Top AI Consulting Firms to Partner With in 2026

TL;DR

• This guide compares 11 AI consulting firms by their services, published projects and client feedback, from specialists such as Tensorway and DataRoot Labs to broader software providers such as Itransition.
• Consulting can help you choose a first AI project, assess your data or work out why an existing system is underperforming. The scope should reflect the problem you need help solving.
• Case studies show what a team has built. Client reviews add details about communication, missed deadlines and how easily the client could take over the work.
• Consulting and development may have separate budgets. A firm’s published minimum project size is only a starting point for discussing your scope.
• Agree on the deliverables before signing. You should leave the engagement knowing what to build, what it is likely to cost and who can carry the work forward.

Before investing in AI, you need a clear idea of what it will change in your business. In PwC’s Global CEO Survey, published in January 2026, 56% of respondents said AI had brought their companies neither higher revenue nor lower costs over the previous 12 months.

The advice you get from AI consultants will shape where your money goes. Before development starts, you need to know whether your data is ready and whether the project can realistically pay for itself. Experience with similar projects matters here. A team that has worked through those problems before is better placed to identify technical constraints during the initial assessment.

In this article, we review 11 AI consulting firms, their services and completed projects to help you build a shortlist for your first conversations.

How we chose the AI consulting firms

This list is based on publicly available company information, case studies and profiles on Clutch and G2. We link any reported results to their sources.

We read client reviews for details about the work delivered, budget changes and whether deadlines were met. We also consider when each review was posted and which service it covers. Ratings for a company’s individual products on G2 apply to those products.

We compare firms using the following criteria.

• What the service includes and what the client receives, such as a data readiness assessment or a pilot plan with a cost estimate.
• The team’s role in the projects described and whether those projects reached production. We look for specific constraints the firm worked with, including difficult integrations or data handling requirements.
• Published budgets and timelines, the option to commission a standalone audit or pilot, and handover arrangements.

Details that are not publicly available need to be confirmed with each firm. Tensorway publishes this article and is included under the same criteria.

Comparing AI Consulting Firms

The list includes specialist AI teams and enterprise software developers. Developing a new model and adding AI to an existing ERP system call for different experience. The table outlines each firm’s focus to help you shortlist candidates for your project.

Minimum project budgets come from the companies’ Clutch profiles. They indicate the project size each firm says it is willing to take on. Pricing for a standalone audit or consulting engagement needs to be confirmed directly.

Clutch ratings cover reviews of various services a company provides. Follow the links to see which projects clients reviewed.

Tensorway

Tensorway was founded in 2019. It grew out of Anadea’s machine learning and deep learning practice, which became a separate brand.

Its current technology stack includes TensorFlow and PyTorch for ML development, alongside LangChain and LangGraph for applications built around language models and AI agents. The team works across several industries, with projects in financial services, law, education, real estate and media.

The company offers the following services.

• AI consulting. Choosing a technical approach, assessing data and costs, and planning development and deployment.
• Audits of existing AI systems. Reviewing model errors, processing speed and resource use. Clients receive recommendations their own engineers can implement.
• Custom development. Training models, building AI agents and connecting them to business systems. The team also develops the application and its user interface.

For a Swedish private equity fund, the team built an agent platform that gathers financial data, evaluates companies using the fund’s methodology and prepares presentations. According to the published case study, initial screening took 80% less time, while investment deck preparation became eight times faster.

Synapse Team’s review on Clutch describes the consulting engagement. The client received an AI strategy and a recommended set of tools to improve customer experience, praising the team’s clear explanations and delivery on schedule. They also suggested a follow-up session to review progress once implementation was underway.

A July 2025 review from Stanislav Bondarenko, CRO at Uinno, on DesignRush describes a different working arrangement. Uinno developed the core logistics system and interfaces, while Tensorway handled the AI modules. Stanislav highlighted the developers’ suggestions for preventing conflicts between the new algorithms and existing code.

