Everything you need to know about working with BigTech
Our Process
How does your development process work?
We follow an agile methodology with 2-week sprints. It starts with discovery & planning, followed by design, development, testing, and deployment. You get full visibility into progress through daily updates and weekly demos.
How long does a typical project take?
Timelines vary by scope. An MVP or prototype typically takes 4-6 weeks. A full-featured web application takes 8-12 weeks. Complex enterprise projects with AI/ML components may take 12-20 weeks.
Do you provide ongoing maintenance?
Yes, all our plans include a maintenance period (1-3 months). For long-term support, we offer monthly retainer packages covering updates, security patches, and feature enhancements.
Pricing & Engagement
How much does a custom software project cost?
Our projects start at Nu. 499,000 for a basic MVP. We offer fixed-price, time-and-materials, and dedicated team models. Contact us for a free estimate.
Do you offer fixed-price contracts?
Yes, for well-defined scope projects we offer fixed-price contracts. For larger projects, time-and-materials or dedicated team models provide more flexibility.
What payment terms do you offer?
We work on a milestone-based schedule: 30% upfront, 40% at midpoint, and 30% upon delivery. Custom plans available for enterprise.
Technical
What technologies do you specialize in?
Python, JavaScript/TypeScript, React, Next.js, Node.js, React Native, Flutter, PostgreSQL, MongoDB, Docker, Kubernetes, AWS, Azure, TensorFlow, and more.
Is my code and data secure?
Absolutely. We follow industry best practices including encryption, secure cloud infrastructure, GDPR compliance, and NDAs. You own 100% of the source code.
Can you integrate with existing systems?
Yes, we specialize in API integrations with existing platforms, legacy systems, third-party services, and databases including ERP, CRM, and payment systems.
AI & ML
What AI/ML services do you offer?
Custom ML model development, NLP, computer vision, predictive analytics, recommendation engines, chatbots, and intelligent process automation.
Do I need large datasets for AI?
Not necessarily. We use transfer learning, data augmentation, and synthetic data generation for small datasets.
How do you deploy AI models?
As scalable API endpoints using Docker containers on AWS SageMaker, Azure ML, or GCP AI Platform with monitoring and automatic retraining.