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AI features for SaaS

AI SaaS integration: LLM, RAG and vector search features

We add language model features to SaaS products, such as document question answering, summaries and classification, with per-customer data separation and cost controls.

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  • LLM and RAG features
  • Vector search on PostgreSQL
  • Tenant-safe data handling

What AI features we build into SaaS products

AI integration means adding model-powered features to a product your customers already use, not building a model from scratch. Typical features include:

  • Question answering over a customer's own documents or records.
  • Summaries, drafting help and rewriting inside your editor.
  • Classification and tagging of tickets, emails or candidates.
  • Data extraction from uploaded files such as invoices or contracts.
  • Natural-language search across your product.

We connect to hosted language model providers through their APIs and keep the integration behind your own service so you can switch providers later.

How RAG and vector search work

Retrieval-augmented generation (RAG) lets a model answer from your data. Documents are split into passages and converted into embeddings, which are stored in a vector index. When a user asks a question, the system retrieves the most relevant passages and passes them to the model, which writes an answer based on them.

For the index we usually use PostgreSQL with pgvector, which keeps data near the rest of the product, or a managed vector database when scale or features call for it. AI services are often written in Python with FastAPI, next to a Next.js and PostgreSQL application. See the tech stack page.

Keeping customer data separate and costs controlled

In a multi-tenant product, retrieval must never return one customer's documents to another, so the vector index follows the same isolation rules as the rest of the data, as described under multi-tenant architecture. We also build in the controls that production AI needs:

  • Usage limits and spend caps per tenant or plan.
  • Logging of prompts and responses, with sensitive fields handled carefully.
  • Citations back to source passages, so users can check answers.
  • Fallbacks and retries when a provider is slow or down.
  • A small evaluation set to compare prompt and model changes.

AI features are usually part of a larger build, such as the production B2B SaaS range of $18,000 to $35,000 over 8 to 12 weeks, or added to a live product by a dedicated engineer at around $5,500 per month. A small proof of concept can be included in a lean first release if the scope is narrow. These ranges are typical, model usage fees are paid to the provider separately, and a written quote follows scoping. See pricing.

Limits of AI features

Language models can produce wrong or invented answers, and no setup removes that risk. We reduce it with retrieval, citations, narrow prompts and human review where it matters, but we cannot promise accuracy. Provider pricing, terms and model behavior can change. If your data is sensitive or regulated, we check provider contracts and data-handling settings with you before sending anything to an outside model. The decision to send that data stays with you.

  • SaaS only
  • Multi-tenant design
  • Stripe billing
  • Client owns the code

How it works

  1. Message us on WhatsApp or Telegram

    Describe the product, the customers and the features that matter most.

  2. We scope and quote

    You get the plan, the architecture outline and a written price range.

  3. We build and ship

    Working builds each sprint, then a tested production launch.

Questions about saas dev agency in United States

Do you train our own AI models?

Usually not. Most SaaS features work well by calling a hosted model and giving it the right context through retrieval. Training or fine-tuning a model is a different project with its own data, cost and maintenance. If your case needs it, we will say so during scoping.

Will our customers' data be used to train the provider's models?

That depends on the provider and the plan you choose. Many offer API settings or business terms that keep your data out of training. We read the current terms with you and configure the integration to match, but you are responsible for choosing a provider whose terms suit your customers.

How do you stop the AI from giving wrong answers?

We cannot stop it completely. We lower the risk by retrieving from your own content, showing sources, instructing the model to say when it does not know, and testing with a set of real questions. For high-stakes tasks, we recommend a human review step before anything is acted on.

Can AI features be added to an existing SaaS?

Yes. We add them as a separate service or module so the rest of the app stays stable. We first review how your data is stored and separated per customer. If the base product has structural problems, refactoring and scaling may come first.

What does it cost to run AI features after launch?

You pay the model provider and any vector database hosting based on usage. We estimate this with you during scoping and build in spend caps, per-plan limits and usage reporting so costs cannot surprise you. Actual costs depend on how much your customers use the features.

Ready to scope your SaaS?

Message us on WhatsApp or Telegram with the product idea and the customers it is for.

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