Developing an AI product requires more than just plugging an app into a language model. You need a team that can translate an idea into production software, source the data, choose the right models, build the integrations, and support the product after launch.
The issue is having the right product engineering development partner. Many companies offer AI consulting or model development, but not all have the engineering bandwidth to deliver the end product.
Featured below are 10 AI product companies and their main engineering strengths, AI areas of expertise, and the work they've documented on publicly available projects. Without further ado, let's begin!
10 AI Product Development Companies: An Overview
| Company | Best suited for | Publicly documented AI project | Founded | Team size | Clutch rating |
| Suffescom Solutions | Custom AI applications and digital product engineering | AI-powered clinical documentation platform | 2013 | 201–500 | 4.9/5 |
| LeewayHertz | Generative AI applications and LLM-powered products | Scrut's innovative AI application | 2007 | 51–200 | 4.7/5 |
| 10Pearls | AI-enabled business platforms and enterprise software | Automated object detection system | 2004 | 1,001–5,000 | 4.9/5 |
| ScienceSoft | Custom AI software for healthcare, finance, and business operations | AI medical image analysis platform | 1989 | 501–1,000 | 4.8/5 |
| ELEKS | Enterprise software engineering and applied AI | AI-Assisted SDLC | 1991 | 1,001–5,000 | 4.8/5 |
| EPAM Systems | Large-scale enterprise AI products | Albert Heijn employee virtual assistant | 1993 | 10,001+ | 5.0/5 |
| Globant | AI-led enterprise transformation and digital products | AI and ML models for pharmaceutical production planning | 2003 | 10,001+ | Not verified |
| Thoughtworks | Enterprise AI, data platforms, and engineering transformation | AI-powered preclinical data work for Bayer | 1993 | 10,001+ | Not verified |
| Appinventiv | AI-enabled mobile applications and business platforms | Tootle AI travel assistant | 2015 | 1,001–5,000 | 4.6/5 |
| DataRoot Labs | AI research, machine learning, and specialised AI products | On-device email assistant | 2016 | 11–50 | 4.9/5 |
6 Criteria to Evaluate an AI Product Development Company's Capabilities
Selecting an AI product development company by considering only the technology stack and portfolio of the company is a mistake. You have to be sure that the company is able to understand the business needs and develop AI products that will fit well into your existing architecture and work properly.
Evaluate potential partners against these six criteria:
1. Use-Case Fit and Alignment with Business Goal
Check to see if the vendor is capable of formulating your business problems as an AI use case. They should be able to articulate the problem statement, the desired outcome, the metrics of success, and data requirements even before they propose a solution. Stay away from vendors who only provide out-of-the-box AI solutions.
2. Production Experience and Proven Track Record
Don’t limit yourself to just prototypes, proofs of concept, or sales demos. Check how the company uses the AI solutions it offers in the real-world scenarios it faces. Request examples of past projects, performance metrics, failed implementation cases, lessons learned, etc.
3. Data Engineering and Integration Capabilities
The effectiveness of the AI solution is directly proportional to the quality of data used to fuel the solution. You may try to find out if the company has scalable AI data pipelines that can process vast amounts of data and meet the compute demands of enterprise AI.
4. AI Security, Governance, and Compliance
Verify how they protect sensitive information and practice proper data management in regard to the AI technology at every stage of its life cycle. Determine whether the service provider is experienced and familiar with terms like the EU AI Act, NIST AI RMF, GDPR, and SR 11-7. Inquire if the service provider implements mechanisms to control PII, restrict access, audit data logs, and prevent prompt injection and model stealing.
5. Model Evaluation and Performance Monitoring
Product development is not just about building the model; there should be a clear process in place to test the model's accuracy and consistency. Pay attention to things like benchmarking, regression testing, infrastructure for evaluating LLMs, production monitoring, and improvement of models.
