AI Development / AI SaaS development
AI SaaS products, built from first release to scale.
We build AI SaaS products for other businesses, end to end: the AI features, the product around them, and the platform they run on.
Astra Short on time? Ask about this page in plain words.
Sound familiar? Four problems we are usually called in to fix.
The AI demo works, the product does not
It impressed in a pitch, but it is not multi-tenant, billable or secure.
- Multi-tenant architecture
- Billing and payments
- Roles, admin and audit
Model costs grow faster than revenue
Every new customer makes the margin worse.
- Model cost control
- Model choice per feature
- Usage visible per tenant
Answers drift and customers notice
Quality changes with every model update and nobody sees it first.
- Grounded answers
- Evals before release
- Monitoring in production
The scope keeps growing before launch
Everything is in version one, so version one never ships.
- Product scoping first
- A first release users want
- A roadmap after launch
An AI product that holds up at scale.
Let’s talkAI SaaS development services. Everything a subscription AI product needs, not just the model.
Product scoping
Shape the first release around the one job customers will pay for.
AI feature development
Assistants, extraction, agents and search built into the core of the product.
Multi-tenant architecture
One platform serving many customers, with each customer's data kept separate.
Grounded answers
AI that answers from each customer's own documents and records, with sources shown.
Model cost control
Model choice per task, caching and usage limits, so AI costs stay predictable per customer.
Billing & payments
Payments, plans and usage-based pricing through providers such as Stripe.
Admin, roles & audit
Workspaces, permissions and audit trails that business buyers expect.
Evals & monitoring
AI output tested before each release and tracked in production.
Scale & support
Performance, reliability and new features as the customer base grows.
AI products we have built for clients. Full-stack AI platforms, built end to end. Each result comes from a delivered engagement.
AI SaaS for fit-out & construction
An AI SaaS platform from drawing to quotation, for English and Chinese-speaking teams.
5-stage connected workflow AI · InsuranceFull-stack AI platform for insurance
Fragmented policy, claims and correspondence data connected into one structured workflow.
~2.5x faster processing AI · Agent platformsAI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effort AI · LegalResearch & review assistant
Citation-grounded answers, each one traced back to its source document.
~50% faster review timeHave an AI product idea you want in customers' hands?
From idea to a product people pay for. Four stages, with a working version reviewed at each one.
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Discovery
Map the product, the systems and data involved, and what good looks like.
Discovery notes
- The customer and the job to be done
- The data the AI depends on
- What the first release must prove
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Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution design
- Product and platform architecture
- Model choice and cost plan
- Release milestones
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Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Working product at each milestone
- AI evals on real cases
- Early user feedback
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Ship & support
Roll out to production with monitoring and a defined support window.
Production rollout
- Production launch
- Usage and quality monitoring
- Roadmap for the next release
Why Avinya for AI SaaS. One team for the AI, the product and the platform.
We have done it before
We have built AI SaaS products for other businesses, from first release to production.
Full-stack delivery
Data pipelines, models, product and infrastructure from one team.
A lovable first release
We scope the smallest product customers will choose, not just tolerate.
Built to scale
Tenant isolation, roles and cost controls designed in from the start.
Results from the AI platforms we built. Each number links to the engagement it came from.
Engagement models. Pick the shape that fits where you are.
Dedicated team
A dedicated team that works as an extension of yours, from first release to scale.
Project-based
A defined scope, milestones and delivery date for a specific product or workflow.
Consulting & advisory
Architecture and readiness advice before you commit to a build.
What our clients say.
AI SaaS for construction
“Avinya Labs delivered exactly what we needed. When a critical issue came up, the team stayed up until midnight so we weren't left hanging.”
Guides on this topic. Plain answers to the questions buyers ask first.
How to Build an MVP: Steps, Timeline and Cost Drivers
Seven steps from one customer and one job to a first release.
Read the guide → GuideWhy a Minimum Lovable Product Beats an MVP
What an MLP adds to an MVP, and when to choose it.
Read the guide → GuideHow to Build a RAG System for Your Company Knowledge Base
Architecture, components and stack choices, and when RAG is the wrong tool.
Read the guide →AI SaaS development FAQs.
Have you built AI SaaS products before?
Yes. We have built AI SaaS products for other businesses, covering the AI features, the product and the platform. One example is a fit-out and construction platform built as SaaS, with Stripe payments, OpenRouter for model orchestration and Langfuse for LLM observability.
What is AI SaaS development?
Building a software-as-a-service product where AI is part of the core value: the AI features, the product experience around them, and the multi-tenant platform, billing and admin a subscription business needs.
Can you add AI to our existing SaaS product?
Yes. We can build AI features into your current product and platform, or build a new product end to end.
How do you keep model costs predictable?
We choose models per task, route requests through a model orchestration layer such as OpenRouter, cache where answers repeat, trace model calls with tools such as Langfuse, and meter usage per customer, so AI costs map to your pricing.
How is each customer's data kept separate?
Tenant isolation is part of the architecture: each customer's data, search index and AI context stay within their own workspace.
Should we start with an MVP or an MLP?
We usually recommend a minimum lovable product: a narrow first release that does one job well enough for customers to choose it. Read our MLP vs MVP guide.
Which AI models do you use?
We evaluate models against your real workflow and choose per task. The architecture is designed so models can change later without rebuilding the product.
How do you approach AI SaaS development securely?
We treat model calls like any other external service: validated inputs, limited permissions and logged outputs. For AI SaaS development we check the web layer against the OWASP Top 10 and the AI layer against the NIST AI Risk Management Framework.
Free tool
How ready is your business for AI?
Twenty questions, about four minutes. You get a score across five areas and what to fix first.