Our real competitors are the tech giants and their cheerleaders, AI SaaS vendors, and agencies. Their business models will never center on bespoke design.
Beyond performance from your specs and our full-stack expertise (at sharp pricing), our maintenance contracts include SLA close to our infrastructure provider (Google Cloud), faster corrective response, and LLMOps follow-through aligned with the best commitments (reporting, hotline). Systems are maintained by their builders. Tickets give way to direct communication (responsiveness and evolution are contractual). Consultancies will sell shelfware. SaaS vendors will too, at heavy cost.
Beyond the usual hype that inflates complexity and saves vendors time, slow AI adoption is partly about strategic and social implications. Employment impact should be surfaced in audit (it shapes design choices). We think that alone explains a lot of today’s lag and “shadow AI”, as RAG adoption shows with multi-year delays.
Classic AI agencies, for their part, ship simple automations (LLMs when needed), often “good enough”. They cannot train models or optimize RAG algorithms for a specific task (the context-window problem), so they cannot handle the most demanding use cases. Like services firms, they will not tell you, and they cannot manage these problems. Your safeguard would be to specify evaluation benchmarks, but you will be neither competent nor helped on that point.
AI-assisted development tools let a studio keep full control over the codebase, deliver high-quality results, and refine over time a methodology covering every aspect of design that, in turn, shortens execution and iteration time.