6. Discovery Process and Pilot Delivery Structure
Review the firm’s approach to project discovery, technical delivery, and early-stage development. Instead of a fixed price without discovery of requirements, request discovery services or a paid pilot. There should be agreed deliverables, timeline, technical scope, and acceptance criteria before moving forward with development.
Turn Your AI Requirements Into a Working Product
From model selection to product engineering, let's build the AI solution your business needs.
1. Suffescom Solutions
Perfectly Suitable for: Custom AI solutions, AI-driven software, and digital product engineering
Suffescom Solutions is a provider of custom software, mobile applications, and digital products based on artificial intelligence. The list of publicly disclosed services provided by the company includes artificial intelligence and intelligent automation, software and product engineering, cloud infrastructure, and data engineering.
According to the description of their services, they offer full-cycle product engineering and application development focusing on business needs rather than developing artificial intelligence models only as a service. The publicly available information about the company states that Suffescom Solutions was founded in 2013, and the company operates in the US, UK, UAE, Australia, and India.
Capabilities
- Custom AI application development
- Workflow automation powered by AI
- NLP and generative AI implementation
- Healthcare Software & FHIR Implementation
- Web & Mobile product engineering
- Cloud and Backend Development
Key Project
AI-Powered Clinical Documentation Platform: Developed an AI-powered healthcare platform that automates medical transcription, SOAP note generation, medical coding, and FHIR-based claim generation.
2. LeewayHertz
Ideal for: Generative AI applications, LLMs, and enterprise AI solutions
LeewayHertz builds AI applications and software solutions for businesses. Some of their published case studies include generative AI projects, LLM applications, data analysis, and AI assistants.
Some of the solutions developed by the company include an LLM solution that enables people to access compliance standards and audit data using Scrut technology. They have also mentioned an LLM machinery troubleshooting application for a Fortune 500 manufacturing company.
Capabilities to consider
- Developing generative AI applications
- LLM technology integration
- AI assistants and chatbots
- Data analysis
- Custom software development
Key Project
Scrut AI Application: Developed an LLM-powered application that helps users retrieve information from compliance frameworks, security documents, and audit data using natural language queries.
3. 10Pearls
Best for: AI-driven business platforms and enterprise product development
10Pearls specializes in digital product engineering and technology solutions ranging from artificial intelligence to cloud development and software development. The firm’s publications range from the application of AI in the finance, healthcare, and enterprise spaces.
10Pearls' case studies cover topics such as AI-powered real estate valuation and facial recognition work for Related Faces, along with an agentic AI financial management solution.
Capabilities of note
- Artificial intelligence and machine learning development
- Enterprise platforms with AI
- Digital product engineering
- Cloud development
- Financial technology solutions
- Healthcare software development
Key Project
Automated Property Image Detection: Built an AI-powered image analysis system for property appraisal that identifies room types, detects objects, and flags prohibited content in property images.
4. ScienceSoft
Suitable for: Custom AI software for healthcare, finance, and business processes
ScienceSoft provides both AI development and consulting services along with custom software engineering. AI portfolio contains predictive analytics, computer vision, AI-driven business applications, and machine learning solutions.
The firm keeps track of its projects in a number of sectors. Some examples are AI-driven investment modeling software, an AI tutor MVP, and a medical image analysis system.
Capabilities to consider:
- Machine learning development
- Computer vision
- Predictive analytics
- AI-driven business applications
- Custom software development
- Healthcare and finance software
Key Project
AI Medical Image Analysis Platform: Modernised a cloud-based medical imaging platform and improved its AI-powered image analysis capabilities to increase reported analysis accuracy from 30% to 90%.
5. ELEKS
Best suited for: Software engineering for enterprises, data science, and artificial intelligence applications
ELEKS offers software engineering and technology consulting solutions. Its published work encompasses data science, machine learning, computer vision, enterprise platforms, and AI-driven software development.
Its portfolio of case studies includes solutions such as a vision intelligence platform for Robovision, an AI-driven software development lifecycle solution, and a machine learning carbon emissions estimation platform.
