Question 1

When should we choose custom AI development over an API like OpenAI?

Accepted Answer

You need custom AI when: 1) You have highly proprietary data (like specific financial or medical records) that standard models don't understand. 2) Data privacy laws prevent you from sending data to cloud APIs. 3) You need a highly specific, lightweight model that runs cheaply at scale, rather than paying per-token API costs. 4) You are dealing with non-text data, like custom sensor data or specific image formats.

Question 2

Who owns the intellectual property of the custom model?

Accepted Answer

You do. When we build a custom AI solution from scratch and train it on your proprietary data, you own 100% of the model weights, the architecture, and the surrounding source code.

Question 3

What if our data is currently messy or unstructured?

Accepted Answer

That is very common. The first phase of our development involves rigorous Data Engineering. We build scripts and pipelines to clean your data, handle missing values, and structure it into a usable format before any AI training begins.

Question 4

How long does it take to develop a custom AI model?

Accepted Answer

Custom AI is a significant engineering effort. A standard predictive model on structured tabular data might take 6–8 weeks. A complex Deep Learning project involving unstructured data (images, complex text) and custom architectures usually takes 3–6 months from discovery to production deployment.

Question 5

Will we need to buy expensive servers to run the AI?

Accepted Answer

Not necessarily. During training, we rent cloud GPUs (like AWS EC2 P4 instances) by the hour, so you don't need upfront capital. For running the model in production (inference), we optimize the model heavily so it can often run on standard, cheaper cloud servers or even on edge devices, depending on the latency requirements.

Question 6

What is 'Model Drift' and how do you handle it?

Accepted Answer

Model Drift happens when the real world changes but your model doesn't (e.g., a pricing model trained before COVID-19 would fail during the pandemic). We implement MLOps monitoring to detect when predictions start losing accuracy. When drift is detected, the system triggers a pipeline to retrain the model on the newest data.

Question 7

Do you only build neural networks and deep learning?

Accepted Answer

No. We are pragmatic. If a simpler machine learning algorithm like Random Forest or XGBoost solves your problem faster, cheaper, and with better explainability than a Deep Neural Network, we will always recommend the simpler, more robust approach.

Question 8

Can you take a model our internal data scientists built and put it into production?

Accepted Answer

Yes. This is a common service we provide. Data scientists often build brilliant models in Jupyter Notebooks that aren't ready for live web traffic. Our software engineers take those models, optimize them, wrap them in secure APIs, containerize them with Docker, and deploy them to scalable Kubernetes clusters.

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