Co-built, AI-accelerated software for the workflows off-the-shelf tools miss.
Build faster with the best vibe coding tools and expert AI pair-programming: your teams gain skills, your projects ship on time.
A co-creation approach to AI-supported software development. We pair the best vibe coding tools and platforms with modern engineering practice to design and build applications together with your team. Short sprints, frequent demos, knowledge transfer at every step, and codebases your developers can keep extending.
AI-accelerated development uses large language models as a pair programmer, which means significantly faster scaffolding of new features (hours rather than days) and faster iteration on UI and logic. For well-defined use cases with accessible data sources, a working prototype is typically buildable in two to four weeks. A production-hardened application with integrations, testing, security review and user training takes eight to sixteen weeks for a focused scope. Productivity gains of two to five times over traditional development are typical for experienced teams, and the domain logic your organization already has is the key accelerant.
AI pair-programming excels at applications where the domain logic is complex but the user interface is relatively well-defined. The best enterprise candidates are: document intelligence tools (applications that extract structured data from contracts, reports or records using LLMs); risk and analytics dashboards (connecting operational, ERP and external data into a unified risk view); digital twin visualization interfaces (tools that render twin-backed data in interactive maps or structured viewers); and workflow automation apps (applications that orchestrate multi-step processes across enterprise systems). These are use cases where the business need is clear and the data exists, but no off-the-shelf tool fits the workflow precisely.
AI-assisted development uses large language models as a pair programmer: the developer describes what they want to build in natural language, the AI generates code, tests and documentation, and the developer reviews, refines and guides the output. Time reductions come from faster scaffolding (an API integration that took a developer three days now takes half a day), automated test generation (unit tests are generated alongside the code), and AI-assisted debugging (root causes surface faster when AI can analyze the full codebase context). The developer's role shifts from writing every line to reviewing, directing and integrating AI-generated code.
A custom AI application for an enterprise use case (a focused scope with two to three user roles and three to five source system integrations) typically costs one hundred and twenty to two hundred and fifty thousand dollars including design, development, testing, deployment and initial training. More complex applications with AI inference components like RAG pipelines or agent orchestration range from two hundred and fifty thousand to five hundred thousand dollars. Applications built on our AI-accelerated development approach cost thirty to forty percent less than equivalent traditional software development engagements because of the productivity gains.
Yes. Most major enterprise platforms provide comprehensive APIs that custom AI applications can integrate with. We build integrations with your ERP, CRM, data platforms, Microsoft 365, and other core systems. Custom applications read from these systems, present AI-enriched views and write back to the source system of record, keeping your teams in their existing tools while adding AI capability on top.
To build: an AI application engineer with LLM and API integration experience; a UX designer to ensure the interface matches the target workflow; and a subject-matter expert from the business side who can validate domain logic and test with real data. To maintain: a developer who understands the codebase (full-time only for large applications; part-time shared resource sufficient for focused tools); a data engineer to manage source system integration changes; and an AI operations resource to monitor model performance and trigger retraining. Knowledge transfer throughout the build means your internal team can own maintenance without returning to us for every update.
Cursor vibe coding uses the Cursor AI code editor as a personal pair programmer: a developer describes what they want in natural language, and Cursor generates code suggestions in real time. It works well for individual developers building focused features on well-scoped codebases. A managed AI application development approach, by contrast, combines vibe coding tools like Cursor with engineering governance: code review standards, testing requirements, security review, integration architecture and production deployment disciplines that solo cursor vibe coding typically skips. For enterprises building internal tools that touch operational records, sensitive data or financial systems, the governance layer is not optional. We use the best vibe coding tools within a structured engagement model so your applications ship production-ready, not just prototype-ready.
Replit vibe coding uses the Replit collaborative development environment to build and deploy applications quickly with AI assistance: anyone on the team can describe a tool, watch it built in minutes, and deploy it immediately. It is excellent for low-stakes internal tools, proof-of-concept builds and learning. An organization should move to a dedicated AI development partner when the application needs to integrate with production systems (enterprise APIs, ERP, identity platforms), handle sensitive data (financial, customer or personnel records), or be maintained and extended over time without ongoing vendor dependency. Replit vibe coding is the fastest path to a prototype; professional AI-accelerated development is the path to a production application that your team can own and extend without us.