computer vision-powered system in e-scooters—and create a best-in-class connection-less mapping and positioning technology. 

On top of helping Fantasmo adapt an existing ML model to the target architecture (scooters), the Tribe team’s work reduced the solution’s compute costs by 72%. Read the full case study.

Source: Tribe Fantasmo case study

How to choose the best machine learning consulting firm for your needs

When looking for the right partner for your ML project, make sure to closely consider the following factors. 


Before you commit to a team, check the credentials of the ML experts and data scientists recommended by the consulting firm. Look for proof of expertise in data science, artificial intelligence, and process automation. 

What’s the educational background of these experts? What’s their track record of successful projects? Have they worked with reputable businesses? Ask all of these questions during your discovery call. 


Checking in on a consulting firm’s reputation will help you understand how long they’ve been around, whether clients are happy with their services, and how their services impact the ROI of the organizations they work with. Before booking a discovery call, make sure to look through online reviews and case studies. 


Getting access to hard-to-hire experts can take a lot of time, even with the help of an AI consulting firm. When looking for the right fit, make sure to ask about average project timelines. 


Your needs are unique—you may need access to AI and ML experts for weeks, months, or full-time. Make sure that the consulting company you go with is flexible with their services.


Don’t be afraid to shop around. If you have a strong understanding of your needs and a defined project scope, consider sending out an RFP (request for proposal) to a few shortlisted consulting firms—then, compare their prices.

On top of understanding the total cost of your ML project, look into how each firm goes about billing. At Tribe AI, for example, projects are billed on a per-sprint basis for flexibility, transparency, and lower commitment.

Remember: Even if hiring ML consultants may seem like a hefty investment, implementing AI solutions to optimize your processes can help drive better business results and higher revenue for years to come. 


Ask questions! Before you commit to a team, make sure you understand who will be working on your project. Also, make sure to ask whether your project is complex enough to require a dedicated product manager. Get an understanding of how often you will chat to track progress and how the team handles urgent support requests.

Innovate and grow your business with machine learning consulting 

Machine learning is helping businesses across industries save time, cut costs, and embrace new opportunities for growth and innovation.

Don’t wait—the market rewards early adopters. Innovating your business with machine learning is not just profitable, it will also help you become a leader in your niche.

Tribe.ai is an expert machine learning consulting company that connects organizations with experienced ML consultants and data science experts. Contact us to learn more about our services and get started with your ML project.

FAQs about machine learning consulting

What does a machine learning consultant do?

A machine learning consultant is a professional who helps businesses design and implement machine learning solutions that address specific business problems or streamline workflows. 

How long does a typical machine learning consulting project take?

Depending on the complexity and scope of the project, deploying an ML model can take anywhere from a couple of weeks to over a year. Most ML consulting projects involve four phases: discovery, design, implementation, and testing and refinement. At the beginning of every project, consulting firms outline the expected timelines for each phase.  

What type of companies typically use machine learning consultancy services?

Businesses in all industries can benefit from machine learning consulting. Any company that collects and processes large amounts of data should consider implementing machine learning models to save time and improve the accuracy and security of their work.

Industries that are currently spearheading the implementation of ML and AI solutions include healthcare, construction, and insurance

How much will it cost me to hire a machine learning consultant?

The pricing of any machine learning consulting project will depend on the complexity and scope of work and the experience and expertise of the consultants. Contact us to book a commitment-free consultation and discuss your machine learning needs.

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