We build a wide range of AI/ML solutions including predictive analytics models for forecasting and decision support, natural language processing systems for chatbots and document analysis, computer vision applications for image recognition and quality control, recommendation engines for personalized user experiences, and intelligent automation systems that streamline business processes.

Our solutions are custom-built for your specific data and business requirements, not off-the-shelf products. We work with both structured data (databases, spreadsheets) and unstructured data (text, images, audio) to deliver actionable insights.

We do both. Many of our projects involve integrating AI capabilities into existing systems, connecting new APIs to legacy software, or modernizing outdated architectures while preserving business logic. We're experienced in working with codebases of all ages and conditions.

For greenfield projects, we design systems from the ground up using modern best practices. Either way, we ensure smooth integration with your current technology stack and business processes.

Absolutely. Data preparation and cleaning is a core part of our AI/ML service. We help clients assess their data quality, implement data pipelines that clean and transform raw data, and establish ongoing data governance practices.

In fact, most businesses don't start with "clean" data. We can work with messy spreadsheets, siloed databases, unstructured documents, and inconsistent records to build reliable ML models.

We work across multiple industries including e-commerce and retail, financial services and fintech, healthcare and medical technology, logistics and supply chain, energy and sustainability, agriculture and agritech, and professional services.

Our technical expertise translates across domains. We quickly learn industry-specific requirements and combine that with deep technology knowledge to deliver relevant solutions. View our portfolio for examples across different sectors.

Every project begins with a free discovery call where we learn about your business challenges, goals, and current technology landscape. This helps us understand if we're a good fit and allows us to ask clarifying questions.

After the call, we provide a proposal outlining our recommended approach, timeline, deliverables, and investment. Once approved, we kick off with a more detailed requirements workshop before development begins.

Timelines vary based on complexity. A proof-of-concept or MVP can often be delivered in 4-8 weeks. Production-ready AI systems typically take 3-6 months. Large-scale enterprise projects may span 6-12 months or longer.

We break every project into phases with clear milestones, so you see progress regularly and can provide feedback throughout. We provide detailed timeline estimates after the discovery phase.

Yes. We offer flexible support arrangements including monitoring and maintenance packages for production systems, model retraining and performance optimization as your data evolves, feature enhancements and iterations based on user feedback, and on-call support for critical systems.

Many clients continue working with us long after initial launch, treating us as their extended technology team.

We believe in transparent, regular communication. Typical arrangements include weekly progress updates via email or video call, access to project management tools (Notion, Jira, or similar) for real-time visibility, milestone demos where you can see and test progress, and direct access to the technical team for questions.

We adapt our communication style to your preferences—some clients want daily standups, others prefer weekly summaries.

Project investment varies significantly based on scope and complexity. Small projects like landing pages or simple integrations typically start from R50,000. Medium projects like custom web applications or initial ML models range from R150,000-R500,000. Large enterprise projects involving complex AI systems or full platform builds start from R500,000+.

We provide detailed quotes after understanding your requirements. We're transparent about costs and work with clients to phase projects if budget is a constraint.

ROI depends on the use case, but our clients typically see returns through cost reduction (automating manual processes can reduce labor costs by 40-70%), revenue increase (recommendation engines often boost sales by 15-35%), better decisions (predictive analytics improves forecast accuracy by 20-40%), and competitive advantage (AI capabilities that competitors lack).

We help you build a business case with projected ROI before starting any project. Read our client testimonials for specific results.

Yes. Most projects are billed in phases tied to milestones—typically a deposit to begin, then payments at key deliverable points, with final payment on completion. This protects both parties and ensures you only pay for delivered work.

For ongoing retainer arrangements, we bill monthly. We're flexible and can discuss arrangements that work for your cash flow.

Security is fundamental to everything we build. We follow ISO 27001 security standards, ensure GDPR and POPIA compliance for all data handling, use encryption for data at rest and in transit, implement role-based access controls, and conduct regular security audits and penetration testing.

We sign NDAs and data processing agreements as standard. Your data remains your property, and we can work with anonymized or synthetic data when full access isn't required.

Yes. Upon final payment, you receive full ownership of all custom code, models, and documentation we create for your project. We don't retain rights to use your proprietary code elsewhere.

We may use open-source libraries and frameworks (which remain under their respective licenses), but all custom work is yours. We also provide complete documentation and knowledge transfer so you're never locked in.

Yes. We're experienced with a wide range of technologies and can adapt to your existing stack. Whether you're running on AWS, Azure, or Google Cloud; using Python, JavaScript, or other languages; or working with specific frameworks or databases—we can integrate with your environment.

If we recommend changes to your stack, we'll explain the reasoning and business case. We never push technology changes just for the sake of it.