AI integration services for SaaS products

AI features work best when they start from a job your users already do, not from a model. Our AI integration services for SaaS products add assistants, retrieval over your own documents and workflow automation to existing platforms, then measure whether each feature is worth its cost. We run OpenAI and Azure AI models in production, and our free tool Roast My App is coming soon. We will tell you when a plain rule beats a model.

Who this is for

• SaaS teams with a working product and a backlog of requests to add AI, who need someone to say which ones are worth it.
• Product leaders whose customers keep asking questions the documentation already answers, and who want an assistant that cites the right page.
• Operations-heavy companies where staff copy information between tools, and a model could draft the first version for a person to approve.
• Founders who want a feature behind a flag within a sprint, with a way to turn it off.

Problems we solve

Finding where AI adds value in an existing product

Three patterns pay off most often inside a SaaS product.

An assistant inside the product that can read the user's own data and take a small set of actions. The value comes from the context and the actions, not the chat window.

Retrieval over a customer's own documents, such as contracts, policies, tickets and reports, with answers that cite their source. This is the feature most B2B customers ask for first, and the one where tenancy matters most: the index for one customer must never answer another customer's question.

Workflow automation inside tools teams already use: a model classifies a ticket, extracts fields from a PDF, drafts a reply or a summary, and a person approves it. These features are unglamorous and they pay back fastest.

Evaluation before launch, not after

A model that is right nine times out of ten is a liability if nobody knows which answer is the tenth. Before anything ships we build an evaluation set from real examples, define what a good answer looks like, and run every prompt and model change against it.

Guardrails that fit the risk

Output validation with schemas, allow-lists for tool calls, strict limits on what the model can see per tenant, and a human in the loop wherever the action is hard to undo. Every prompt and response is logged so an incident can be traced.

Cost control

Token spend grows with usage, not with your plan prices. We meter calls per tenant and per feature, cache repeated answers, pick the smallest model that passes the evaluation set, and set budgets with alerts before the invoice surprises anyone. Usage-based billing with Stripe can pass part of the cost to the customers who use the feature most.

Privacy and data handling

Customer data goes to a model provider only when the feature needs it, under a data processing agreement, with training on your data disabled. Retrieval indexes are partitioned per tenant. Retention is explicit and short. If a customer's contract says their data stays in a given region or cloud, we design around that from the start.

Choosing between OpenAI and Azure AI Foundry

Both run the same families of models. The OpenAI API is the fastest way to prototype and has the broadest range of models. Azure AI Foundry runs those models inside your own Azure subscription, with regional deployment, private networking and the same compliance boundary as the rest of your platform. If your SaaS already runs on Azure, or your customers ask about data residency, Foundry is usually the right production home. We keep the provider behind one interface so you can switch and avoid being locked in.

Shipping behind feature flags

An AI feature is never switched on for everyone at once. It goes live for a handful of tenants who agreed to try it, and it can be turned off in seconds without a deploy.

How we approach it

1. Discover. A free call on what your users struggle with today. We come back within 48 hours with a written scope that names one or two features worth building first and the ones to skip.
2. Design. We agree the data the model may see, the actions it may take, the evaluation set and the budget. This is where privacy and tenancy are settled.
3. Build. Two-week sprints with weekly demos. The feature ships behind a flag to a small group of tenants, and evaluation results are part of the demo.
4. Scale. Roll out tenant by tenant, watch quality and cost, tune prompts and models from real usage, and hand over the evaluation suite with the code.

What we build with

We use the OpenAI API or Azure AI Foundry for models, depending on where your platform lives and what your customers require. Both sit behind one TypeScript interface.

The rest is the same production stack as our SaaS work: Node.js and TypeScript on the server, PostgreSQL for application data and for vector search while the scale allows it, and Redis with BullMQ for the queue that runs embeddings, indexing and long generations outside the request. Feature flags gate every AI feature. Vitest runs the evaluation set in CI. Structured logging keeps every prompt, response, latency and token count queryable per tenant.

We avoid frameworks that hide the prompt. The prompt is product code, and your team should be able to read it.

A project our founder has led

Our founder leads development of a tenant screening and property management SaaS for the Canadian rental market: hostname-based tenancy, four role-based portals, Stripe usage-based billing and BullMQ workers deployed to Azure. That is the kind of multi-tenant platform our AI work is designed for. [TODO: founder to confirm which AI features are live in the platform and which OpenAI or Azure AI Foundry deployments they use.] Our free tool Roast My App is coming soon: paste an App Store or Play Store link and the AI reads public reviews, then returns a roast, a health score out of 100 and the five fixes that would lift the rating most.

What you get

• An NDA signed before you share any details.
• All code, prompts, evaluation sets and IP in your repositories. You own it completely.
• A weekly demo of working software with evaluation results, not a slide of possibilities.
• A written scope within 48 hours of the first call.
• A reply to every message within 24 hours.
• A team that starts in 1 to 2 weeks.
• A senior lead who writes the code and reads the prompts.

Related services: SaaS product engineering when the platform itself needs work, and technical leadership if you want an honest second opinion on an AI roadmap. Every engagement is a custom quote; see ways to work with us.

Ready to find the one feature worth building? Start a project brief. Four quick questions, then a free 30-minute call.