AI that ships

A custom SaaS typically costs $50,000–$250,000 for a one-time build, depending on complexity. We also offer a build + maintenance model starting at $30k–$150k upfront plus $3k–$10k/month for ongoing support and feature development. Most teams break even on their investment within 18–24 months through reduced SaaS subscription costs and increased team productivity of 30–50%.

On-device AI (also called edge AI) runs machine learning models directly on the user’s device—phone, tablet, IoT sensor, or embedded system—instead of sending data to the cloud. Use it when you need real-time speed (no network latency), data privacy (nothing leaves the device), offline capability, or lower cloud costs. Common use cases include mobile apps, healthcare, finance, and industrial IoT. We specialize in ExecuTorch and React Native ExecuTorch for cross-platform on-device deployment, and work with other frameworks as needed.

Vibe-coded apps are rapid prototypes built with AI coding tools like Cursor, v0, or Claude. They work as demos but lack the architecture, testing, security, and scalability needed for real users. Productionizing means refactoring code for maintainability, adding proper error handling and testing, implementing CI/CD pipelines, optimizing performance, setting up monitoring, and ensuring security—turning a working demo into a reliable, scalable product.

Build custom if you have 10+ team members, pay for 5+ SaaS tools, have unique workflows that off-the-shelf tools don’t fit well, need tight integrations between systems, or want full data ownership and control. Stick with SaaS if you have fewer than 5 people, standard workflows well-served by existing tools, a budget under $50k, or need a solution in under a month.

We work with PyTorch, TensorFlow, JAX, and ONNX for model development. For on-device deployment, we specialize in ExecuTorch and React Native ExecuTorch for cross-platform mobile and edge inference, and integrate other frameworks as projects require. Our team is proficient in Python, Rust, TypeScript, Swift, and Kotlin. We deploy across mobile (iOS/Android), web, IoT, and embedded systems using Docker, Kubernetes, and modern cloud infrastructure (AWS, GCP, Azure).

Timelines vary by complexity. Small projects like POCs, audits, or specific optimizations take 2–4 weeks. Medium projects such as full implementations or migrations take 1–3 months. Large enterprise transformations take 3–6+ months. For custom SaaS development, our typical timeline is 16 weeks from kickoff to deployment, including design, development, testing, and training.

Yes. We build custom CRM and customer success platforms that replace tools like Salesforce, HubSpot, Intercom, and Zendesk. Your custom solution includes a unified customer data platform, AI-powered support ticket routing, custom email automation, pipeline management, and analytics—all tailored to your exact workflow instead of forcing you into someone else’s. Teams typically see 90%+ adoption rates compared to 40–60% with off-the-shelf CRMs.

We work with organizations of all sizes. Startups get production-ready AI architecture from day one so they can ship MVPs faster. Scale-ups get infrastructure optimization for growth. Enterprises get strategic AI adoption, team enablement, and organizational transformation. Our engagement models range from fixed-scope projects to ongoing retainers and embedded team augmentation.

Our custom SaaS projects typically achieve 50–70% reduction in software costs starting in year 2, 30–50% increase in team productivity, 90%+ user adoption (vs. 40–60% for off-the-shelf), and less than 1 week onboarding time for new team members. For a 20-person team paying $30k–$60k/year in SaaS tools, a custom solution typically pays for itself in 18–24 months.

We start with a technology assessment to evaluate AI opportunities and your current capabilities. Then we create an implementation roadmap with phased milestones and ROI projections. We run proof-of-concept projects to validate approaches, provide team training and workshops, and establish governance and best practices. Engagements typically run 3–6 months with clear deliverables at each stage.