LLM Integration: Add AI where it actually earns its place
AI features are the parts of your software that use a language model to do something a person used to: read a document and pull out the figures, draft a first reply, sort an enquiry, answer a question from your own records. We build these into the systems you already run, so the AI does real work inside a real workflow rather than sitting in a separate chat window nobody opens.
Most AI bolted onto business software is decoration: a chatbot in the corner that gets used twice and forgotten. The useful version is narrow and specific. It takes one repetitive, judgement-light task that quietly eats your team's time and does it reliably, with a person checking the output where a mistake would matter. That is what we are interested in, not AI for the brochure.
What we build
The features that pay off are the dull, repetitive ones, the jobs that are easy for a person but slow at volume. These are the four shapes most of that work takes.
Document extraction
Pull structured data out of invoices, contracts, forms, and emails, so it lands in your system instead of being keyed in by hand.
Drafting and summarising
First-draft replies, summaries of long threads or documents, and descriptions built from a few facts. A person approves; the AI does the typing.
Classification and routing
Sort incoming enquiries, tickets, or records and send each one to the right place, without someone triaging the inbox by hand.
Search over your own data
Ask a question in plain English and get an answer grounded in your documents and records, with sources, rather than a confident guess.
Built to be trusted
AI that makes things up is worse than no AI. We build features that show their working: answers cite the source they came from, extractions can be checked against the original, and a person stays in the loop wherever a wrong answer would cost something. We choose the task carefully, because the difference between a useful AI feature and an expensive liability is whether a mistake gets caught before it matters.
Grounded, not guessed. We connect the model to your actual data and cite what it used, so you can check the answer rather than trust it blindly.
How it runs
We prove the feature works on your real cases before building it into anything. That way you see the accuracy before you commit the budget.
Find the task
The repetitive, time-eating job where AI would genuinely help, and where a mistake is recoverable rather than catastrophic.
Prove it on real examples
We test the approach against your real documents and cases first, so you see how often it is right before a line of it ships.
Build it into the workflow
The feature lives inside the system your team already uses, with a person checking output at the points that matter.
Measure and tune
We track how often it is correct and how much time it saves, and adjust. AI features need tending, not just shipping and forgetting.
Who it is for
AI is worth adding when there is a specific, costly task behind it, not because it is in the headlines.
Where it fits
AI features usually sit inside a system we build or already support, so if the system is not there yet, that comes first. They are close cousins of business automation: automation handles the rules-based work, AI handles the judgement-light work that rules alone cannot. Often the two run side by side in the same system.
Talk to us about AI in your software
Tell us the repetitive task that is eating your team's time. We will give you an honest read on whether AI would help and how well. The first conversation is free, takes about thirty minutes, and comes with no obligation. Read more about what working with us looks like, or get in touch directly.
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