AI Product Engineering & AI SaaS

An AI prototype (ChatGPT wrapper, Jupyter notebook, internal demo) works in ideal conditions with one user and clean inputs. A production AI product handles real users, real documents, edge cases, errors, and scale, with authentication, data isolation, monitoring, cost controls, and a UI that non-technical users can operate. The gap between prototype and production is where most AI projects stall. We bridge that gap.

We connect AI capabilities to your existing product via APIs and custom integration layers. Common additions include AI-powered search (semantic over your existing data), document assistants (RAG over user-uploaded files), workflow automation (AI agents triggered by user actions), and intelligent analytics (LLM-generated insights from your metrics). We work within your existing tech stack and deployment pipeline, no full rebuild required. Most AI feature additions take 4–8 weeks.

A focused AI SaaS MVP, core AI workflow, authentication, billing, admin dashboard, and cloud deployment, typically takes 8–12 weeks. A full-featured platform with multi-tenancy, advanced analytics, API access, and enterprise security takes 14–20 weeks. We use two-week agile sprints with demos so you see working software throughout, not just at the end.

We build AI SaaS products using Next.js (frontend), FastAPI + Python (backend and AI logic), Supabase (database + authentication), LangChain and OpenAI/Claude (AI layer), Stripe (billing), and AWS or Vercel (deployment). This stack delivers fast performance, real-time capabilities, built-in auth, and easy scalability, and it's what we use internally for our own tools.

Yes, multi-tenancy is a core part of our AI SaaS architecture. We implement data isolation at the database level (Row Level Security in Supabase/PostgreSQL), role-based access control, subdomain or path-based tenant routing, tenant-specific AI configurations (different models or knowledge bases per customer), and usage metering for billing. This ensures your platform can scale to thousands of customers without data leakage.

Yes. We deliver every AI product with full deployment on AWS, Vercel, or GCP, including CI/CD pipelines, environment configuration, SSL, and monitoring dashboards. We also offer ongoing maintenance and support to handle model updates, performance tuning, and new feature development. Post-launch, we set up alerts so issues are caught before users notice them.