AI in Your Product
Add AI to the product you already have
We integrate large language models into existing apps and websites: search that understands what users mean, summaries, drafting and answers over your users' own data. Features people use every day — built with clear costs, quick responses and privacy in mind.
What is LLM integration?
LLM integration means connecting a large language model to an existing product so it can read, write and answer inside the app, using the app's own data. The model is called from your backend, given only the information it needs for each request, and its output is shown to users or used in a workflow. Done well, users don't notice "the AI" — they notice that search finds the right thing and long text turns into a two-line summary.
Which AI features do users actually use?
Plenty of AI features get launched, admired once and ignored. The ones that last save users a step they take every day:
| Usually worth building | Usually a gimmick |
|---|---|
| Search that understands meaning, not just keywords | A generic chatbot bolted onto the homepage |
| Summaries of long records, threads or documents | "Write it with AI" buttons on fields nobody writes in |
| First drafts of replies, descriptions or reports | AI-generated content with no review step |
| Questions answered from the user's own data | Answers from the open internet inside a business tool |
| Automatic tagging and sorting of incoming items | Features added only to say the product "has AI" |
What do we build?
AI features in Bubble.io apps
Semantic search, summaries, document Q&A and in-app copilots, added to the Bubble app you already run.
See the service →AI search and assistants for Webflow sites
Visitors ask questions in plain language and get answers from your site's own content, with links to the right pages.
Webflow development →Custom RAG systems
Question answering over large or specialised document sets, with citations and access control, as a service your product calls.
See the service →Private and self-hosted LLMs
Open-source models hosted on your infrastructure for data that must not go to a commercial API.
See the service →Automated QA testing with browser agents
AI agents that click through your app's key flows like a user would and report what broke after each release.
See the service →Who is it for?
- SaaS products where users search, filter and read a lot of records.
- Marketplaces that need better search and listing descriptions.
- Internal tools where staff summarise cases, tickets or reports by hand.
- Content-heavy websites where visitors can't find answers that are already published.
- B2B platforms whose customers upload documents and ask questions about them.
How does an AI feature work inside an app?
The user does something they already do — searches, opens a record, asks a question. Your backend collects only the data that user is allowed to see, sends it to the model with clear instructions, checks the response and the cost limits, and shows the result in your interface. Keys and prompts stay on the server, never in the browser.
What about cost, speed and privacy?
Token costs
Language models charge per amount of text processed. Costs stay predictable with the right model for each task, limits per user, caching of repeated results and sending only the data a request needs. We estimate running costs before the build, from your expected usage.
Latency
Model responses take from a fraction of a second to several seconds. We stream long answers so they appear as they're written, run slow tasks in the background, and use smaller, faster models where quality allows.
Data privacy
We send the minimum data needed, keep keys server-side, document which provider processes what, and offer private or self-hosted models when data must stay on your infrastructure.
How do we test AI features before users see them?
AI output varies from one request to the next, so "it worked when I tried it" isn't enough. Before launch we:
- Build a test set of real examples from your product, with what a good result looks like for each.
- Run every version of the prompt or model against that set and compare the results side by side.
- Check the edge cases: empty records, very long inputs, other languages in user content and attempts to make the feature misbehave.
- Measure speed and cost per request, so there are no surprises when usage grows.
- Release behind a feature flag to a small group first, and watch real usage before rolling out.
The same test set is reused whenever the model or prompt changes later, so improvements don't quietly break what already worked.
What do we build with?
We choose models per feature, not per vendor, and we aren't partners or resellers of any of them.
How do we work?
- Discovery call. We look at your product and how people use it, and pick the one or two features worth building first.
- Audit and fixed-scope proposal. We review your data and architecture, then send a written scope, timeline, price and running-cost estimate.
- Build and test with human review. We build behind a feature flag and test on real data before users see it.
- Launch and monthly care. We release to a group of users, watch usage and costs, and improve from there.
Payments are milestone-based: you pay after you approve each stage. Every launch includes one month of free bug fixing.
How much does it cost to add AI to an app?
Adding a first AI feature typically starts at $2,000, with optional monthly care from $250 per month. Model usage is paid to the provider and grows with your users. What drives the price:
- The feature: a summary button is simpler than search across all your data.
- Your data: clean, structured records are faster to work with than scattered files.
- Permissions: features that must respect who can see what need more design.
- Privacy requirements: self-hosted models cost more to set up and run.
Is adding AI right for your product?
Good fit when…
- Users search, read or write a lot inside your product
- You have data the AI can work with
- You can name the step the feature removes
- You're ready to watch usage and adjust
Not a fit when…
- The goal is to say the product "has AI"
- The product has few users or little data yet
- Every answer must be exactly right with no review
- Running costs would exceed what users pay
Frequently asked questions
What AI features should I add to my app first?
Start with the feature that removes a step users take every day, such as search that understands meaning, summaries of long records or first drafts of routine text. These get used. Generic chatbots and AI buttons on rarely used fields usually don't, so they're better left for later or skipped.
How much does it cost to run AI features in an app?
Running costs depend on how much text the model processes per request and how often users trigger it. They stay predictable when you choose the right model for each task, set limits per user, cache repeated results and send only the data needed. We estimate them before the build from your expected usage.
Is my users' data safe when I add AI features?
It can be, with the right design: the backend sends only the data each request needs, keys stay on the server, and you know which provider processes what. When data must not leave your infrastructure, a privately hosted open-source model can be used instead of a commercial API.
Do I need to rebuild my app to add AI?
Usually not. AI features are added to the existing app through its backend, an API or a workflow tool such as n8n. We review your architecture first and tell you if anything needs to change before the feature can work well.
How long does it take to add an AI feature?
A first AI feature typically takes 3–6 weeks from discovery to launch, including testing on real data. Features that search across large data sets or must respect detailed permissions take longer.
Want AI your users will actually use?
Show us your product. We'll suggest the one feature worth building first, and what it would cost to build and run.
Book a Free Call →