Question 1

What is the difference between Generative AI and Predictive AI?

Accepted Answer

Generative AI creates new content such as text, code, or images. Predictive AI analyzes historical data to forecast future outcomes, such as sales numbers or equipment failure.

Question 2

How do you ensure our data remains secure with Generative AI?

Accepted Answer

We use RAG (Retrieval-Augmented Generation) and private cloud deployments. Your data is indexed in a secure vector database and is never used to train public models like GPT-4. We adhere to GDPR and ISO 27001 standards.

Question 3

How much does a custom AI solution cost?

Accepted Answer

Costs vary by complexity. A simple GenAI chatbot pilot typically starts around 15,000 to 25,000 USD, while enterprise-grade predictive systems or custom LLM fine-tuning can range from 50,000 to 150,000 USD or more.

Question 4

Can you integrate AI into our existing software?

Accepted Answer

Yes. We build AI microservices that connect to your existing ERP, CRM such as Salesforce or HubSpot, or custom software via RESTful APIs, ensuring no disruption to your current workflows.

Question 5

How long does it take to build an AI MVP?

Accepted Answer

We typically deliver a functional Minimum Viable Product in 4 to 6 weeks. This allows you to validate the value quickly before investing in a full-scale deployment.

Question 6

Do you offer support after deployment?

Accepted Answer

Yes, we offer MLOps (Machine Learning Operations) packages. AI models degrade over time due to model drift; we monitor accuracy, retrain models, and manage server scaling.

Question 7

What industries do you specialize in?

Accepted Answer

We have deep vertical expertise in Fintech for Fraud Detection, Healthcare for Patient Triage, Retail for Dynamic Pricing, and Manufacturing for Predictive Maintenance.

Question 8

Do I need a Data Scientist on my team to work with you?

Accepted Answer

No. We provide a full-service team including Data Engineers, ML Architects, and AI Developers. We also offer training to help your internal team manage the solution post-handover.

Question 9

What is RAG (Retrieval-Augmented Generation)?

Accepted Answer

RAG is a technique that connects an LLM to your live company data. It enables the AI to answer questions based on your specific documents such as PDFs, emails, and databases rather than relying solely on general internet knowledge.

Question 10

Why should we choose custom AI over off-the-shelf tools?

Accepted Answer

Off-the-shelf tools offer generic capabilities and low data privacy. Custom AI provides a competitive advantage by being trained on your proprietary data, ensuring 100% data sovereignty and alignment with your specific KPIs.

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