The cost of developing an AI product depends on what you're building, the complexity of the AI, the data involved, integrations, infrastructure and the level of custom development required.

A focused AI proof of concept will naturally cost less than a complete production platform with custom AI workflows, multiple integrations, security requirements and a large user base.

We can help define the right scope for your product and provide a clear estimate once we understand what you're trying to achieve.

The timeline depends on the complexity of the product and the AI capabilities required.

A focused proof of concept can be developed relatively quickly, while a production-ready AI platform involving custom data, integrations, user management, security and complex workflows will naturally take longer.

We normally begin by identifying the smallest useful version of the product that can validate the idea.

We build a wide range of AI-powered products, including intelligent assistants, AI SaaS platforms, AI automation, document processing systems, recommendation engines, computer vision applications and products powered by large language models.

We can also integrate AI into existing web and mobile applications where it can improve the product or automate complex workflows.

Yes. AI doesn't always require building an entirely new product.

We can integrate AI into existing web applications, mobile apps, SaaS platforms and business systems to introduce capabilities such as intelligent search, document analysis, recommendations, automation, AI assistants and natural-language interfaces.

We take a model-agnostic approach rather than building every product around one particular AI provider.

Depending on the requirements, we can work with technologies including OpenAI, Anthropic, Google Gemini, open-source models and custom machine-learning solutions.

The technology is selected based on factors such as performance, accuracy, cost, security, scalability and the requirements of the product.

Yes. Your own business data can often be one of the most valuable components of an AI product.

Depending on the use case, we can build systems using techniques such as retrieval-augmented generation (RAG), vector search, structured data processing and custom machine learning.

This allows AI applications to work with your documents, knowledge and business information while maintaining appropriate controls around how that information is accessed.

Yes. We can build AI-powered agents and workflows that combine language models with business rules, APIs, databases and external systems.

These can automate tasks such as research, document processing, customer support, reporting, data analysis and internal operational processes.

The important part is designing the workflow around a real business process rather than simply adding an AI chatbot.

Yes. Our AI experience includes computer vision and machine learning applications that analyse images and visual data.

Depending on the requirement, this can include image classification, object detection, visual inspection, image analysis and automated classification.

Accuracy depends on the use case, data quality, model and how the AI system is designed.

We don't simply connect an AI model and consider the product finished. We establish evaluation criteria, test outputs and design appropriate validation and human-review processes where required.

For applications where accuracy is critical, testing and evaluation are built into the product development process.

We can do either.

Rounded Labs is a digital product development studio, so we can build the complete product including UX and UI design, frontend, backend, APIs, databases, cloud infrastructure and AI.

We can also integrate AI into an existing product if you already have the rest of the platform in place.

Absolutely. We don't start with a particular AI model or technology. We start with the problem you're trying to solve.

We'll look at your users, workflows, data and desired outcomes and identify where AI could create meaningful value.

Sometimes AI is the right solution. Sometimes a simpler piece of software is better. We believe it's important to establish that before development begins.

Security and data handling are considered as part of the product architecture rather than added at the end.

Depending on the application, this can include access controls, data isolation, encryption, secure APIs, appropriate cloud infrastructure and careful selection of AI providers and models.

The appropriate approach depends on the type of information, regulatory requirements and the way the product will be used.

Yes. An MVP or proof of concept can be a sensible way to validate an AI product before investing in a larger production platform.

We can identify the core use case, build the essential AI workflow, create the product interface and test it with real users or data.

Once the concept has been validated, the product can be developed further with the architecture and infrastructure required for scale.

Yes. AI becomes much more useful when it can interact with the systems and data that your business already uses.

We can integrate AI products with CRMs, ERPs, databases, internal APIs, SaaS platforms, payment systems and other third-party services.

AI products need ongoing monitoring and improvement just like any other software product.

Models change, APIs evolve, usage patterns develop and new product requirements emerge.

We can continue supporting the product after launch with maintenance, optimisation, new functionality, integrations and ongoing AI development.

You don't need a detailed technical specification.

It's helpful to explain the problem you're trying to solve, who the users are, what information or data is involved, what you'd like the AI to achieve and any existing systems the product needs to work with.

From there, we can help shape the product, technology, scope and development approach.

Yes. You don't need to have everything figured out before speaking to us.

We can help turn an initial idea into a clearer product concept, identify the role AI should play, define the first version and determine the technology and development requirements.