Frequently asked questions about our AI software

We build a wide range of AI-powered solutions including predictive analytics platforms, natural-language chatbots and virtual assistants, computer-vision systems for quality control and object detection, intelligent process automation that combines RPA with machine-learning decision engines, and fully custom AI applications designed from the ground up. Every solution is tailored to the client's industry, data maturity and business objectives rather than being a one-size-fits-all product.

Timelines vary significantly depending on scope. A focused proof-of-concept — for example, a demand-forecasting model trained on your historical sales data — can often be delivered in four to six weeks. A full production deployment involving data pipeline engineering, model training, user-interface development, integration with existing systems and staff training typically takes three to nine months. We always break projects into phases so you see tangible progress early and can adjust direction based on real results.

Not necessarily. While more data generally leads to better models, we have successfully launched projects with surprisingly modest datasets by leveraging techniques such as transfer learning, data augmentation and synthetic data generation. During our initial assessment we evaluate the quality, volume and variety of your existing data and advise on any gaps that need filling before model training begins. In some cases we help clients set up new data-collection processes as part of the engagement.

You do. All custom code, trained models, documentation and associated artefacts become your intellectual property upon full payment, as explicitly stated in our statement of work. We never lock clients into proprietary platforms or create dependencies that force ongoing reliance on us. Our pre-existing frameworks and internal tools remain our property but are licensed for use within your project scope at no additional cost.

Our technology choices are driven by the requirements of each project. For machine learning and data science we primarily use Python with libraries such as PyTorch, TensorFlow, scikit-learn and Hugging Face Transformers. For backend services we work in Python (FastAPI, Django), Node.js and Go. Front-end interfaces are typically built with React or Vue.js. Infrastructure is managed through Terraform and deployed on AWS, Google Cloud or Azure depending on client preference. We are framework-agnostic and will adapt to your existing tech stack when it makes sense.

We offer two primary pricing models. Fixed-price engagements are ideal for well-defined projects with clear deliverables and timelines — you know exactly what you will pay before work begins. Time-and-materials arrangements suit exploratory or rapidly evolving projects where scope may shift as discoveries are made. In both cases we provide detailed estimates broken down by phase, and we never bill for work that has not been approved. Our initial consultation is always free.

Yes, integration is one of our core strengths. We have connected AI models to ERP systems (SAP, Oracle, Microsoft Dynamics), CRM platforms (Salesforce, HubSpot), data warehouses (Snowflake, BigQuery, Redshift), messaging platforms (Slack, Microsoft Teams) and bespoke internal tools via REST APIs, webhooks and event-driven architectures. We always map out the integration architecture before development begins to minimise disruption to your live environment.

We offer tiered support plans ranging from basic monitoring and bug fixes to fully managed services that include model retraining, performance optimisation, feature enhancements and 24/7 incident response. Most clients start with a three-month post-launch support period included in the project fee, then transition to a monthly retainer if ongoing assistance is needed. We also provide knowledge-transfer sessions and documentation so your internal team can handle day-to-day operations independently.

We work with organisations of all sizes. For startups, we often begin with a focused MVP — a minimum viable AI product — that demonstrates value to investors and early customers without requiring a large upfront investment. For enterprises, we typically engage through a discovery workshop, followed by a phased roadmap that aligns with corporate governance and procurement processes. The common thread is our insistence on measurable value before scaling.

Over the past several years we have delivered projects across mining and resources, agriculture, healthcare, financial services, retail and e-commerce, logistics, education, government and professional services. While domain expertise accelerates delivery, our methodology is designed to be industry-adaptive. We invest time upfront to understand the regulatory landscape, competitive dynamics and operational nuances of each client's sector.