Generative AI development services

Generative AI development services: FAQ

• What is generative AI, and how does it work?

  Generative AI refers to machine learning models—such as large language models (LLMs) and generative adversarial networks (GANs)—that create content based on learned patterns in training data. These models produce human-like text, images, audio, or code by predicting and generating sequences or structures. The application range is nearly endless—from healthcare diagnostics and legal summarization to marketing content and game design.

• What types of projects can benefit from generative AI development services?

  Projects across domains can benefit from generative AI, including intelligent customer support, personalised marketing, dynamic content creation, data synthesis for training other AI models, drug discovery, and digital design. Its value lies in automating creativity, improving efficiency, and unlocking new product capabilities that were previously impractical or time-consuming.

• How can generative AI enhance my business operations or products?

  Generative AI can improve your business by automating repetitive creative tasks, generating content, enhancing decision-making through synthetic data and simulations, and accelerating research and development. From daily content marketing to complex industrial design, it supports innovation and reduces time-to-market and operational costs.

• What is the process for developing a custom generative AI solution?

  We follow a structured, collaborative development cycle that includes these steps:

  – Discovery and scoping – understand your objectives and constraints
  – Data preparation – collect, label, and clean data
  – Model selection and prototyping – select the right architecture for your use case
  – Development and fine-tuning – train the model and iterate based on feedback
  – Testing and evaluation – ensure performance, safety, and bias mitigation
  – Deployment and integration – embed into your environment with full scalability
  – Ongoing monitoring – track performance and improve over time

• How long does it typically take to develop a generative AI model?

  Timelines vary by use case and complexity. A simple proof of concept can take a couple of weeks, while robust, production-grade systems may require several months or more. Key factors include data availability, model architecture, regulatory requirements, and integration needs.

• What kind of data is needed to train a generative AI model, and how is it handled?

  High-quality, domain-specific data is essential. This can include text, images, audio, or structured records depending on the task. We follow best practices for data anonymization, security, and compliance with GDPR, HIPAA, or other relevant standards. Our pipelines include preprocessing, augmentation, and validation to ensure data integrity and model fairness.

• What support and maintenance services do you offer after the deployment of the AI solution?

  We provide post-launch maintenance that includes performance monitoring, system updates, model retraining, and user support. Our team ensures your AI solution stays aligned with evolving business needs and industry changes, as well as data environments, and compliance obligations, keeping it scalable and secure.

• How do you validate the AI concept?

  We create interactive prototypes using low-code tools like Flowise or Voiceflow. These simulate real user flows by linking LLMs to tools like CRMs or Notion. This helps test UX, refine prompts, and evaluate feasibility early on.

• How is data collected and prepared?

  We source internal, public, or synthetic data and store it using Supabase. We tokenize and embed documents using SentenceTransformers or OpenAI tools to support RAG pipelines, search, and response generation.

• What does development involve?

  We design custom solutions using models like OpenAI, Mistral, or LLaMA and tools like LangChain. This includes building APIs, chaining logic, vector search, and security layers like feedback collection and content moderation.
  How are models evaluated?
  We use automated tests (e.g., BLEU, ROUGE) and human evaluations to assess accuracy, tone, and coherence. For RAG systems, we measure retrieval quality and hallucination rates using tools like OpenAI Evals.