Custom Software
Custom SaaS and Software Development That Ships
We are Nerai Labs, a software and AI studio that builds custom SaaS products and internal tools end to end. From a blank repo to a deployed app handling real traffic, we own architecture, code, infrastructure, and the AI features that make the product worth using.
What custom SaaS and software development means here
We build web applications tailored to how your business actually runs, not a template forced to fit. That covers multi-tenant SaaS platforms, customer portals, billing and subscription logic, admin dashboards, internal operations tools, and the APIs that connect them. Our founder built systems at Pomelo, MercadoLibre, Mercado Pago, and Scale AI, and our team has shipped pipelines handling 50K+ daily executions. You get product-grade engineering, not a prototype that breaks under load.
What we deliver in a custom software build
A working product in your hands, deployed and documented. That means a typed codebase (TypeScript, Python), a real database schema with migrations, authentication and role-based access, payment integration when you need it, CI/CD, observability, and tests on the paths that matter. We also wire in AI where it earns its place: RAG search over your data, AI agents, document processing, or workflow automation, all with cost and latency tradeoffs spelled out before we write code.
How we work: scoped, local-first, dry-run
We start with a short discovery to define the smallest version that delivers value, then ship in two-week increments you can review and use. We test against local environments before touching production, run bulk or data operations as dry-runs first, and gate destructive changes behind explicit flags. You see progress weekly, own the repository from day one, and never get locked into us to keep the lights on.
Where AI fits into the product
Most teams want AI in the product but not AI for its own sake. We build RAG chatbots grounded in your documents, agents that take actions across your tools, and automation that removes manual steps. We pick models on cost and accuracy for your case, add prompt caching and evals so quality is measurable, and keep a non-AI fallback so the feature degrades gracefully instead of failing.
How it works
- 01
Scope
We map the problem, define the smallest valuable release, and agree on architecture, timeline, and budget before any code is written.
- 02
Build
We ship in two-week increments with a working deploy at each step, so you review real software, not slide decks.
- 03
Harden
We add tests, observability, and CI/CD, run data operations as dry-runs first, and tune cost and latency on the paths that matter.
- 04
Hand off
You own the repo and infrastructure with documentation and a clean migration path. We stay on for support only if you want it.
What you get
- A deployed, production-grade SaaS or internal tool, not a throwaway prototype
- A typed codebase with migrations, tests, and CI/CD you fully own
- Authentication, role-based access, and billing logic wired in correctly
- AI features (RAG, agents, automation) with measured cost, latency, and accuracy
- Observability and dry-run safeguards so changes ship without breaking production
- Two-week increments you can review and use, with weekly visibility into progress
Questions
Do you build from scratch or work on existing codebases?
Both. We start greenfield SaaS products from an empty repo, and we also extend or fix existing applications. For existing code we begin with a short audit of the architecture, data model, and risk areas, then propose a scoped plan before changing anything.
What technologies do you use?
We default to TypeScript and React on the frontend, Python or Node on the backend, and Postgres (often Supabase) for data. We choose infrastructure based on your scale and team rather than forcing one stack. For AI features we use the major model providers and add caching and evals so quality stays measurable.
How long does a custom SaaS build take?
A focused first release is usually 6 to 12 weeks depending on scope. We ship in two-week increments, so you have working software early instead of waiting months for a single launch. Discovery sharpens the timeline before any commitment.
Can you add AI to a product we already have?
Yes. We add RAG search, AI agents, document processing, or workflow automation to existing apps. We scope each feature against cost and accuracy, build a non-AI fallback, and instrument it so you can see whether it actually performs in production.
Do we own the code and infrastructure?
You own everything from day one: the repository, the database, and the cloud accounts. We document the system and provide a clean handoff so your team or another vendor can take over. Ongoing support is optional, never a dependency we engineer in.
