SaaS MVP
A focused first release that proves the product with real customers.
Engineering Layer
SaaS Application Development Company
From MVP to enterprise-ready: authentication, billing, multi-tenancy, and cloud infrastructure — with AI capabilities designed into the product architecture, not patched in later.
We have built SaaS products across education, e-commerce, property, and analytics, and we bring that pattern library to yours.
- Multi-tenancy
- Stripe
- Auth
- Next.js
- PostgreSQL
- AWS
- Kubernetes
CAPABILITIES
What we build
SaaS architecture
Multi-tenancy, data isolation, and a path to scale.
Authentication
SSO, roles, teams, and permissions.
Subscription billing
Plans, trials, metering, and invoicing.
Analytics
Product and revenue analytics built in.
AI SaaS
Metered AI features with cost controls per tenant.
OUR APPROACH
Our AI-native process
Seven steps, one team. Intelligence is scoped at the start and evaluated at the end.
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01
Discovery & AI Opportunity Mapping
We start every engagement by mapping where intelligence belongs in your product: which workflows an agent can own, where an LLM adds real value, and where plain software is the right answer. You get an honest AI scope — including what not to build.
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02
UI/UX Design
Research, flows, wireframes, and high-fidelity interfaces, validated with users before development begins. For AI features, we design the states that matter: streaming, uncertainty, and human-in-the-loop control.
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03
Intelligence Engineering
Model selection, prompt and agent architecture, RAG pipeline design, and evaluation harnesses — built with LangChain, LangGraph, CrewAI, and the vector store that fits your data.
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04
Web Development
Frontend and backend engineering on React, Next.js, Node, and Python, delivered in short sprints with working software at the end of each one.
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05
Mobile Development
Native and cross-platform apps for iOS and Android, built against the same APIs and design system as the web product.
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06
Cloud & Deployment
Infrastructure as code, CI/CD, and monitored releases on AWS, Azure, or GCP — including GPU/inference infrastructure and model-cost monitoring where AI workloads demand it.
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07
Testing & AI Evals
Automated test suites cover every release. AI features get their own evaluation suites — regression tests for prompts, hallucination checks, and output-quality scoring — so intelligence ships with the same rigor as code.
FAQ
SaaS Application Development — common questions
How much does SaaS development cost?
It depends on the feature set, user roles, integrations, billing complexity, and compliance needs. We scope an MVP with a fixed first phase after discovery.
How long does SaaS development take?
A focused MVP typically takes 10 to 16 weeks. Enterprise features such as SSO, audit logs, and advanced billing add time.
Can you add AI to an existing SaaS product?
Yes. We add AI features to live products with per-tenant data isolation, usage metering, and cost monitoring.
Do you handle hosting and maintenance?
Yes. We set up cloud infrastructure, CI/CD, and monitoring, and offer ongoing maintenance.
Ready to build AI-native?
Tell us what you're building — we'll show you where AI belongs in it.