Turn AI Into Real Business Capability.

An AI solutions company designs and develops software that uses artificial intelligence to solve business problems. This may include Generative AI applications, AI agents, machine learning, intelligent automation, predictive analytics, conversational AI and AI integration. Grawlix develops these capabilities around specific business workflows, data and objectives.

Generative AI development involves creating applications that use generative models to produce or transform information such as text, summaries, answers, recommendations or other outputs. Business applications can combine these models with enterprise data, APIs, permissions and workflows.

Grawlix develops Generative AI applications, AI agents, RAG systems, conversational AI, intelligent assistants, machine learning models, predictive systems, document intelligence solutions and AI-powered business automation.

Yes. Depending on the use case and data architecture, AI applications can connect with approved company documents, databases, APIs and knowledge sources. Retrieval-Augmented Generation is one approach for grounding language-model responses in relevant enterprise knowledge.

Retrieval-Augmented Generation is an architecture in which relevant information is retrieved from an external knowledge source and supplied as context to a generative model. It is commonly used for enterprise knowledge assistants and applications that need access to business-specific information.

AI agents are software systems designed to pursue defined goals by reasoning about tasks, accessing information, using tools or APIs and performing actions. Agent architectures can be designed with permissions, guardrails and human approval for appropriate business use cases.

Yes. AI can be combined with APIs, workflow automation and custom software to assist or automate repetitive activities such as information processing, document handling, customer support, data entry, reporting and defined multi-step operational tasks.

Yes. AI capabilities can often be added to existing web applications, mobile applications, SaaS platforms, portals and enterprise systems through APIs, data integrations and custom development.

The appropriate model depends on the use case, performance requirements, data, privacy considerations, infrastructure and budget. Solutions may use commercial models, open-source models or specialized machine-learning models as appropriate.

Timelines vary based on complexity, data readiness, integrations, user experience, security requirements and whether the project is a prototype or production system. A focused proof of concept can generally be evaluated faster than a full enterprise implementation.

AI development cost depends on the use case, architecture, model usage, data preparation, integrations, infrastructure and required product features. Grawlix can review the requirement and recommend an appropriate scope and implementation approach.

A useful starting point is identifying repetitive knowledge work, high-volume processes, valuable data, customer-service bottlenecks, manual decision points or product experiences that could benefit from intelligence. An AI discovery assessment can then evaluate feasibility, data readiness and expected business value.

Yes. A proof of concept can be used to validate the use case, model behavior, data availability and technical feasibility before committing to a larger production implementation.

Share the business challenge, existing process, available data, systems involved and desired outcome. Grawlix can then evaluate the use case and discuss the appropriate AI architecture and implementation path.