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AI Enabled SaaS Development
AI products thatprotect their margins
We build subscription software with AI features at its core, keeping each customer's data separate, metering model usage per account and controlling cost so the product stays profitable as it grows.
In an AI product every request has a price, so margins depend on engineering choices made long before the first invoice is sent.
A SaaS product with AI built in
- 01Tenant isolation
- 02Usage metering
- 03Cost controls
AI enabled SaaS is multi-tenant software in which language models do part of the work for every customer. Beyond the usual accounts, billing and administration, it needs data isolation that extends into prompts and search indexes, metering of model usage per tenant, limits that protect your margins and evaluation that keeps quality steady across releases.
It suits founders building a new AI product and established SaaS companies adding AI features to a platform customers already pay for. The two differ. A new product can be designed around these needs, while an existing one must fit them into current plans and architecture. We have a view on both and begin from your pricing model.
Talk to us about your projectCapabilities, End to End
What It Looks Like When It Ships
From Kickoff to Launch
We start from your pricing and estimate the model cost of each feature per customer, which shows early whether a plan is profitable and where limits or cheaper models are needed.
Tenancy, data isolation, the AI pipeline, metering and billing are designed together, since retrofitting any one of them into a running product is far harder than planning it.
The first release contains one valuable AI feature working end to end for real tenants, with isolation, usage tracking and evaluation in place, instead of many features resting on weak foundations.
Early customers use the product while we watch quality, usage and cost per account, adjust limits and model choices from real data and add features in order of demand.
Built for Teams Like Yours
What buyers ask before they start
If yours is not here, ask us directly. The first conversation is free and useful whether or not we work together.
Book a callIsolation is enforced at every layer the AI touches. Database rows, files, search indexes and caches are keyed to the tenant, and the pipeline can only retrieve within the account of the signed-in user. Prompts are assembled per request and never mix sources. We write automated tests that try to cross the boundary, since this is the failure an AI product cannot afford.
Start from what a request costs you. Common models are a usage allowance inside each plan with paid top-ups, credits that customers spend, or a higher tier that includes the AI features. Flat unlimited pricing is risky while model costs vary with use. We build metering first so that pricing can be based on observed behavior and changed without rewriting the product.
The product should handle it by design. Each account has limits per period, with a warning as it approaches them and a clear choice when it reaches them, such as upgrading or waiting. Background jobs are queued fairly so one tenant cannot slow the others. You see the heaviest users in a dashboard and can adjust their plan before the cost becomes a problem.
Yes. We review the current architecture, tenancy model and billing, then design the AI layer to fit them. Typical first steps are a metering service, a tenant-scoped retrieval index and one feature released to a subset of customers. Where the existing platform lacks proper isolation, that is addressed first, because AI features magnify any weakness in how data is separated.
Preferably none exclusively. We put a thin layer between the product and the providers, so that models can be swapped per feature as prices and quality change. Evaluation makes such a switch a measured decision. Enterprise customers may also ask for a specific provider or region, and an architecture that already supports several can say yes without a rebuild.