Relevant capabilities:
- Machine learning and data science
- Computer vision
- Software engineering for enterprises
- Data engineering
- Cloud and platform engineering
- AI-driven software engineering
Key Project
Integrated AI into the SDLC to build a highly secure, agentic AI-powered decision intelligence platform designed for government, defense, and high-security enterprise environments.
6. EPAM Systems
Best suited for: AI products for enterprises and digital engineering on a large scale
EPAM Systems is a company offering services in digital engineering, software development, and consultancy. Its AI solutions include generative AI, enterprise platforms, data engineering, and AI-based business applications.
An illustrative case study of EPAM is its partnership with Albert Heijn and Microsoft to create an AI-based virtual assistant for supermarket staff. The assistant was embedded into the company’s existing app and connected to backend applications using Microsoft Azure.
Relevant skills:
- Enterprise AI product development
- Generative AI applications
- Cloud-native software engineering
- Data and platform engineering
- Integration of AI with existing systems
- Large-scale digital transformation
Key Project
Albert Heijn Virtual Assistant: Developed an AI-powered assistant integrated into the supermarket's employee app to help staff access product information, stock details, and operational guidance.
7. Globant
Best for: AI-driven enterprise transformation and digital experiences
Globant offers digital product engineering services, AI, data, and enterprise transformation. Its portfolio of AI services consists of AI studios for industries as well as platform development for AI agents and assistants.
Its publications include customer experience using AI, educational use cases, and machine learning projects for operational purposes.
Relevant Capabilities:
- AI & machine learning
- Generative AI and AI agents
- Enterprise AI platforms
- Digital product engineering
- Data & analytics
- Technology solutions for industries
Key Project
Pharmaceutical Production Planning: Developed machine learning models and data pipelines to predict pharmaceutical manufacturing timelines, helping improve production scheduling and planning.
8. Thoughtworks
Useful for: Enterprise AI, data platforms, and engineering transformation
Thoughtworks is a technology consulting firm with competencies in software engineering, data, and enterprise AI. Its AI portfolio includes machine learning, generative AI, data strategy, and enterprise AI integration within existing systems.
Client stories include projects such as AI in Bayer preclinical data, agentic AI with Splio, and AI-driven enterprise workflows.
Capabilities that apply:
- Enterprise AI strategy and engineering
- Machine learning
- Generative AI
- Data platforms and data strategy
- Software architecture
- Modernising legacy systems
Key Project
Bayer's PRINCE Platform: Built a preclinical research data platform and integrated a generative AI chatbot to help researchers search and summarise information from historical study reports.
9. Appinventiv
Best for: Mobile apps and digital products enabled by AI technology
Appinventiv creates mobile applications and digital products, which include AI services related to AI agents, intelligent automation, and AI-based apps.
The company's publicly available portfolio features Tootle, which is an AI-based travel assistant, and an AI-enabled last-mile delivery solution for Americana.
Capabilities to consider:
- Mobile apps enabled by AI technology
- AI agents and assistants
- Integration of generative AI
- Development of mobile and web products
- API development
- Digital product design
Key Project
Tootle AI Travel Assistant: Developed an LLM-powered travel assistant with voice and text interactions, integrating external information sources to provide travel recommendations and conversational assistance.
10. DataRoot Labs
Ideal for: Customised AI engineering and machine learning products
DataRoot Labs is known for its AI R&D work. The company's publicly available portfolio covers topics like machine learning, conversational AI, robotics, and AI-driven applications.
Examples of DataRoot Labs' public works include autonomous logistics, embodied conversational AI, and specialised AI assistants. Case studies from the company cover topics like on-device email assistants and developer workflow assistants.
Capabilities:
- Artificial Intelligence Research & Development
- Machine Learning
- NLP
- AI Assistants
- Computer Vision + Robotics
- Custom AI Engineering
Key Project
On-Device Email Assistant: Built an AI-powered Chrome extension using a fine-tuned, lightweight BERT model to classify emails, organise inboxes, and support unsubscribe workflows.
