AI SaaS Development for SaaS
The AI feature on your roadmap has slipped three quarters running.
It is not a prioritisation problem. Building retrieval that is accurate, agents that are safe, and inference costs that survive contact with your pricing model requires specialist time your team does not have spare. We build that layer alongside your roadmap in your codebase.
- Tenant isolation
- MultiTenant isolation
- Usage billing
- MeteredUsage billing
- Grade infra
- ProdGrade infra
Typical stack
- Next.js
- NestJS
- Postgres
- Stripe
- Vercel
What this handles for SaaS companies
- Build and ship the AI feature your roadmap keeps deferring
- Instrument per-tenant inference cost so pricing is based on real economics
- Add evaluation suites to CI so AI quality regressions fail the build
- Take a working prototype to production-grade with isolation, limits, and observability
The deferred AI feature shipped, instrumented, and maintainable by your own team.
The saas problem underneath it
SaaS teams rarely lack AI ideas. They lack the specialist capacity to build them properly alongside an existing roadmap — retrieval that is actually accurate, agents that are safe to let loose in a customer account, and inference costs that do not quietly destroy gross margin.
- The AI feature on the roadmap keeps slipping behind committed work
- A prototype exists but nobody is confident enough to put it in front of customers
- Inference cost per account is unmodelled, so pricing is guesswork
- Support volume grows with the customer base and headcount follows
Inside an AI SaaS platform
We treat model cost and latency as product constraints from the first architectural decision, not as something to optimise after launch. Tenancy, metering, and evaluation get designed in at the start because retrofitting any of the three means a rewrite.
- Multi-tenant architecture with enforced data isolation per customer
- Subscription billing, plan limits, and usage-based metering via Stripe
- Authentication, roles, team management, and invitations
- The AI layer — retrieval, agents, or generation — engineered for cost and latency
- Admin tooling for support, impersonation, and plan management
- Deployment, observability, and CI so shipping is routine rather than an event
What ships with the engagement
- Product and technical architecture agreed before build starts
- Working application deployed to staging and production
- Billing integration with plans, trials, upgrades, and dunning
- Cost model showing unit economics per active customer
- Handover documentation and a runbook for your team or ours
AI SaaS Development for SaaS — questions
- Do you work in our repo or build separately?
- In your repo, your review process, your deployment pipeline. The output is code your team owns and can maintain — a separately built black box creates a dependency neither of us wants.
- How do we price a feature with variable inference cost?
- Meter first, price second. We instrument usage per tenant and per feature so you can see the real distribution, then choose between seat pricing with caps, credits, or usage tiers on evidence rather than intuition.
- Can you take over an existing codebase?
- Yes. We start with an audit covering architecture, cost exposure, and the parts that will break under load, then agree what is worth keeping before touching anything.
- How do you keep model costs under control?
- Model routing so cheap requests do not hit expensive models, aggressive caching of repeated work, prompt and context budgets, and per-tenant usage caps. Costs are tracked per customer so you can see unit economics rather than one alarming monthly invoice.
AI SaaS Development in other sectors
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