Two meanings of "AI app development"
People mean two different things by it, and we do both.
AI inside your product. Features where a language model does real work for your users or your team: answering questions from your own documents, reading and extracting data from PDFs and forms, drafting replies, classifying and routing requests, or an assistant that can take actions in your system.
AI in how the product is built. We are an AI-native studio: AI writes scaffolding, tests and boilerplate, and senior engineers review and sign off every change. That is why our builds are fast without being fragile, and it is how we build everything on this site, AI features or not.
What we build
- Assistants grounded in your data. Chat or search over your documents, products or knowledge base, with answers that cite their source and say "I don't know" rather than inventing one.
- Document and data workflows. Extract fields from invoices, contracts, IDs or forms, check them, and hand exceptions to a person. The boring back-office work where AI pays for itself first.
- MCP servers and agent integrations. The Model Context Protocol lets assistants such as Claude, ChatGPT and Cursor call your system directly. Wipe Board runs a remote MCP server: add one URL to an assistant and ask which servers wipe tonight in Europe, and it answers from live data with links back to the site.
- APIs that agents can use. Clean JSON endpoints and an OpenAPI spec, so your product works as a tool for custom GPTs and agents, not just as a website.
- Measurement pipelines on top of LLMs. BoostSearch calls the Claude and OpenAI APIs with web search every month to record which businesses real assistants recommend, and runs a daily watch on the queries they issue.
How we keep AI features reliable
A demo that works once is easy. A feature that works for the thousandth user is engineering.
- Evaluations before launch. We build a test set of real questions and inputs with known good answers, and measure every prompt or model change against it.
- Grounding and citations. Answers come from your data, with sources shown, so users and your team can check them.
- A human where it matters. Anything irreversible (a payment, a message to a customer, a decision about a person) goes to a human for approval.
- Privacy by default. Your code and data are never used to train third-party models. Sensitive data stays in your infrastructure, and we send a model only what the task needs.
- Cost and latency budgets. We pick the smallest model that passes the evaluations, cache what can be cached, and show you the running cost per request.
The same discipline applies to our own process, and we have written up the human-review gates we never skip.
Being the source AI assistants quote
More and more buyers ask an assistant before they ask Google. An app that exposes clean HTML, structured data, an llms.txt, an API and an MCP server is one assistants can read, cite and use. We build that in from the start. If you want your existing business to show up in AI answers, that is what our consultancy BoostSearch does.
Frequently asked questions
How much does it cost to add AI to an app?
It depends on scope, and running costs depend on usage. We price the build after a free 15-minute consult and a short discovery sprint, and we give you an estimate of the monthly model cost per user before you commit.
Which AI models do you use?
Claude and OpenAI models through their APIs, chosen per task by evaluation rather than habit. We design so you can switch models later without a rewrite.
Is our data safe with AI?
Yes, if it is built properly. We use API terms under which your data is not used for training, keep sensitive data in your own infrastructure, send models only what a task needs, and log what the AI did so it can be audited.
What is an MCP server and do we need one?
The Model Context Protocol is an open standard that lets AI assistants call tools and read data from other systems. An MCP server makes your product usable from inside Claude, ChatGPT or Cursor. If your customers or staff already work in an assistant, it is one of the cheapest ways to meet them there.
Can you add AI features to our existing app?
Yes. We start with a short audit to find the one workflow where AI saves the most time, build and evaluate that first, and expand from what works.