How Much Does AI Product Development Cost?
The exact average price of an AI product cannot be estimated. The addition of some AI functionality to a current application is a completely different thing compared to a separate platform with customized models, pipelines, integration, etc.
Cost depends on:
- Product scope: A chatbot or recommendation function is usually cheaper to develop compared to a full-fledged AI platform consisting of several modules.
- Data preparation: Data cleansing, labeling, structuring, and data integration could consume significant resources.
- AI architecture: Utilizing a pre-built model via an API and building a customized one are two different things.
- Integration: Integrating AI with CRM, ERP, payments, EHR, and other services requires additional development effort.
- Security and compliance: Security considerations, access control, logging, and compliance affect architecture and testing.
- Ongoing operations: Hosting, using a model, monitoring, retraining, etc., influence total cost.
What to Include in Your AI Product Budget
| Cost area | What the budget should cover |
| Discovery and feasibility | Use-case validation, requirements, data assessment, and technical planning |
| UX and product design | User journeys, wireframes, interface design, and prototypes |
| Data engineering | Data collection, cleaning, labelling, pipelines, and storage |
| AI development | Model selection, integration, custom model development, and evaluation |
| Software engineering | Frontend, backend, APIs, and application infrastructure |
| Testing and deployment | Functional testing, model evaluation, security checks, and production release |
| Maintenance | Monitoring, bug fixes, model updates, infrastructure, and support |
How to Choose the Right AI Product Development Company
The availability of an AI services page from the company alone does not tell you if they can provide your product. You need to look into the proposed team, method, and delivery plan.
1. Check for Relevant Product Experience
Find those that work on something similar to your product. Just because one firm has built an AI chatbot does not necessarily mean that they know how to build a computer vision system or predictive analytics tool.
Ask for a case study explaining the business challenge, their part in it, the technical solution used, and what was delivered.
2. Understand Its AI Development Approach
Questions that one could ask include whether the choice will be made from an existing machine learning model, an existing model fine-tuned to fit the use case, or the development of a machine learning model from scratch. Considerations such as accuracy, latency, data sensitivity, cost of running, and maintenance needs must be taken into account.
3. Evaluate the Full Engineering Team
An AI product needs more than machine learning engineers. Depending on the scope, the project may also require:
- Product managers and business analysts
- AI and machine learning engineers
- Data engineers
- Backend and frontend developers
- UX/UI designers
- Cloud and DevOps engineers
- QA and security specialists
Confirm who will actually work on your project. Ask whether the people involved in discovery will remain engaged during development and deployment.
4. Data Security and Ownership
Before providing any company data, find out how the company plans to protect it.
Talk about data access, encryption, storage time, policies for the model provider, and whether the company uses your information to train their models. Your contract needs to clearly describe ownership of the source code, custom models, training data, documentation, and other deliverables.
5. Production and Post-Launch Support
Success of the prototype doesn't automatically imply success of the production.
Find out how the company is going to test output from models, monitor performance, manage downtime, track usage costs, and update models.
Questions to Ask Before Hiring an AI Product Development Company
Use these questions during vendor discussions:
- Have you developed a product with a similar AI use case?
- Can you share a case study and explain your team's specific contribution?
- Who will own the AI architecture and technical decisions?
- Will you use an existing model, fine-tune one, or develop a custom model?
- How will you test accuracy and manage incorrect AI outputs?
- How will you protect our data and control model-provider access?
- What is included in the development estimate?
- Who owns the source code, models, and project documentation?
- What happens if the AI model or third-party API changes?
- What support do you provide after launch?
Red Flags to Watch for When Evaluating AI Development Partners
Be cautious if a company:
- Promises a specific accuracy rate before reviewing your data.
- Recommends an LLM for every use case without discussing alternatives.
