Question 1

Do we need a lot of data to use AI?

Accepted Answer

It depends on the use case. For LLM-powered chatbots and document processing using pre-trained models like GPT-4 or Claude, you can start with minimal data. For custom predictive models (churn, demand forecasting), you need at least 1–2 years of historical data with reasonable quality. For computer vision, you typically need hundreds to thousands of labelled images. We assess data readiness during discovery and give you an honest answer on what's feasible.

Question 2

Can you integrate AI into our existing software?

Accepted Answer

Yes. We specialise in adding AI capabilities to existing software via REST APIs — the AI model runs as a separate microservice and your application calls it when needed. This approach doesn't require rebuilding your existing system and typically takes 6–12 weeks to go from zero to production.

Question 3

How long does an AI/ML project take?

Accepted Answer

A focused ML model with a clear use case takes 6–10 weeks from discovery to production. A complete AI product feature with data pipeline, model, API integration, and monitoring takes 12–20 weeks. We deliver a working proof-of-concept in 2–4 weeks — before committing to the full build — so you can validate the approach with real data.

Question 4

What is the difference between using the OpenAI API and building a custom model?

Accepted Answer

Using the OpenAI or Anthropic API (GPT-4, Claude) is fastest for NLP tasks like chatbots, summarisation, and document extraction — you can be in production in weeks, with no training data required. Custom ML models (scikit-learn, PyTorch) are better for structured prediction tasks (churn, demand forecasting, fraud) where you have proprietary historical data and need a model trained specifically on your patterns. We recommend the right approach based on your use case, data availability, and cost sensitivity.

Question 5

How do you ensure AI model accuracy?

Accepted Answer

We define accuracy metrics before building anything — precision, recall, RMSE, or whatever's appropriate for your use case. We split data into training, validation, and test sets. We don't declare success until the model exceeds the agreed accuracy threshold on held-out test data that the model has never seen. We also set up ongoing accuracy monitoring in production to detect drift before it impacts your business.

Question 6

Can AI replace specific roles in our team?

Accepted Answer

AI is most effective at augmenting humans — handling the routine, repetitive, rule-based portions of a job and surfacing recommendations for humans to act on. Fully autonomous AI replacing knowledge workers is still the exception rather than the rule. We help you identify exactly where AI adds the most value in your workflows without overpromising on capabilities that current AI genuinely can't reliably deliver.

Question 7

What is RAG and is it relevant to our business?

Accepted Answer

RAG (Retrieval-Augmented Generation) is a technique where an LLM like GPT-4 answers questions by retrieving relevant context from your own documents or database before generating a response. This is how you build an AI chatbot that gives accurate answers about your specific products, policies, or knowledge base — without the LLM hallucinating. If you want a customer support bot or internal knowledge assistant that answers accurately based on your content, RAG is the approach.

Question 8

How do you handle data privacy for AI projects?

Accepted Answer

We take data privacy seriously in every AI project. Data is processed in your cloud environment (not ours), we sign NDAs before any data is shared, we don't use your business data to train public models, and for projects requiring GDPR or HIPAA compliance, we design data handling procedures that meet those regulatory requirements. For particularly sensitive data, we use on-premises model deployment that never sends data to external APIs.

Question 9

What is MLOps and why does it matter?

Accepted Answer

MLOps is the practice of managing AI models in production — version control, performance monitoring, data drift detection, and automated retraining. Without MLOps, a model that was 95% accurate when deployed can degrade to 70% accuracy 6 months later as your data patterns change, and nobody notices until the business is making decisions on bad predictions. We include basic MLOps in every AI project we deploy.

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