Software engineering service
SaaS products built with AI in the architecture, not bolted on.
We help founders and product teams build multi-tenant SaaS platforms — with billing, permissions, onboarding, and AI features designed together from the first sprint.
The problem
SaaS foundations are hard to retrofit.
Tenancy, permissions, billing, and data models decided in the first months shape everything that follows. Adding AI later to a product that was not designed for it is slow and risky.
What we typically hear
- Tenant isolation was not designed in from the start.
- AI features need data the product does not capture.
- Usage-based AI costs are not reflected in pricing.
- Onboarding and admin tooling lag behind the core product.
- Technical debt slows every release.
What we build
What we build
Multi-tenant core
Organisations, users, roles, and data isolation designed correctly from day one.
Billing and plans
Subscriptions, usage metering, and entitlements, including AI usage.
AI features
Copilots, generation, search, and automations inside the product.
Onboarding
Sign-up, setup flows, and in-product guidance that reduce time to value.
Admin and support tools
Internal consoles for your team to manage customers safely.
Product analytics
Event tracking that shows how customers use the product.
Typical use cases
Where this work pays off
New AI-native product
01A platform where AI is central to the value proposition.
Adding AI to an existing SaaS
02Introduce AI features with proper tenancy, cost tracking, and controls.
Vertical SaaS
03Industry-specific software with workflows and terminology built in.
Platform rebuild
04Re-architect a product that has outgrown its first version.
Architecture
SaaS platform layers
- 1
Tenants
Organisations, users, and roles.
- 2
Product
Core workflows and data.
- 3
AI layer
Models, retrieval, and agents per tenant.
- 4
Billing
Plans, metering, and limits.
- 5Human
Ops console
Your team's oversight tools.
Technology considerations
Decisions we make with you
Technology choices follow from the workflow, data, and constraints — not the other way around.
Tenancy model
Row-level security, schema separation, or dedicated resources depending on customer requirements.
AI cost attribution
Model usage tracked per tenant and feature so pricing reflects cost.
Scalability
Queues, caching, and background jobs for workloads that grow with customers.
Release safety
Feature flags, staged rollouts, and automated tests.
Delivery
How the engagement runs
01
Product discovery
Customer, problem, and scope for the first release.
02
Architecture
Tenancy, data model, and AI design.
03
Build in increments
Ship to real users early and often.
04
Launch
Production readiness, monitoring, and support processes.
05
Scale
Performance, features, and team growth.
Security & governance
Control is designed in, not added later.
Every system we deliver includes the controls below as a baseline. Specific compliance requirements are scoped with your team.
- Tenant data isolation enforced at the database layer
- Per-tenant AI usage limits
- Customer data excluded from model training by default
- Security logging and access reviews
- Documented data processing for customer due diligence
FAQ
Frequently asked questions
01Can you work alongside our in-house team?
Yes. We can own a product area, provide additional engineering capacity, or lead the architecture while your team builds.
02How do you handle AI costs in a SaaS product?
We track model usage per tenant and feature, apply limits, and help you reflect AI costs in pricing so margins stay healthy as usage grows.
03Do you help after launch?
Yes. Ongoing engineering engagements cover new features, performance, reliability, and AI improvements.
Related reading
Go deeper on this topic
Related services
Often delivered together
Product
MVP & Product Development
MVP development by Neuralex Labs: product discovery, design, and engineering for AI-powered MVPs that test your idea with real users and can scale.
AI
AI Copilots
Neuralex Labs builds AI copilots for internal teams and SaaS products: context-aware assistants grounded in your data, with permissions and citations built in.
Engineering
Ongoing AI Engineering
Ongoing AI engineering from Neuralex Labs: monitoring, evaluation, model updates, and continuous improvement for production AI systems and software.
Next step
Bring us the process your team is tired of doing manually.
A strategy call is a working conversation about one workflow: what it involves today, what could be automated, and what a first build would look like.
What happens next
- 1Tell us about one workflow
- 2We review it before replying
- 3A working session, then a first-build proposal