- Cannot explain how it will evaluate model performance.
- Shows portfolio projects but cannot clarify its role in delivering them.
- Excludes deployment, monitoring, or maintenance from its proposal without making that clear.
- Does not define data ownership, source-code rights, or model-provider terms.
- Gives a fixed quote without understanding the product requirements.
These issues can create technical, financial, and operational problems after development starts.
Should You Build an AI Product In-House or Hire a Development Company?
The right approach depends on your internal team's capabilities, delivery timeline, and long-term product plans.
| Factor | In-house development | External development partner |
| Technical control | Direct control over architecture and delivery | Defined through the contract and project governance |
| Team availability | Depends on internal hiring and existing skills | Can provide access to a wider mix of specialists |
| Initial setup | May require recruitment and infrastructure investment | Can start with an agreed project team |
| Product knowledge | Builds continuously within the organisation | Requires knowledge transfer and documentation |
| Long-term ownership | Internal team can own ongoing development | Depends on the support and handover arrangement |
| Best suited for | Organisations with established AI and product teams | Businesses that need external engineering capacity or specialised expertise |
Final Thoughts
It all comes down to finding an AI development company whose engineering prowess matches your product needs. An AI developer company with expertise in LLMs may not necessarily be the best choice for developing an industrial computer vision system. Similarly, an enterprise engineering company might be too much for an AI MVP for a start-up.
Filter down a list of potential AI developers by their past experience in similar projects, the qualifications of the suggested team, the engineering solution, data security, and follow-up after deployment. Do a thorough comparison of offers and not rely on rankings and vague claims of AI expertise.
Your best development partner can outline how it will implement your AI feature and integrate it into the final product.
FAQs
1. What does an AI product development company do?
An AI product development company designs and builds software products that use artificial intelligence. Its work may include product discovery, data engineering, model development, LLM integration, application development, testing, deployment, and maintenance.
2. What is the difference between AI development and AI product development?
AI development can focus on a specific model, algorithm, or AI feature. AI product development covers the broader software product, including its interface, backend, integrations, infrastructure, user experience, and ongoing operation.
3. How long does it take to develop an AI product?
The timeline depends on the product's scope, data readiness, AI complexity, and integration requirements. A limited proof of concept takes less time than a production platform with custom models, multiple integrations, and enterprise security requirements. Ask vendors to separate discovery, prototype, MVP, and production timelines in their proposals.
4. Do AI product development companies build custom AI models?
Some do. Others primarily integrate existing models or adapt open-source and third-party models. The right approach depends on the use case, available data, accuracy requirements, privacy constraints, and operating budget.
5. How much does it cost to develop an AI product?
There is no universal price. Costs depend on product scope, data preparation, model complexity, integrations, infrastructure, and support requirements. Request an itemised estimate based on your product requirements rather than comparing headline prices.
6. What technologies are used to build AI products?
Common technologies include Python, machine learning frameworks, cloud platforms, vector databases, model APIs, data pipelines, and application development frameworks. The right stack depends on the product's architecture and operational requirements.
7. How do I evaluate an AI development company's case studies?
Check whether the case study identifies the problem, solution, technologies, and the company's contribution. Ask for a demonstration or a reference where possible. Treat performance figures published only by the vendor as vendor-reported results.
8. Who owns the AI product after development?
Ownership depends on the contract. Before signing, define rights to source code, custom models, training data, datasets, documentation, and other deliverables. Also check the licensing terms for third-party models, APIs, and software components.
9. Can an AI development company integrate AI into an existing application?
Yes. Depending on the application and its architecture, a development partner can add AI-powered search, recommendations, document processing, chat interfaces, predictive features, or workflow automation. The existing system's APIs, data quality, and security requirements will influence the work.
10. What should an AI product development agreement include?
It should define project scope, deliverables, milestones, acceptance criteria, payment terms, data protection, intellectual property ownership, third-party licensing, change requests, warranties, and post-launch support.
