Frequently Asked Questions

Bella Tech is an enterprise technology consulting, engineering and talent solutions company. We work across Data & AI, Cloud & Platform Engineering, Cybersecurity & Identity, and Software & Enterprise Architecture, either delivering an outcome end to end or providing specialized technical talent into a program you run.

We're based in Indianapolis, Indiana, but we work with clients across the US and globally. Our team operates both in-office and remotely, and we're set up for seamless collaboration regardless of location.

We are set up to work in regulated and data-sensitive environments, including healthcare, financial services, legal, and enterprise SaaS. Our approach is industry-agnostic: what changes between industries is the constraint set, not the engineering discipline.

It starts with a conversation. We learn about your idea, your goals, and your constraints. From there, we scope the project, define milestones, and present a proposal with timeline and investment. Once aligned, we kick off with a discovery sprint to nail down architecture and design before building.

An MVP typically takes 8-14 weeks depending on complexity. A full-featured product can take 3-6 months. We work in 2-week sprints with demos at the end of each one, so you see progress continuously — not just at the end.

Both. We're comfortable inheriting existing projects, refactoring legacy code, or building greenfield. We'll assess what you have, recommend the best path forward, and be honest about whether a rebuild or incremental improvement makes more sense.

We build custom AI models, integrate large language models (LLMs) into products, develop NLP and computer vision systems, create predictive analytics engines, and design intelligent automation workflows. We work with OpenAI, Anthropic, open-source models, and custom-trained models depending on the use case.

We choose the right tools for each project. Our most common stack includes React/Next.js and TypeScript on the frontend, Node.js or Python on the backend, PostgreSQL for databases, and AWS for cloud infrastructure. For AI/ML, we use Python with PyTorch, LangChain, and vector databases like Pinecone or pgvector.

Absolutely. We specialize in embedding AI into existing products — adding intelligent search, automated classification, content generation, predictive analytics, or conversational interfaces without rebuilding your application from scratch.

Project costs depend on scope, complexity, and timeline. A focused MVP typically starts around $25,000-$50,000. Larger, enterprise-grade platforms range from $75,000-$250,000+. We provide detailed estimates after a discovery session and are transparent about costs at every stage.

Yes. We offer maintenance and support packages that include bug fixes, performance monitoring, security updates, and feature iterations. Support scope and coverage hours are agreed per engagement, because a system that keeps evolving needs someone accountable for it after the first release.

We offer both models. Fixed-price works well for clearly defined projects with firm scope. Time-and-materials is better for complex, evolving products where flexibility matters. We'll recommend the model that makes the most sense for your situation.