DataRoot Labs

DataRoot Labs was founded in 2016. Its research team in Kyiv develops language models, AI agents and computer vision systems. If you’re unsure whether your data and hardware can support the system you want to build, the team can start by testing possible approaches.

The company offers three ways to work together.

• Research into a specific problem. Engineers test potential technical approaches. The results help the client decide whether to fund further development.
• Joint product development. DataRoot Labs develops the AI components alongside the client’s team. The work covers prototyping, integration, testing and code handover.
• A dedicated R&D team. For longer projects that need ongoing research and model improvements.

The company’s technology stack includes LangGraph for coordinating agents and TensorRT for running models faster on GPUs. Its robotics work also involves accounting for sensor data and the hardware constraints of the device running the model.

Ekkobar wanted to analyse social media posts using the surrounding conversation and information about the author. DataRoot Labs developed a language model for the task. In his August 2024 review, CEO Rhys Ryan described weekly reports covering completed work, unresolved issues and next steps. He said these updates helped keep the project within budget. At the end of the project, Ekkobar received the code, documentation and instructions for updating the model.

For Art of Peace.ai, the team developed a prototype that detects offensive language and suggests alternative wording. In a November 2023 review, the company’s CEO said it was delivered on time and cost less than expected.

Addepto

Addepto was founded in Warsaw in 2018. It is now part of KMS Technology and provides AI consulting, data engineering and software development services.

Through its AI consulting services, the team identifies tasks suited to automation and tests proposed solutions using the client’s data. It also assesses whether custom development is needed or an existing product could do the job. Further work depends on the project.

• When data is spread across different systems, Addepto sets up data collection and quality checks. Its data work includes Databricks implementations and processing with PySpark.
• Development covers predictive models, computer vision, document processing and applications built around language models.
• Clients with existing models can commission MLOps work, including deployment, monitoring and updates.

Addepto also developed ContextClue. The platform connects information from documents, ERP platforms and CAD systems, allowing users to search it through questions in plain language. It combines language models, semantic search and knowledge graphs.

In her April 2024 review on G2, client Michelle d. described the results of an MLOps engagement. Aligning the development and production environments reduced the time her team spent resolving differences between them. According to her review, deploying models took hours instead of days or weeks.

A film and advertising production company described a different project in its September 2026 review on Clutch. Addepto was developing a system that uses past estimates to suggest budget items and expected costs for new projects. The client said the team took its working processes into account and adjusted the solution in response to feedback. The project was still underway when the review was published.

InData Labs

InData Labs was founded in 2014 by Marat Karpeko, whose background includes big data analytics in the gaming industry. The company develops predictive models, computer vision systems and text processing software. It works in e-commerce, logistics, financial services and healthcare.

The company’s technology stack includes TensorFlow and PyTorch for neural network development. It also uses Core ML and TensorFlow Lite to run models directly on users’ devices.

A pump and motor manufacturer described its forecasting project in a November 2024 review on Clutch. The client said InData Labs met deadlines, stayed within budget and shared knowledge with the internal team. They also saw room to improve planning meetings. Having more proposals to review beforehand would have made those discussions more productive.

The founder of Adptive Products and Solutions described a different engagement in a November 2025 review. InData Labs built a web application that physical therapists use to prescribe home exercises for patients. Development followed two-week sprints, with requirements changing along the way. The client praised the team’s flexibility and timely delivery. Work on the iOS app was still underway when the review was published.

Itransition

Itransition was founded in 1998 as a software development company. Its work covers AI, enterprise software and cloud infrastructure for clients in financial services, manufacturing, retail and healthcare.

As of 2026, Itransition reports more than 3,000 engineers on its team and over 800 clients served since its founding. Its projects span 40 countries.

The company also has a Microsoft Dynamics 365 practice. The team implements ERP and CRM systems, connects them to other applications and adds Copilot and AI agent capabilities. This experience may suit companies whose AI plans involve changes to the systems their departments already use.

Itransition uses TensorFlow and PyTorch for AI development. It also builds generative AI applications with Azure OpenAI and Amazon Bedrock.

