AI SaaS product development
Customer workflows, responsive interfaces, APIs, databases, admin controls and deployment for a complete subscription product.
Global AI SaaS product engineering
AI SaaS Development Services Built for Production
I help founders and product teams design, build and improve AI SaaS products where the AI feature, application, data, billing and operations work as one reliable system.
With 11+ years in full-stack engineering, I work directly across React, Next.js, Angular, Node.js, Supabase, PostgreSQL, Stripe and production AI APIs. You speak with the engineer responsible for the architecture and implementation—not an account layer passing requirements between teams.
AI SaaS development services
The product around the model matters
A useful AI feature still needs secure data access, a clear interface, tenant isolation, billing, failure handling and monitoring. I build those connected parts together so the result can serve real customers rather than remain a disconnected demo.
AI integrations and agents
Assistants, retrieval, document processing, summaries and tool-using workflows with clear scope, validation and human approval.
Multi-tenant architecture
Organisation boundaries, roles, row-level data policies, onboarding and audit trails designed for B2B SaaS from the start.
Subscriptions and usage billing
Stripe plans, seats, webhooks, entitlements, trials and usage controls that stay consistent with product access.
Secure APIs and integrations
Node.js services, webhooks, authentication, third-party systems and background workflows with retries and observability.
Existing product improvement
Add AI to an established product, modernise Angular or React code, fix performance and strengthen production reliability.
Evidence from real product work
Production systems, not invented capability claims
My approach comes from building and maintaining products where permissions, billing, integrations and failure states affect real users. These examples show the range of that work.
AI SaaS platform
CultureAI
A multi-tenant product combining anonymous employee feedback, AI-assisted insights, role-based access, authentication and Stripe billing.
View live product
Commerce automation
Shopify Social Image Automation
A Node.js application using Shopify GraphQL, webhooks, metafields and image processing to publish branded social previews.
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Multi-role SaaS
BWL League
A Next.js and Supabase sports platform with administrator, coach and team workflows protected by role-aware database access.
View live platformI also work as a senior engineer on an established automotive SaaS product, contributing to production Angular interfaces, complex workflows, debugging and performance. I separate that team contribution from products I built directly, so the scope of my experience stays clear.
Production-ready architecture
Four systems must stay aligned
The model call is only one part of an AI SaaS product. I treat customer identity, AI behaviour, commercial access and operations as connected production concerns.
01 · Data boundary
Tenant identity and authorization
The server and database enforce which organisation owns each request. Prompt text never decides access.
02 · AI boundary
Grounding, tools and approval
AI receives the minimum context and narrow tools required, with evaluation and approval around risky actions.
03 · Commercial boundary
Billing and entitlements
Subscription state, plan limits and AI usage controls remain consistent through verified, idempotent events.
04 · Operational boundary
Monitoring and safe failure
Trace IDs, cost tracking, rate limits, fallbacks and reversible workflows make behaviour understandable in production.
Direct delivery process
Move from product risk to working software
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01
Define the paid problem
Clarify the customer, workflow, evidence of value and the smallest useful first release.
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02
Design the boundaries
Plan tenancy, permissions, AI context, tools, billing and failure handling before implementation.
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03
Ship reviewable milestones
Test working increments against real workflows instead of hiding progress behind status reports.
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04
Launch and observe
Monitor usage, cost and failures, then improve the product from production evidence.
Common questions
Hiring an AI SaaS developer
What do AI SaaS development services include?
AI SaaS development can include product architecture, multi-tenant data design, authentication, AI features, secure tool and data access, subscription billing, APIs, dashboards, deployment and production monitoring. The exact scope should follow the product problem rather than a generic feature checklist.
Can you add AI to an existing SaaS product?
Yes. I can add focused AI capabilities to an existing product, including assistants, document processing, search, summarisation and workflow automation. The work starts by understanding the current application, data and security boundaries so the new feature fits the product instead of becoming a fragile side system.
How do you keep an AI SaaS product secure and cost-controlled?
I use server-side authorization, tenant-aware data access, narrow tools, approval gates for sensitive actions, output validation, usage limits, model routing, monitoring and clear fallbacks. The exact controls depend on what the AI can read, create or change.
Can you build both an AI SaaS MVP and the production platform?
Yes. The first release should prove the core customer workflow without creating avoidable architecture debt. I can deliver that initial product and continue through billing, integrations, hardening and production support as real usage provides better evidence.
How does a direct engineering engagement work?
We define the outcome and risks, agree on clear milestones, review working software regularly and adjust from evidence. You communicate directly with the engineer responsible for the architecture and implementation.
Need an AI SaaS product that can survive production?
Share the customer problem, current product and target outcome. I’ll help turn them into a practical architecture and delivery plan.