AI Integrations

Add powerful AI features to your existing website or app, from content generation and smart search to personalised recommendations and summarization, built on the latest LLMs.

See key features

Free 30-minute call · No obligation

Proven track record
35+
Projects Delivered
7+
Industries Served
Explore Our Work
What's inside

Key features

Everything AI Integrations includes, built to move the needle for your business.

  • LLM-powered features (GPT, Claude)
  • AI content generation
  • Semantic & smart search
  • Personalised recommendations
  • Document & data summarization
  • Image generation & analysis
  • RAG over your own data
  • Secure API integrations

What you get

  • AI feature integration
  • API & model setup
  • Data pipeline
  • Documentation
  • 6-month support

Why work with us

  • Latest LLM Models
  • Built Into Your Product
  • Privacy-First Setup
Timeline

A typical project

  1. 1
    3 days

    Discovery

    Feature scoping

  2. 2
    1 week

    Design

    Architecture & model choice

  3. 3
    2-3 weeks

    Build

    Integration & testing

  4. 4
    3 days

    Launch

    Deploy & monitor

Which AI features are worth building into a product?

The AI features that get used are narrow and specific. Semantic search across your own content, so people find things by meaning rather than exact keyword. Drafting and summarisation for work someone would otherwise write from scratch. Classification and routing to get an item to the right queue. Recommendations based on real usage rather than guesswork. Each solves a defined problem a user already has.

The features that go unused are the general ones. A chat assistant bolted onto a dashboard demonstrates well and then sits idle, because users did not arrive with a question phrased for it. If a feature cannot be described in one sentence that names the user's task, it is usually not worth building yet.

Two practical constraints shape every integration. First, cost is usage-based and easy to underestimate, so we estimate per-request cost during scoping and build in caching and rate limits — you should know your cost per thousand users before launch, not after a traffic spike. Second, data handling: we use providers and configurations where your content is not retained for training, and confirm that in writing. For genuinely sensitive data we will discuss keeping inference inside your own infrastructure.

Anything customer-facing gets a confidence threshold, a human fallback and a visible way to report a bad answer. Features where being wrong is expensive stay internal, assisting your team rather than replacing their judgement.

Worth thinking about before you commission AI features

  • Which specific user task would this make faster?
  • How sensitive is the data the feature needs to read?
  • What is an acceptable cost per user per month?
  • What happens, visibly, when the model gets it wrong?
Who it's for

Who AI Integrations is for

The industries where we have actually shipped this work.

Interested in AI Integrations?

Book a free 30-minute call and let's discuss how we can help your business grow.

AI Integrations questions

Semantic search across your own content, drafting and summarisation, classification and routing, and recommendations based on real usage. The useful ones are narrow and specific. A general assistant bolted onto a product tends to impress in demos and go unused.

Not on the configurations we deploy. We use providers and settings where your content is not retained for training, and we confirm that in writing during scoping. For sensitive data we will also discuss keeping processing inside your own infrastructure.

Usage-based, and easy to underestimate. We estimate per-request cost during scoping and build in caching and limits so a traffic spike cannot produce a surprise bill. You should know the cost per thousand users before launch.

Design for it. Anything customer-facing gets a confidence threshold, a human fallback, and a visible way to flag a bad answer. Features where being wrong is expensive stay internal, assisting your team rather than replacing their judgement.

A fixed build fee plus usage-based model costs, which we estimate per thousand users during scoping. Caching and rate limits are part of the build specifically to keep that predictable. You should know the per-user cost before launch, not after a spike.

Model provider costs are yours and billed to your own account, so you retain control and visibility. We do not train custom models on your data as standard; that is a much larger project and rarely the right answer for the features most products need.
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