Applied AI

There’s a difference between a product that uses AI and a product that’s better because of it. The difference is judgement — knowing when AI adds real value to a user’s experience, and when it’s just a feature for the pitch deck.

We don’t lead with AI. We start with the product problem. If AI is the right tool — for intelligent search, smarter workflows, faster document processing, a more responsive interface — we integrate it cleanly, efficiently, and in a way users barely notice because it just works. If it isn’t the right tool, we’ll tell you that too.

Every AI feature we build is evaluated against one question: does this make the product meaningfully better for the person using it?

How we approach it

UX first, always

AI should reduce friction, not create it. We design the interaction before we build the integration.

Relevant, not everywhere

We apply AI where the data, the use case, and the user behaviour justify it — not as a default layer on everything.

Practical integration, not prototypes

We build AI features into production products — with proper error handling, fallback states, cost management, and performance budgets.

What we build

AI features embedded in your product

Intelligent search, document summarisation, smart suggestions, content generation, and recommendation logic woven into the product experience.

Conversational interfaces and AI assistants

In-product chat, customer support automation, and conversational UI — designed around real user journeys, not generic chatbot templates.

LLM workflows and AI agents

Multi-step pipelines where the model reasons, retrieves information, calls tools, and takes actions. Built with OpenAI Assistants API, Anthropic tool use, and function calling for reliable, controllable behaviour

RAG pipelines

Retrieval-Augmented Generation — connecting LLMs to your own data so users get answers grounded in your content, not hallucinated from general training data.

Long-context document processing

Anthropic Claude’s extended context window makes it particularly well-suited for document analysis, contract review, and large report summarisation.

Predictive and data-driven features

Where your product has sufficient data, we build features that surface patterns, flag anomalies, or support decisions — integrated into the product interface.

Custom AI-powered software

For teams whose problems don’t fit any existing AI product — we design and build from scratch. This sits alongside our Custom Business Tools practice.

Platforms we work with

OpenAI

Azure AI

Gemini

Claude

CloudFlare

Meta AI (Llama)

Amazon Bedrock

ElevenLabs

Hugging Face

Cohere

Industries

  • Real estate

  • Enterprise SaaS

  • Healthcare

  • Logistics

  • Internal operations

Have questions?
We’ve got answers.

OpenAI provides direct access to GPT-4o. Azure OpenAI is OpenAI’s models on Microsoft Azure — preferred for enterprise data residency and compliance. Google Gemini is strong for multimodal applications involving text, image, and structured data. Anthropic Claude excels at long-context document processing, nuanced reasoning, and safety-critical outputs. We recommend the right model for your requirements — and in some cases use more than one.
RAG (Retrieval-Augmented Generation) connects an LLM to a specific knowledge base — your documentation, product data, or client records — so it answers questions using your content rather than general training data. You need it when accuracy and specificity matter.
We design for cost from the start — choosing the right model tier, implementing caching for repeated queries, setting token limits, and monitoring usage. AI API costs can scale unexpectedly without proper architecture.
Yes. We integrate AI APIs into React Native mobile apps — handling streaming responses, latency management, and offline fallback states appropriate for mobile contexts.
AI-first means building around AI regardless of whether it serves the user. Applied AI means evaluating each feature against the product problem — and only integrating AI where it removes real friction or enables something genuinely useful. We think the second approach builds better products.

Let’s talk about where AI fits in your product.

Talk to Us About AI Integration
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