SaaS Product Development

We build SaaS products architected to survive contact with real customers — multi-tenant from day one, billing and subscription logic that doesn't need a rewrite at your next pricing tier, and an MVP scoped to what actually proves your hypothesis.

3

Legacy tools replaced by one platform (Northbeam Logistics)

98%

Client retention

24hr

Average response time

Why Avenva

What sets working with us apart

You own the code

No proprietary lock-in — the repository, infrastructure, and IP are yours from day one.

No vendor lock-in

Standard, well-documented stacks, so your next team (or ours) can extend it without a rewrite.

One team, start to finish

The same engineers scope, build, and support your project — no hand-offs between sales and delivery.

Direct access to engineers

You talk to the people writing the code, not an account manager relaying messages.

Fixed-scope, transparent pricing

Clear estimates before work starts, and no surprise change orders for work that was always in scope.

Support after launch

Launch day isn't the finish line — we stay on for monitoring, fixes, and iteration.

What's included

What we deliver

MVP scoping & architecture

We map the two or three features that actually test your hypothesis, and architect the data model so version two extends it instead of replacing it.

Multi-tenant data architecture

Customer data isolated cleanly from the first schema, so adding your hundredth tenant is a config change, not an incident.

Billing & subscription logic

Plans, metering, upgrades, and dunning wired into Stripe or your billing provider of choice, built to handle a pricing change without a migration.

Public site + logged-in app, one codebase

A fast, SEO-friendly marketing site and a complex, interactive product sharing one deployment — so a pricing page going viral never slows down paying customers.

Is this you?

Where this actually pays off

You're validating a hypothesis, not building the final version

An MVP scoped to what actually proves the idea, architected so the parts that work can be extended instead of thrown away once you learn something.

Your pricing model doesn't fit your original data model

Adding a team plan, usage-based billing, or a new tier shouldn't require a schema migration — if it does today, that's a sign the multi-tenant architecture needs a second look.

You're moving off a no-code MVP that proved the idea

Bubble, Retool, or a spreadsheet-backed prototype got you real customers, but can't handle the tenant count or edge cases you're seeing now.

Your marketing site and product feel like two different companies

When the public site and the logged-in app are built and maintained separately, copy updates and product updates start competing for the same engineering time.

Our approach

How we run this engagement

01

Discovery & MVP scoping

Define the smallest version that proves the core hypothesis.

02

Architecture

Design the multi-tenant data model and billing logic to scale without a rewrite.

03

Build

Ship a working product early, with continuous preview deploys.

04

Launch & iterate

Onboard real customers, then extend based on what they actually use.

Tech we use

Built on a proven, modern stack

Next.js

One codebase for the public marketing site and the logged-in product, deployed together.

Node.js

A fast, well-supported runtime for the API layer your product runs on.

PostgreSQL

Row-level security and schema design that scale cleanly as tenants multiply.

TypeScript

Types that catch a broken billing edge case before it reaches a paying customer.

Stripe

Subscription billing, metering, and dunning without building payments infrastructure yourself.

AWS

Infrastructure that scales per-tenant without provisioning for guessed-at growth.

Proof

Work we've shipped like this

“Avenva rebuilt our platform in a fraction of the time we quoted internally, and the code quality made our own engineers' lives easier for years after.”

SC

Sarah Chen

VP of Engineering, Northbeam Logistics

Questions

Things people ask before starting

Can't find what you're looking for? Reach out and we'll answer directly.

We start from the hypothesis you're actually trying to prove, not a full feature list — the smallest version that gives you a real signal, architected so it extends cleanly once you have that signal instead of getting thrown away.