AI integration
We add AI features to existing software and workflows: reading documents, summarising text, searching your content, and answering routine questions.
Sound familiar?
When businesses come to us for this
AI is on the roadmap, but nobody knows where
There is pressure to add AI, but no clear job for it to do.
Hours go into reading and retyping documents
Invoices, forms, and reports are read by hand and typed into another system.
People cannot find answers you already have
The information is in your documents and tickets, but search does not surface it.
What we build
AI integration: what that covers
LLM features
Summaries, drafting, classification, and translation inside the app you already have.
AI assistants
Assistants that answer from your own content, show their sources, and hand over to a person when unsure.
Document processing and OCR
Reading scanned files, invoices, and forms, and turning them into structured data.
AI agents and automation
Multi-step tasks such as triaging requests or updating records, with approval steps where needed.
Semantic search
Search across your documents that finds relevant results even when the exact words do not match.
Need something else?
If your project is not on this list, describe it and we will tell you whether it is a good fit.
Ask us
How we approach it
Our approach, step by step
- 01
Find the job worth automating
We look for tasks your staff repeat many times a day, where an AI draft or answer can be checked quickly.
- 02
Prototype with your data
A quick test on real examples shows what works before anything is built properly.
- 03
Add guardrails
Human review, source citations, logging, and limits on what the AI is allowed to do.
- 04
Track cost and quality
We monitor usage, cost per task, and error rates after launch, and adjust prompts or models when needed.
Technologies
What we build it with
Our usual stack for this work. We choose per project, and can work with what you already have.
- OpenAI
- GPT models for text, vision, and structured output
- Anthropic
- Claude models for long documents and careful reasoning
- LangChain
- Retrieval and multi-step workflows
- Python
- Data pipelines and model integration
- FastAPI
- Lightweight APIs for AI services
In practice
Work we have done in this area
Our productSaaS · Document Tools
iLoveToolkit: PDF, image, and Word tools in the browser
A web app for everyday document jobs: merge, split, convert, edit, and protect PDFs, plus AI tools, with nothing to install.
Read the case studyFurther reading
Guides on this topic
AI & ML
GPT-6 Astra vs Claude Fable 5.1 vs Gemini 3.8 Flash: Which AI Model Fits Your Product?
AI & ML
A New AI Model Every Week: How to Choose the Right One for Your Product
AI & ML
Integrating LLMs into Web Applications: A Practical Guide
AI & ML
AI Features That Add Real Value to SaaS Products (and Ones That Do Not)
AI & ML
How AI Is Changing Product Development
FAQ
Common questions
It depends on the provider and the settings. We use business API plans whose terms exclude training on your data, send only what each task needs, and can keep sensitive steps on your own servers.
Each request has a usage cost from the model provider. We measure it per task during the prototype, so you know the running cost before launch.
Usually not, and it should not try. It works best answering common questions and drafting replies, so people can focus on the cases that need judgement.
Planning a project like this?
Share a few details about your business and what you need. We will suggest a sensible first step and a rough scope.
