Question 1

Do we need our own data to start an AI project?

Accepted Answer

It depends on the use case. For LLM-based features (chatbots, summarization, RAG), your existing documents, knowledge bases, and support tickets are usually enough. For custom ML models (predictions, classification, recommendations), you need labeled training data — we help you assess what you have, identify gaps, and design a data collection strategy before any modeling begins.

Question 2

Can you add AI features to our existing product?

Accepted Answer

Yes — this is one of our most common engagements. We embed ML-powered features into existing web and mobile applications: smart search, content recommendations, automated classification, document processing, and AI assistants. Our engineers integrate directly into your codebase and deployment pipeline so the AI feature ships like any other product update.

Question 3

Do you build custom models or use pre-trained APIs?

Accepted Answer

Both, depending on what the problem requires. For many NLP and generative tasks, fine-tuned LLMs or API-based models (OpenAI, Anthropic) are the fastest and most cost-effective path. For domain-specific problems — specialized image recognition, proprietary classification, or edge deployment — we train custom models on your data. We recommend the approach during the feasibility phase.

Question 4

How do you handle data privacy and security for AI projects?

Accepted Answer

We support on-premise and private cloud deployments for sensitive data. For LLM projects, we can use self-hosted open-source models (Llama, Mistral) instead of third-party APIs when data cannot leave your infrastructure. All data processing pipelines are designed with access controls, encryption, and audit logging from the start.

Question 5

How long does a typical AI/ML project take?

Accepted Answer

A focused integration of an existing LLM or pre-trained model into your product takes 4–8 weeks. A custom ML feature with data preparation, model training, and production deployment takes 8–16 weeks. Ongoing ML systems with retraining, monitoring, and iteration are scoped as dedicated team engagements. We define the timeline during the feasibility assessment.

AI / ML Development

FAQ.

AI / ML Development

FAQ.