Enterprise Application Development Built to Scale

Enterprise application development is the design, engineering, and maintenance of software built for the operations of a growing company or large organization: multi role platforms, internal business systems, integrations between existing tools, and AI powered workflows. Unlike a single team app, an enterprise application has to handle many concurrent users, connect to systems like ERPs and CRMs, enforce role based access and audit trails, and stay maintainable for years. We cover the full cycle: discovery, UX, architecture, engineering, QA, deployment, and ongoing support.

At our published estimate ranges, a mid sized multi role platform costs $50,000 to $150,000, and enterprise systems cost $150,000 to $300,000 and up, at a blended rate of $25 to $49 per hour. US agencies typically quote 3 to 5 times more for identical scope. Cost is driven by the number of integrations, the depth of role based permissions, compliance review, and the volume of legacy data to migrate, more than by feature count. Our software cost guide breaks down the numbers and includes a calculator.

A mid sized platform takes 4 to 8 months and an enterprise system 8 to 18 months. Timeline is driven more by integrations, compliance review, data migration, and decision speed than by feature count. We ship working software in increments from the first weeks, with a staging environment you can click through every sprint, rather than a single delivery at the end.

Buy an off the shelf suite when a mature product already fits 80% or more of the workflow and that workflow is not where you compete. Build custom when the process IS the business, when per seat licence fees scale painfully with headcount, when the suite forces workarounds that cost real hours every week, or when you need to own the data model and the release schedule. Many enterprises run a hybrid: a suite for commodity functions, custom software for the core that differentiates them.

Through a dedicated integration and middleware layer. We connect ERPs, CRMs, e-commerce systems, payment gateways, and BI tools over their APIs or event streams, so data flows between the systems you already run without manual rekeying. Where a system has no clean API, we build adapters and reconciliation jobs. The goal is one trusted data model across the business instead of spreadsheets bridging the gaps.

Security is built in, not added later. We operate an ISO/IEC 27001:2022 certified Information Security Management System and align our handling of personal data with the EU General Data Protection Regulation (GDPR). Enterprise builds include role based access control, encryption in transit and at rest, per record audit trails, and security review as part of the build. We run everything in cloud accounts you own and control, and hand over full source and documentation.

Yes. A large share of our work is inheriting systems other teams built: stabilizing them, adding test coverage, untangling legacy integrations, and modernizing incrementally rather than risking a single big rewrite. We start with a technical audit so you know the real state of the codebase and the data before committing to a roadmap.

Java Spring Boot, Node.js, Python, React, Next.js, and TypeScript on the application side; PostgreSQL and Redis for data; AWS and Google Cloud for infrastructure, with Docker and Kubernetes where the scale calls for it; and AI integration with Claude, GPT, and Gemini where it earns its place. For data heavy products we also build on Xano as an Enterprise tier partner, which can cut backend cost and timeline meaningfully.

We are an AI native team: roughly 80% of our production code is AI generated and engineer reviewed, verified by our internal team. That changes the economics of enterprise software, delivering faster on the same quality bar while senior engineers focus on architecture, security, and review. We also build AI features into the applications themselves, from RAG search over company data to document automation and autonomous agents, with the guardrails and evaluation infrastructure production AI needs.