For AiBUY, Itransition helped develop a platform that lets viewers buy products while watching a video. The project involved machine learning and computer vision. In a testimonial published on Itransition’s website, AiBUY CFO Jim Roundtree credited the team with improving product quality and customer satisfaction.

An automotive finance client described a long-term engagement in a July 2026 review on Clutch. They valued the team’s work on complex integrations and ongoing platform support. The review also noted that a structured development process sometimes slowed urgent changes. The client wanted more suggestions for simplifying requirements and delivering MVPs sooner, although the team was receptive to feedback and adjusted its approach.

‍

Intellectsoft

Intellectsoft was founded in 2007. The company builds web and mobile products, modernizes existing software and adds AI features. It works with clients in financial services, healthcare, construction and tourism.

As of 2026, Intellectsoft reports a team of more than 250 developers, AI architects and data specialists. It also lists 35 Fortune 1000 companies among its clients.

The company’s technology stack includes models from OpenAI and Anthropic, alongside LangChain and LlamaIndex for building applications around those models and connecting them to company data. It uses RAGAS and Langfuse to evaluate responses and monitor AI systems.

Intellectsoft added audience segmentation, churn prediction and content recommendations to Mobile Roadie’s app-building platform. In a June 2026 review, a Mobile Roadie executive recalled initially wanting a more complex segmentation model. The architect recommended a simpler approach because each client had relatively little data and app owners needed to understand the results. The executive was glad the architect had suggested the change.

IAATO, the International Association of Antarctica Tour Operators, needed an app that would give users access to documents and visitor guidelines without an internet connection. In an August 2026 review, IAATO’s Allison Kean confirmed that the team had delivered full offline functionality. She also noted that Intellectsoft provided regular budget and schedule updates and often completed work ahead of the agreed deadlines.

SoluLab

SoluLab was founded in 2014. It started with web, mobile and blockchain development, later expanding into AI products and consulting. As of 2026, its website lists more than 250 developers and over 40 completed AI projects.

Its AI consultants help clients decide what to automate, whether they have enough data and which model to use. The company estimates $5,000–20,000 and 2–4 weeks for an initial readiness assessment or AI strategy sprint. Clients approve any subsequent development work separately.

Accounting firm Ocean CPA, for example, needed a tool to identify suspicious financial transactions. SoluLab built the Audit Guard prototype around this review process.

• The system flags higher-risk transactions and explains what raised the concern.
• An accountant reviews the recommendation and approves or overrides it.
• Each decision is recorded in an audit log.

The developers used Python and XGBoost for transaction scoring, with Claude and LangGraph powering the AI agents. In his August 2026 review, Ocean CPA’s founder also highlighted the handover. He received source code, documentation and deployment instructions that another engineer could use to continue development. The prototype was delivered on time and within the agreed fixed price.

Marketing agency EthicalSEO hired SoluLab to reduce time spent on repetitive work. According to its representative’s November 2025 review, tasks that previously took 8–10 hours a week took 2–3 hours after automation. There were some minor delays early on while the teams agreed on priorities. The client was satisfied with the support after launch.

S-PRO

S-PRO was founded in 2014 by two Ukrainians. It started with five developers and now reports more than 300 employees, including over 250 engineers. Headquartered in Zurich, the company works extensively with banks and fintech businesses. Its portfolio also includes energy and healthcare projects.

S-PRO’s AI consulting services cover data audits, identifying tasks worth automating and planning development. Clients can also commission the team to build assistants, forecasting systems or tools that search company documents.

The company offers several ways to structure an engagement.

• A fixed price is available for projects with an agreed scope.
• When an idea still needs testing and requirements may change, clients can pay for the time spent on the work.
• A dedicated team can handle ongoing product development.

For Compliance Aspekte, a platform for managing compliance with standards and regulations, S-PRO developed an AI assistant. The previous chatbot could only answer a predefined set of questions. The new assistant searches company documents, explains requirements and helps users create tasks to prepare for certification. The team used Python, Azure OpenAI and Azure Cognitive Search. According to the case study, development took five months, and the system went live in September 2023.

