AI Development Company for Production-Ready AIApplications
AI Development Company
Unity Infoway designs and develops AI-powered applications that connect leading language and multimodal models with your business data, software, APIs and workflows. We build RAG knowledge systems, AI agents, copilots, intelligent automation, document AI, conversational experiences and custom AI features with evaluation, security, observability and human controls designed in from the start.
Whether you are adding AI to an existing SaaS product, creating a private knowledge assistant, automating a multi-step business workflow or validating a new AI product idea, our team can help move the project from discovery and prototype to production deployment and continuous improvement.
What is AI development?
AI development is the process of designing software that uses machine-learning or generative-AI models to understand information, generate or transform content, retrieve knowledge, make predictions, call tools or support decisions. In modern business applications, the model is only one layer. Production AI also requires data pipelines, retrieval, permissions, prompts, APIs, evaluation, monitoring, security controls, user experience and integration with existing systems.
AI Development Services We Offer
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Discuss Your Business Software Requirements
What We Build with AI
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Generative AI & LLM application development
We build generative AI features and applications around defined user tasks, business context and measurable quality requirements. Depending on the use case, the system can generate or transform text, summarize records, classify content, extract structured data, create drafts, reason over context, call tools or combine multiple input types.
Evaluation, observability & AI quality engineering
This should be a core conversion section: serious buyers increasingly ask how AI quality will be measured.
Frequently Asked Questions
What does an AI development company do?
An AI development company designs and builds software that uses AI models, business data and integrations to perform defined tasks. Work can include generative AI applications, RAG systems, AI agents, copilots, chatbots, document processing, workflow automation, evaluation, security and production deployment.
What is the difference between generative AI, RAG and AI agents?
Generative AI creates or transforms content. RAG retrieves relevant private or current information and supplies it to the model before an answer is generated. An AI agent goes a step further by choosing and calling approved tools to complete a bounded multi-step task.
Can you build AI using our private company data?
Yes. A common approach is a RAG or application-integration architecture that retrieves only authorized company information at request time. The design should include access controls, data-retention rules, tenant isolation where needed, and clear model-provider data policies.
Do we need to train our own AI model??
Usually not. Many business use cases can be solved with a suitable foundation model plus good prompts, retrieval, tools and application logic. Fine-tuning or custom model training is considered only when evaluation shows that it is justified by the task and available data.
Can AI connect to our CRM, ERP or internal APIs?
Yes. AI applications can call approved APIs, query authorized databases or use integration layers such as function tools or MCP servers. The application should validate user permissions and business rules before any action is executed.
What is RAG and when should we use it?
Retrieval-augmented generation retrieves relevant evidence from your documents or data before generating an answer. Use it when information is private, changes frequently, is too large for a prompt, or should be cited back to a source.
What is an AI agent?
An AI agent is software that uses a model to decide which approved step or tool to use next in order to
complete a task. Production agents should have explicit tool permissions, stopping conditions, human
approval where needed and action traces for debugging and auditability.