Custom

Generative AI

Development Services

Generative AI development services help businesses plan, build, integrate, and maintain software that generates content, retrieves knowledge, assists users, or completes business workflows. For a practical overview of the technology, its capabilities, and common applications, read our guide to understanding Generative AI. The development process can include consulting, data preparation, model selection, RAG, application development, AI agents, integration, testing, deployment, evaluation, and monitoring.

Creole Studios provides consulting, custom application development, RAG development, AI agent development, enterprise integration, model optimization, data preparation, testing, deployment, monitoring, and maintenance. Each solution is designed around the client’s workflow, data, systems, users, and security requirements.

The cost depends on the complexity of the use case, the condition of the data, integrations, model approach, user roles, security requirements, and deployment environment. A focused proof of concept costs less than a production enterprise solution with multiple integrations and advanced governance requirements.

Use the Generative AI cost calculator for an initial estimate.

The timeline depends on the project scope, data readiness, integrations, architecture, and testing requirements. A focused proof of concept can be completed faster, while a production solution involving proprietary data, multiple systems, custom interfaces, and governance controls requires a longer delivery cycle.

Yes. Generative AI can be integrated with existing web applications, mobile applications, CRM systems, ERP platforms, customer-support tools, databases, document repositories, communication platforms, and custom software through secure APIs and data pipelines.

Generative AI primarily creates or transforms outputs such as text, images, summaries, code, and responses. An AI agent uses Generative AI capabilities together with memory, tools, rules, and planning logic to complete actions or multi-step workflows.

Yes. A retrieval-augmented generation system can connect a language model with approved internal documents, policies, manuals, reports, databases, and knowledge repositories. Access controls and metadata filtering can be used to restrict information according to user permissions.

Output quality can be improved through better data preparation, retrieval-augmented generation, prompt design, structured outputs, model selection, fine-tuning, validation rules, human review, and use-case-specific evaluation. The appropriate approach depends on the application and acceptable level of risk.

Security controls can include role-based permissions, access-aware retrieval, encryption, sensitive-data handling, private deployment options, activity logging, output validation, and human approval for high-impact actions. The controls should be selected according to the organization’s security and regulatory requirements.

Yes. Post-launch services can include output-quality monitoring, failure analysis, model and prompt updates, knowledge-base synchronization, cost monitoring, security reviews, user-feedback analysis, and continuous workflow optimization.

Look for a provider that can demonstrate product engineering expertise, data and integration capabilities, model flexibility, evaluation processes, security controls, post-launch support, and relevant project experience. The provider should be able to explain when Generative AI is appropriate and when a simpler technical solution would be more effective.

Yes. A proof of concept is an effective way to validate the use case, data, model, architecture, integration, and output quality before full-scale development. It should have clear success criteria and produce a practical recommendation for the next development stage.