A fintech CTO described a longer engagement in a February 2023 review on Clutch. His company had been working with S-PRO since May 2020. He valued having the same developers on the project throughout the partnership and praised the team’s daily communication. He also wanted more transparency around internal staff promotions, since these affected costs for the client.

Softermii

Softermii was founded in 2014. The company builds web and mobile products, AI agents and internal business tools for clients in healthcare, fintech, real estate and logistics. As of 2026, Softermii reports more than 120 engineers and over 200 completed projects.

Its consulting services cover AI readiness assessments, choosing tasks to automate, system architecture and policies for using AI at work. Consulting fees are agreed upfront. Development is priced separately, and clients can take the resulting plans to their own team or another provider.

For generative AI applications, Softermii works with GPT, Llama and Mistral models. It also builds RAG systems that retrieve relevant passages from company documents and use them to produce answers with source references.

At Faropoint, an industrial real estate investment firm, the engagement began with support for the internal development team. Softermii later helped migrate infrastructure to AWS and automate financial reporting. In 2026, it also audited the company’s AI environment, introduced safeguards and trained staff. In an October 2026 review, a Faropoint representative valued being able to use one provider for technical work across several departments. Faropoint retained responsibility for planning most of the work.

ThirdEye Data

According to its website, ThirdEye Data was founded in 2011. It started with big data projects, later adding machine learning and AI applications. The company still offers data engineering services, bringing information from different systems together, checking its quality and preparing it for analytics and model development.

Its AI services include several areas.

• Image analysis for product quality inspection, inventory counting and safety monitoring.
• Predicting equipment failures using sensor data.
• Search tools for company documents and AI assistants for employees and customers. The team also builds agents to automate business processes.

For Southern California Edison, ThirdEye Data built a system to assess photographs of utility poles. Before the images could be used for further analysis, the system needed to check for blur and determine whether the equipment was clearly visible. It also read identification tags on the poles. The project used TensorFlow, Google Vertex AI and Azure Data Factory.

Kumar Mankala, who advised SCE’s AI/ML program, described the results in a June 2023 review. Four of the six models were delivered on time and met the quality requirements. The other two fell short of the expected performance and were not deployed. He appreciated the team’s willingness to adapt to changing requirements but recommended testing the complete system earlier, including how its cloud and on-premises components worked together.

ThirdEye Data also works with startups. In an April 2025 review, the co-founder of an AI travel-planning service described the team’s help with the MVP interface, APIs and cloud infrastructure. The client praised its punctuality and flexibility in adjusting the team’s size as the project required.

AI Superior

AI researchers founded AI Superior in 2019 in Darmstadt, Germany. As of 2026, the company reports that 57% of its team hold a PhD. Its work covers computer vision, forecasting, text processing and applications built around language models.

Through its consulting services, AI Superior helps clients choose AI use cases, assess their data and plan implementation. It also helps companies build their own AI teams, from defining roles to evaluating candidates during interviews.

For a UK SaaS provider, AI Superior built a computer vision prototype using Python and YOLOv5 to analyse photographs of the client’s facilities. Requirements included detecting people in images while protecting their privacy. 

In May 2026, Carolina Milberg, CEO of Stiftung GOLDKIND, described the development of a chatbot to support children and young adults in difficult family circumstances. The team built the conversation logic and safeguards. Milberg praised the timely delivery and documentation. Some technical topics required further discussion within the foundation, but the developers were willing to explain them in more detail.

Final Word

You can approach an AI consultant with a problem you don’t yet know how to solve. Your team spends hours on reports each week, paperwork keeps piling up, and you’re unsure where to start with automation. Look for a partner who takes the time to understand how your team works and helps you decide what’s worth investing in.

If that sounds familiar, get in touch with us at Tensorway. Tell us what’s taking up your team’s time or which idea you’d like to try. We’ll talk through the options and suggest a first step to test whether it could work.