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AI Integration

Add AI to the software you already run: apps, sites, CRMs, support desks and internal tools.

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The problem

You do not need a new platform to benefit from AI. You need the assistant inside the help desk, the summary inside the CRM, the answer inside the docs your customers already read. Bolting a chatbot onto the homepage rarely moves a number; wiring a model into the workflow where the question is asked does.

What we build

  • Retrieval-backed assistants that answer from your own content: sites, Notion, PDFs, Drive
  • Model features inside existing products: summaries, classification, extraction, drafting
  • Integrations with Shopify, WordPress, Webflow, Wix, Squarespace, Slack and plain HTTP APIs

LLM integration, Retrieval-augmented generation, Chatbots, Notion, Slack and Shopify connectors, WordPress, Webflow, Wix and Squarespace, HTTP API integration

AI chatbots, E-commerce (Shopify, WooCommerce), WordPress

How it works

  1. 01

    Pick the moment

    Where in the product or the process is a question asked or text produced? That is where the model goes first.

  2. 02

    Connect the knowledge

    Ingest the content the answer needs, chunk and index it, and set the rules for what the assistant may and may not say.

  3. 03

    Ship behind a flag

    The feature launches to a subset of users with logging of every prompt, answer and rating.

  4. 04

    Tune and expand

    Weekly review of failed answers, prompt and retrieval fixes, then the next moment in the product.

Work that proves it

  • Userdesk home page: AI assistants that collect leads, beside a chat preview and content sources

    Userdesk

    No-code AI support and lead assistant trained on your site, Notion, PDFs and Drive

    Designed and developed for the client: retrieval pipeline, the embeddable assistant and the lead hand-off flow.

  • HelpKit home page: building a help center out of Notion pages, above a customer quote

    HelpKit

    Notion to help center with AI support

    Designed and developed for the client: the Notion-to-help-center renderer, search and the AI answer layer.

  • ChatBuddy home page: a dark hero with phone mockups and App Store and Google Play buttons

    ChatBuddy

    Multi-model AI mobile app with in-app credits

    Designed and developed for the client: the mobile app, multi-model chat and in-app credit purchases.

What you get

  • Integration design and data-flow diagram

  • Retrieval pipeline over your content

  • Model feature with evaluation set

  • Platform connector (CMS, CRM or help desk)

  • Prompt and cost report

  • Handover documentation

Questions

What's included in an AI integration engagement?
An integration design with a data-flow diagram, a retrieval pipeline over your own content, and the model feature itself with an evaluation set so quality is measured rather than guessed. Where the feature lives inside a platform you already run — a CMS, a CRM, a help desk — the connector is part of the work. You also get a prompt and cost report and the handover documentation.
How long does a first AI feature take to ship?
A first feature inside a product that already exists usually ships in two to six weeks. The variables are how much content the assistant has to read, how clean and permissioned that content is, and whether the platform offers a real API or only a plugin surface. The second and third feature are faster, because the pipeline and the evaluation set already exist.
What do you need from us?
API access to the product or platform, the content the assistant should answer from, and someone who can say what it must never say. For the evaluation set we need about thirty real questions with the answers you would accept, which is usually an hour with whoever handles support today.
Who owns the code and the data?
You do. The integration lives in your repository, the model keys and the index sit in your accounts, and your content is never used to train anyone else's model. If you later want a different provider, or want to bring the feature in-house, the pipeline and the evaluation set travel with you.
Which model do you use, and can we change it later?
We pick per feature, not per company: the cheapest model that passes your evaluation set, with a stronger one behind a fallback for the hard cases. Calls go through one internal interface, so switching provider is a configuration change and a re-run of the evaluation set rather than a rewrite. Models and prices move every few months, and the design assumes that.

Not sure where to start?

Describe the problem in a few lines. You get a straight answer on what we would build and how long it takes.

Book a 5-minute growth call
Book a 5-minute growth call