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Software

SaaS Product Development: AI-Native Platforms and MVPs

We build SaaS products with AI in the core rather than bolted on: architecture, multi-tenant backend, billing, integrations, and the assistant, automation, or intelligence that makes the product different. From an MVP that proves the market to a platform that scales, on a stack your team can own.

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Who This Is For

Built for founders and companies productizing what they know

  • Founders with domain expertise who need an engineering partner to build the product, not just a prototype.
  • Companies turning an internal system into a product for their industry.
  • SaaS teams adding AI features that must be reliable, secure, and priced sustainably.
  • Investors and operators who need an MVP that proves demand before a larger build.

What We Build

Everything a SaaS product needs

Product and architecture

Scope, data model, multi-tenant architecture, security model, and an AI feature plan with cost and latency budgets.

MVP in increments

The smallest product that proves the value, built and released in weeks, with real users and measurement from the first release.

Backend and data

Node.js or Python services, PostgreSQL, Redis, queues, background jobs, and APIs designed for tenancy, scale, and auditability.

Frontend

React and Next.js applications with accessible, fast interfaces, onboarding flows, and admin tools.

AI features

Assistants, retrieval, automation, and analytics built into the product on hosted or self-hosted models, with evaluation and guardrails.

Billing, auth, and operations

Subscriptions and usage billing, SSO and roles, monitoring, logging, CI pipelines, and infrastructure on AWS, Kubernetes, and Cloudflare.

Products with AI in the core

The difference between a SaaS product with AI added and one built AI-native is in the data model and the workflow. AI-native products are designed around what the model does for the user: answering from their data, automating their core job, or making decisions visible. That design decides retention, and it must be made at the architecture stage, with evaluation and cost budgets, not after launch.

How we build

Discovery defines users, the job, the differentiating capability, and the first release. Architecture covers tenancy, security, data, and the AI plan. The MVP ships in increments to real users, measured from the first release, and each increment is shaped by what users do. Billing, authentication, monitoring, and pipelines are built in so the product is operable from day one.

A stack you can own

Node.js or Python backends, React and Next.js frontends, PostgreSQL and Redis, containers on Kubernetes or AWS, Cloudflare at the edge, CI on GitLab or GitHub, and AI on hosted or self-hosted models with LangChain, LangGraph, and vector retrieval. Documentation and runbooks let your engineers take over at any point.

What you measure

Time to first release, activation and retention, the metric the AI capability moves for users, cost per tenant and per request, and reliability, reviewed every release.

How It Works

How we build a SaaS product

  1. Discovery and scope

    Users, jobs to be done, the differentiating capability, and the first release defined with success metrics.

  2. Architecture and design

    Data model, tenancy, security, AI plan, and interface design prototyped and reviewed.

  3. Build and release

    Increments every few weeks with real users, measurement, and feedback shaping the next release.

  4. Scale and hand over

    Performance, security, and cost tuned as usage grows; your team onboarded to own the codebase or DPI operates it.

Stack & Integrations

SaaS stack

Node.js, TypeScript, Python React, Next.js PostgreSQL, Redis, queues OpenAI, Anthropic Claude, AWS Bedrock, self-hosted models LangChain, LangGraph, pgvector, Qdrant Docker, Kubernetes, AWS, Cloudflare GitLab and GitHub CI/CD Payment, auth, and email providers

Industries

SaaS products by industry

Real Estate

Leasing, management, and investor platforms.

Engagement Models

Engagement models

Product discovery

Two to three weeks: scope, architecture, AI plan, design prototype, and a build roadmap with releases and budgets.

MVP build

Fixed-scope delivery of the first release with real users, measurement, and a plan for the next increments.

Product team

Ongoing development and operations as a dedicated team, or transition to your own engineers.

FAQ

Questions about SaaS development

How long does an MVP take?

A focused MVP with one differentiating capability typically reaches real users within weeks after discovery. Scope, integrations, and AI features drive the timeline; discovery sets it.

What makes a product AI-native rather than AI-added?

The data model, workflows, and interface are designed around what the AI does: retrieval over the customer's data, automation of the core job, or intelligence in the decisions, with evaluation and cost budgets from the start.

Who owns the code?

You do, from the first commit. Documentation, pipelines, and runbooks are part of the deliverable so your team can take over.

Can you handle security and compliance requirements?

Yes. Tenancy isolation, SSO, roles, audit logs, encryption, and private model hosting where needed; HIPAA-aligned designs with a BAA for healthcare products.

How do you price it?

Discovery and MVP builds are fixed-scope quotes after a short call; ongoing development is a monthly team arrangement. Ranges depend on scope and are given in writing.

Related

Next Step

Bring one workflow. Leave with a production plan.

Tell us where calls, tickets, documents, or approvals pile up. We map the workflow, size the impact, and propose a deployment you can measure.

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